{ "version": "PE-2026.10-v4", "date": "2026-10-10", "dimensions": [ { "id": "ai-core", "name": "AI Core", "ar": "الذكاء الاصطناعي في صميم العمل", "question": "Where has AI changed a material operating workflow?" }, { "id": "workforce", "name": "Augmented Workforce", "ar": "القوى العاملة المعززة", "question": "How do people learn, use and supervise AI in their work?" }, { "id": "ecosystem", "name": "Ecosystem", "ar": "المنظومة المتكاملة", "question": "How do partners and shared capabilities support internal AI operations?" }, { "id": "interfaces", "name": "Interfaces & Automation", "ar": "الواجهات والأتمتة", "question": "How does AI change the way customers and employees interact with services?" }, { "id": "lineage", "name": "Decision Lineage", "ar": "تتبّع القرارات", "question": "Can consequential AI decisions be traced to their inputs, versions and authority?" }, { "id": "experimentation", "name": "Experimentation", "ar": "التجريب", "question": "How are AI use cases tested, compared, stopped and improved?" }, { "id": "autonomy", "name": "Autonomy & Operating Model", "ar": "الاستقلالية ونموذج التشغيل", "question": "What may AI execute, and how do people supervise, reverse or stop it?" }, { "id": "knowledge", "name": "Knowledge", "ar": "المعرفة", "question": "How does AI make organizational knowledge available and reusable?" }, { "id": "governance", "name": "Governance", "ar": "الحوكمة", "question": "Who owns AI decisions, risks and lifecycle controls?" } ], "companies": [ { "id": "emirates-group", "name": "Emirates Group", "country": "UAE", "sector": "Aviation & Logistics", "scope": "Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes", "scopeLimitations": "IT gateway and software-governance evidence does not establish workforce-wide or dnata-wide deployment.", "cluster": null, "evidence": [ { "id": "LOG-001", "companyId": "emirates-group", "sourceId": "L-S01", "dimension": "ai-core", "claim": "Emirates Airport Services reports a launched AI system for aircraft-turnaround analysis and resource optimization.", "url": "https://www.emirates.com/media-centre/forsatek-2025-offers-platform-for-high-impact-scalable-innovation/", "originalSourceUrl": "https://www.emirates.com/media-centre/forsatek-2025-offers-platform-for-high-impact-scalable-innovation/", "sourceTitle": "ForsaTEK 2025 offers platform for high-impact scalable innovation", "corroboratingSourceIds": [], "publisher": "Emirates Group", "sourceType": "corporate-disclosure", "publicationDate": "2025-06-23", "accessedDate": "2026-10-10", "locator": "AI-powered solutions paragraph; fully launched Airport Services initiative", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes. Company assertion; no independent operational measurement. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Explicit launched internal airport-operations practice; evaluation and deployment coverage are undisclosed.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "0b255dabfbf5ff004f042aef66c536f3a043224766bb46c80bd01be596732d8c", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-002", "companyId": "emirates-group", "sourceId": "L-S01", "dimension": "experimentation", "claim": "Emirates Engineering is trialling AI and computer vision to identify engine parts.", "url": "https://www.emirates.com/media-centre/forsatek-2025-offers-platform-for-high-impact-scalable-innovation/", "originalSourceUrl": "https://www.emirates.com/media-centre/forsatek-2025-offers-platform-for-high-impact-scalable-innovation/", "sourceTitle": "ForsaTEK 2025 offers platform for high-impact scalable innovation", "corroboratingSourceIds": [], "publisher": "Emirates Group", "sourceType": "corporate-disclosure", "publicationDate": "2025-06-23", "accessedDate": "2026-10-10", "locator": "AI-powered solutions paragraph; engineering trials", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes. Innovation showcase includes projects at different stages. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named internal AI trial; no production deployment established.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "689844c25f58aa7f77cdb7b18d6f4d240169c230100f974e73790b5768a61296", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-003", "companyId": "emirates-group", "sourceId": "L-S02", "dimension": "workforce", "claim": "The Group announced enterprise ChatGPT access and AI-literacy programmes for employees.", "url": "https://www.emirates.com/media-centre/emirates-group-collaborates-with-openai-to-accelerate-ai-adoption-and-innovation/", "originalSourceUrl": "https://www.emirates.com/media-centre/emirates-group-collaborates-with-openai-to-accelerate-ai-adoption-and-innovation/", "sourceTitle": "Emirates Group collaborates with OpenAI to accelerate AI adoption and innovation", "corroboratingSourceIds": [], "publisher": "Emirates Group", "sourceType": "corporate-disclosure", "publicationDate": "2025-11-21", "accessedDate": "2026-10-10", "locator": "Enterprise ChatGPT, training and internal champion network paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes. Later implementation is assessed separately in LOG-004; no additive points. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific internal rollout commitment; announcement uses forward-looking implementation language.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "50f4f5c85791b6612543d774addd5df2ca0201ea6341cdf31142dd13698e8808", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-004", "companyId": "emirates-group", "sourceId": "L-S03", "dimension": "workforce", "claim": "More than 2,000 Group IT staff had joined its AI gateway by August 2026.", "url": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "originalSourceUrl": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "sourceTitle": "Catalysing the next wave of innovation: ForsaTEK 2026 concludes successfully", "corroboratingSourceIds": [], "publisher": "Emirates Group", "sourceType": "corporate-disclosure", "publicationDate": "2026-09-28", "accessedDate": "2026-10-10", "locator": "ARRIVE and AI gateway paragraphs near article end", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes. IT subset only; total eligible staff, usage frequency and productivity effects undisclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Primary Emirates page paragraphs115–116 explicitly reports IT onboarding count. Retain level3 for bounded workforce coverage; do not infer active usage or all employees.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "b67a614a4fd3f8ca2a7b96cfd1281a5032a2e7ad01be91f5a982ed4d99a9c138", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-005", "companyId": "emirates-group", "sourceId": "L-S03", "dimension": "lineage", "claim": "ARRIVE records intent, scope, risk, evidence and decisions in AI-assisted software delivery.", "url": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "originalSourceUrl": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "sourceTitle": "Catalysing the next wave of innovation: ForsaTEK 2026 concludes successfully", "corroboratingSourceIds": [], "publisher": "Emirates Group", "sourceType": "corporate-disclosure", "publicationDate": "2026-09-28", "accessedDate": "2026-10-10", "locator": "ARRIVE software-engineering framework paragraph", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes. IT software-delivery scope; record completeness and audit results undisclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Primary Emirates paragraph115 describes records for intent, scope, risk, evidence and decisions in the software lifecycle. Retain3 for this IT lineage mechanism, not enterprise-wide traceability.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "9b33c371d45d272513cf3bdb957cf191801e14a686e183164c4310f4dce04e04", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-006", "companyId": "emirates-group", "sourceId": "L-S03", "dimension": "governance", "claim": "ARRIVE embeds governance and human accountability in Group IT AI-assisted engineering.", "url": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "originalSourceUrl": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "sourceTitle": "Catalysing the next wave of innovation: ForsaTEK 2026 concludes successfully", "corroboratingSourceIds": [], "publisher": "Emirates Group", "sourceType": "corporate-disclosure", "publicationDate": "2026-09-28", "accessedDate": "2026-10-10", "locator": "ARRIVE paragraph; governance and human accountability", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes. No evidence of enterprise-wide model inventory, independent assurance or incident performance. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Primary Emirates paragraph115 names ARRIVE and governance/human-accountability plus the structured delivery record. Retain3 for described IT operating framework, not proof of governance effectiveness.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "86f149e66c93eb38ef0d504a2d4d0a673ec6a19abdd7b7624bf6e339592c362e", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-007", "companyId": "emirates-group", "sourceId": "L-S03", "dimension": "experimentation", "claim": "ARRIVE followed two years of controlled experimentation by Group IT.", "url": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "originalSourceUrl": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "sourceTitle": "Catalysing the next wave of innovation: ForsaTEK 2026 concludes successfully", "corroboratingSourceIds": [], "publisher": "Emirates Group", "sourceType": "corporate-disclosure", "publicationDate": "2026-09-28", "accessedDate": "2026-10-10", "locator": "ARRIVE development history", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Emirates Group, including Emirates airline and dnata; claim-specific IT and Airport Services scopes. Duration alone does not establish scientific evaluation or causal results. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Actual sustained internal experimentation is reported; evaluation methods are not specified.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "b9d6e01de2ecbdddc2d263d09e78afa5573c64bad365f44f47830e5949f58d27", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 2, "workforce": 3, "ecosystem": null, "interfaces": null, "lineage": 3, "experimentation": 2, "autonomy": null, "knowledge": null, "governance": 3 }, "points": 13, "score": 48, "coverage": 5, "rank": 1, "sourceCount": 3, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 7, "excludedClaims": 0, "eligibleSourceUrls": 3, "undatedEligibleClaims": 0, "latestSourceDate": "2026-09-28", "lineage": [ { "dimension": "ai-core", "level": 2, "winningClaimIds": [ "LOG-001" ], "contribution": 7.4074 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "LOG-004" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": 3, "winningClaimIds": [ "LOG-005" ], "contribution": 11.1111 }, { "dimension": "experimentation", "level": 2, "winningClaimIds": [ "LOG-007" ], "contribution": 7.4074 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "LOG-006" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/", "scoreWithoutSource": 14.81 }, { "url": "https://www.emirates.com/media-centre/forsatek-2025-offers-platform-for-high-impact-scalable-innovation/", "scoreWithoutSource": 40.74 }, { "url": "https://www.emirates.com/media-centre/emirates-group-collaborates-with-openai-to-accelerate-ai-adoption-and-innovation/", "scoreWithoutSource": 48.15 } ], "scenarioResults": [ { "scenario": "baseline", "score": 48.15, "rank": 1 }, { "scenario": "implemented-only", "score": 48.15, "rank": 1 }, { "scenario": "detail-cap", "score": 37.04, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 47.06, "rank": 1 }, { "scenario": "ai-core-2", "score": 50.0, "rank": 1 }, { "scenario": "workforce-0.5", "score": 45.1, "rank": 1 }, { "scenario": "workforce-2", "score": 53.33, "rank": 1 }, { "scenario": "ecosystem-0.5", "score": 50.98, "rank": 1 }, { "scenario": "ecosystem-2", "score": 43.33, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 50.98, "rank": 1 }, { "scenario": "interfaces-2", "score": 43.33, "rank": 1 }, { "scenario": "lineage-0.5", "score": 45.1, "rank": 1 }, { "scenario": "lineage-2", "score": 53.33, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 47.06, "rank": 1 }, { "scenario": "experimentation-2", "score": 50.0, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 50.98, "rank": 1 }, { "scenario": "autonomy-2", "score": 43.33, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 50.98, "rank": 1 }, { "scenario": "knowledge-2", "score": 43.33, "rank": 1 }, { "scenario": "governance-0.5", "score": 45.1, "rank": 1 }, { "scenario": "governance-2", "score": 53.33, "rank": 1 } ], "scenarioRankRange": [ 1, 1 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "qatar-airways-group", "name": "Qatar Airways Group", "country": "Qatar", "sector": "Aviation & Logistics", "scope": "Qatar Airways Group; cargo revenue-management implementation and group transformation plans", "scopeLimitations": "Cargo implementation must not be generalized to passenger operations or all group functions.", "cluster": null, "evidence": [ { "id": "LOG-017", "companyId": "qatar-airways-group", "sourceId": "L-S09", "dimension": "ai-core", "claim": "Qatar Airways Cargo went live with CARGOSTACK Optimiser, gradually rolling out demand, inventory and overbooking optimization.", "url": "https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser", "originalSourceUrl": "https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser", "sourceTitle": "Qatar Airways Cargo goes live with CARGOSTACK Optimiser", "corroboratingSourceIds": [], "publisher": "Wiremind", "sourceType": "official-partner-publication", "publicationDate": "2024-11-06", "accessedDate": "2026-10-10", "locator": "Go-live paragraph and phased implementation details", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Qatar Airways Group; cargo revenue-management implementation and group transformation plans. Cargo scope; staged rollout means no inference of complete network or module coverage. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Reopened Wiremind primary implementation account94–95: go-live, progressive rollout, demand/inventory/overbooking and bid-price ML. Retain3 for Cargo revenue-management implementation, not all Qatar Airways operations.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ad1af793dde2de3f97637d2aabe288adfdd813e2771429b2764ac505ab3e81e1", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-018", "companyId": "qatar-airways-group", "sourceId": "L-S09", "dimension": "experimentation", "claim": "The cargo team and Wiremind tested and iterated multiple machine-learning approaches during model validation.", "url": "https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser", "originalSourceUrl": "https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser", "sourceTitle": "Qatar Airways Cargo goes live with CARGOSTACK Optimiser", "corroboratingSourceIds": [], "publisher": "Wiremind", "sourceType": "official-partner-publication", "publicationDate": "2024-11-06", "accessedDate": "2026-10-10", "locator": "Collaboration and model-development/validation paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Qatar Airways Group; cargo revenue-management implementation and group transformation plans. Validation criteria, datasets and comparative performance results are not public. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Reopened Wiremind paragraph106: company-team implementation input and explicit multiple ML approaches tested and iterated. Retain3 under evaluation-process anchor; no criteria, effect size or rigorous independent validation established.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "8ad5061e10a3ca54c4f00f177dbc245f9eee6bb86ac2670188f0f89b0a16ff37", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-019", "companyId": "qatar-airways-group", "sourceId": "L-S09", "dimension": "ecosystem", "claim": "Qatar Airways Cargo staff contributed to the supplier implementation and refinement of CARGOSTACK Optimiser.", "url": "https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser", "originalSourceUrl": "https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser", "sourceTitle": "Qatar Airways Cargo goes live with CARGOSTACK Optimiser", "corroboratingSourceIds": [], "publisher": "Wiremind", "sourceType": "official-partner-publication", "publicationDate": "2024-11-06", "accessedDate": "2026-10-10", "locator": "Joint-development and airline-team contribution paragraphs", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Qatar Airways Group; cargo revenue-management implementation and group transformation plans. Partner-authored account; not evidence of broader ecosystem breadth. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named staff/supplier collaboration in an implemented internal AI operating system.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "40b567a03ad01b739e523b19c7e8677f47ca69bc7db6397eeded6a25d420ff9a", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-020", "companyId": "qatar-airways-group", "sourceId": "L-S10", "dimension": "governance", "claim": "The AI Skyways programme commits to responsible-AI controls including ethical guidelines, privacy and continuous monitoring.", "url": "https://newsroom.accenture.com/news/2025/qatar-airways-and-accenture-embark-on-ai-powered-partnership-to-set-new-benchmark-in-aviation-excellence", "originalSourceUrl": "https://newsroom.accenture.com/news/2025/qatar-airways-and-accenture-embark-on-ai-powered-partnership-to-set-new-benchmark-in-aviation-excellence", "sourceTitle": "Qatar Airways and Accenture embark on AI-powered partnership", "corroboratingSourceIds": [], "publisher": "Accenture", "sourceType": "official-partner-publication", "publicationDate": "2025-08-13", "accessedDate": "2026-10-10", "locator": "Responsible AI and joint operating model paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Qatar Airways Group; cargo revenue-management implementation and group transformation plans. Future tense; control operation and assurance results are not established. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific AI transformation programme and named intended controls.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "d12bca29c08e0936813f2f648e9bbaebafb759250be9657208920e3b1ee2b4db", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 2, "interfaces": null, "lineage": null, "experimentation": 3, "autonomy": null, "knowledge": null, "governance": 1 }, "points": 9, "score": 33, "coverage": 4, "rank": 2, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 4, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 0, "latestSourceDate": "2025-08-13", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "LOG-017" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 2, "winningClaimIds": [ "LOG-019" ], "contribution": 7.4074 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 3, "winningClaimIds": [ "LOG-018" ], "contribution": 11.1111 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 1, "winningClaimIds": [ "LOG-020" ], "contribution": 3.7037 } ], "singleSourceRemoval": [ { "url": "https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser", "scoreWithoutSource": 3.7 }, { "url": "https://newsroom.accenture.com/news/2025/qatar-airways-and-accenture-embark-on-ai-powered-partnership-to-set-new-benchmark-in-aviation-excellence", "scoreWithoutSource": 29.63 } ], "scenarioResults": [ { "scenario": "baseline", "score": 33.33, "rank": 2 }, { "scenario": "implemented-only", "score": 29.63, "rank": 2 }, { "scenario": "detail-cap", "score": 25.93, "rank": 2 }, { "scenario": "ai-core-0.5", "score": 29.41, "rank": 2 }, { "scenario": "ai-core-2", "score": 40.0, "rank": 2 }, { "scenario": "workforce-0.5", "score": 35.29, "rank": 2 }, { "scenario": "workforce-2", "score": 30.0, "rank": 2 }, { "scenario": "ecosystem-0.5", "score": 31.37, "rank": 2 }, { "scenario": "ecosystem-2", "score": 36.67, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 35.29, "rank": 2 }, { "scenario": "interfaces-2", "score": 30.0, "rank": 3 }, { "scenario": "lineage-0.5", "score": 35.29, "rank": 2 }, { "scenario": "lineage-2", "score": 30.0, "rank": 2 }, { "scenario": "experimentation-0.5", "score": 29.41, "rank": 2 }, { "scenario": "experimentation-2", "score": 40.0, "rank": 2 }, { "scenario": "autonomy-0.5", "score": 35.29, "rank": 2 }, { "scenario": "autonomy-2", "score": 30.0, "rank": 2 }, { "scenario": "knowledge-0.5", "score": 35.29, "rank": 2 }, { "scenario": "knowledge-2", "score": 30.0, "rank": 2 }, { "scenario": "governance-0.5", "score": 33.33, "rank": 2 }, { "scenario": "governance-2", "score": 33.33, "rank": 2 } ], "scenarioRankRange": [ 2, 3 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "dp-world", "name": "DP World", "country": "UAE", "sector": "Aviation & Logistics", "scope": "DP World global group; cited operating implementation at Jafza/Jebel Ali", "scopeLimitations": "UAE headquarters is not the geographical boundary of group operations; site-level evidence must remain site-level.", "cluster": null, "evidence": [ { "id": "LOG-014", "companyId": "dp-world", "sourceId": "L-S07", "dimension": "ai-core", "claim": "DP World describes AI-enabled CARGOES TOS+ at Jafza tracking terminal assets and identifying operational inefficiencies.", "url": "https://www.dpworld.com/en/insights/is-artificial-intelligence-the-key-to-resilient-supply-chains", "originalSourceUrl": "https://www.dpworld.com/en/insights/is-artificial-intelligence-the-key-to-resilient-supply-chains", "sourceTitle": "Is artificial intelligence the key to resilient supply chains?", "corroboratingSourceIds": [], "publisher": "DP World", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "What does AI look like in action / Jafza and TOS+ paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: DP World global group; cited operating implementation at Jafza/Jebel Ali. Site-specific issuer description; undated page and no independent evaluation. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Reopened DP World primary page paragraph86: current TOS+ use in Jafza, container/equipment tracking and operational inefficiency identification. Retain3 for site-specific internal process, not global coverage.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "c2b0b027ea07fb032b7164bd7eba0c93690d62acd1c4fca25ee708aad0d7129d", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-015", "companyId": "dp-world", "sourceId": "L-S07", "dimension": "workforce", "claim": "CARGOES Rostering is being trialled in Europe and Australia to match staff capabilities with operating requirements.", "url": "https://www.dpworld.com/en/insights/is-artificial-intelligence-the-key-to-resilient-supply-chains", "originalSourceUrl": "https://www.dpworld.com/en/insights/is-artificial-intelligence-the-key-to-resilient-supply-chains", "sourceTitle": "Is artificial intelligence the key to resilient supply chains?", "corroboratingSourceIds": [], "publisher": "DP World", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "CARGOES Rostering paragraph", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: DP World global group; cited operating implementation at Jafza/Jebel Ali. Trial results and workforce adoption coverage are not established. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named AI workforce-allocation trial with geography and purpose.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "b040700d57f558295732a935d8ae09d9836d69b5276ade847ace303a443d2136", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-016", "companyId": "dp-world", "sourceId": "L-S08", "dimension": "interfaces", "claim": "The AI-driven TOS+ digital twin at Jafza uses real-time data for terminal actions including gate opening and maintenance alerts.", "url": "https://www.dpworld.com/en/insights/how-ai-is-transforming-global-trade-possibilities", "originalSourceUrl": "https://www.dpworld.com/en/insights/how-ai-is-transforming-global-trade-possibilities", "sourceTitle": "How AI is transforming global trade possibilities", "corroboratingSourceIds": [], "publisher": "DP World", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Digital twin and CARGOES TOS+ paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: DP World global group; cited operating implementation at Jafza/Jebel Ali. Does not establish autonomous high-stakes authority; human approval rules and rollback controls undisclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Reopened DP World CTO account paragraphs107–110: AI suite, Jafza digital twin, gate opening and maintenance alerts. 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AI vessel monitoring is attributed only to the cited businesses.", "cluster": null, "evidence": [ { "id": "LOG-011", "companyId": "bahri", "sourceId": "L-S05", "dimension": "ai-core", "claim": "Bahri reports AI/IoT vessel monitoring using navigation, engine and cargo data for predictive maintenance, fuel and route optimization.", "url": "https://www.bahri.sa/media/go4a3eke/bahri-ar25_english-final-to-tadawul.pdf", "originalSourceUrl": "https://www.bahri.sa/media/go4a3eke/bahri-ar25_english-final-to-tadawul.pdf", "sourceTitle": "Bahri Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Bahri", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF page 54, printed pages 106-107, Digital Transformation", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: National Shipping Company of Saudi Arabia (Bahri), consolidated group; vessel monitoring specifically Bahri Oil and Bahri Ship Management. Particularly Bahri Oil and Bahri Ship Management; accuracy, adoption coverage and independently measured effects undisclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Read retained issuer-PDF extraction at lines4138–4146, Digital Transformation printed106–107: explicit AI/IoT monitoring, navigation/engine/cargo inputs and maintenance/route uses. Retain3, specifically Bahri Oil/BSM. This review read the locally retained source extraction; web fetch failed, no independent operational assurance.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "9e634c8c5fb69afce0b1efb1c8c3dc58c529632d970bc494be604628a231911e", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-012", "companyId": "bahri", "sourceId": "L-S05", "dimension": "knowledge", "claim": "An enterprise data platform combines vessel, logistics and transactional data to support AI-assisted business decisions.", "url": "https://www.bahri.sa/media/go4a3eke/bahri-ar25_english-final-to-tadawul.pdf", "originalSourceUrl": "https://www.bahri.sa/media/go4a3eke/bahri-ar25_english-final-to-tadawul.pdf", "sourceTitle": "Bahri Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Bahri", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF page 54, printed pages 106-107, enterprise data analytics platform", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: National Shipping Company of Saudi Arabia (Bahri), consolidated group; vessel monitoring specifically Bahri Oil and Bahri Ship Management. Shared data access is not proof of model-level provenance or decision lineage. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "The report describes an implemented AI-supporting shared information foundation and decision uses.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "bdfb66f737e51b7d1b1c8be697d4bbdf9ccc2ff1c482488634bc9fe6b239b6f8", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-013", "companyId": "bahri", "sourceId": "L-S06", "dimension": "ecosystem", "claim": "Bahri and Aventra announced a maritime data-orchestration platform intended to support machine learning and AI.", "url": "https://www.bahri.sa/en/news-insights/news-insight/2022/bahri-partners-with-aventra-group-to-accelerate-digital-transformation-journey/", "originalSourceUrl": "https://www.bahri.sa/en/news-insights/news-insight/2022/bahri-partners-with-aventra-group-to-accelerate-digital-transformation-journey/", "sourceTitle": "Bahri partners with Aventra Group to accelerate digital transformation journey", "corroboratingSourceIds": [], "publisher": "Bahri", "sourceType": "corporate-disclosure", "publicationDate": "2022-08-08", "accessedDate": "2026-10-10", "locator": "Partnership and platform-function paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: National Shipping Company of Saudi Arabia (Bahri), consolidated group; vessel monitoring specifically Bahri Oil and Bahri Ship Management. 2022 commitment; the 2025 report does not explicitly identify Aventra as the provider of its operating platform. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named internal AI-related platform programme with partner delivery role.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "4689b01a3dd5bb39316dd7b6be9dc3e1db32bbdf0807e92343c41bb69df33bb1", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 1, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": 2, "governance": null }, "points": 6, "score": 22, "coverage": 3, "rank": 4, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 3, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 2, "latestSourceDate": "2022-08-08", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "LOG-011" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "LOG-013" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 2, "winningClaimIds": [ "LOG-012" ], "contribution": 7.4074 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.bahri.sa/media/go4a3eke/bahri-ar25_english-final-to-tadawul.pdf", "scoreWithoutSource": 3.7 }, { "url": "https://www.bahri.sa/en/news-insights/news-insight/2022/bahri-partners-with-aventra-group-to-accelerate-digital-transformation-journey/", "scoreWithoutSource": 18.52 } ], "scenarioResults": [ { "scenario": "baseline", "score": 22.22, "rank": 4 }, { "scenario": "implemented-only", "score": 18.52, "rank": 4 }, { "scenario": "detail-cap", "score": 18.52, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 17.65, "rank": 5 }, { "scenario": "ai-core-2", "score": 30.0, "rank": 4 }, { "scenario": "workforce-0.5", "score": 23.53, "rank": 4 }, { "scenario": "workforce-2", "score": 20.0, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 21.57, "rank": 5 }, { "scenario": "ecosystem-2", "score": 23.33, "rank": 3 }, { "scenario": "interfaces-0.5", "score": 23.53, "rank": 3 }, { "scenario": "interfaces-2", "score": 20.0, "rank": 5 }, { "scenario": "lineage-0.5", "score": 23.53, "rank": 4 }, { "scenario": "lineage-2", "score": 20.0, "rank": 4 }, { "scenario": "experimentation-0.5", "score": 23.53, "rank": 4 }, { "scenario": "experimentation-2", "score": 20.0, "rank": 4 }, { "scenario": "autonomy-0.5", "score": 23.53, "rank": 4 }, { "scenario": "autonomy-2", "score": 20.0, "rank": 4 }, { "scenario": "knowledge-0.5", "score": 19.61, "rank": 4 }, { "scenario": "knowledge-2", "score": 26.67, "rank": 3 }, { "scenario": "governance-0.5", "score": 23.53, "rank": 4 }, { "scenario": "governance-2", "score": 20.0, "rank": 4 } ], "scenarioRankRange": [ 3, 5 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "gulf-air-group", "name": "Gulf Air Group", "country": "Bahrain", "sector": "Aviation & Logistics", "scope": "Gulf Air Group Holding; source-specific Gulf Air airline executive and training applications", "scopeLimitations": "Gulf Air airline application evidence does not establish implementation in Bahrain Airport Company or the aviation academy.", "cluster": "gulf-air-family", "evidence": [ { "id": "LOG-031", "companyId": "gulf-air-group", "sourceId": "L-S19", "dimension": "knowledge", "claim": "Gulf Air Group says it has begun working with Power BI Copilot for internal dashboard creation.", "url": "https://www.microsoft.com/en/customers/story/22484-gulf-air-group-microsoft-365-e5", "originalSourceUrl": "https://www.microsoft.com/en/customers/story/22484-gulf-air-group-microsoft-365-e5", "sourceTitle": "Gulf Air Group strengthens security and collaboration with Microsoft 365 E5", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2025-02-17", "accessedDate": "2026-10-10", "locator": "Power BI Copilot future-development passage", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Gulf Air Group Holding; source-specific Gulf Air airline executive and training applications. Ordinary Power BI deployment and training counts do not establish Copilot adoption. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific internal generative-AI exploration; the source does not establish routine production use.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "994021a91fa7df8ac77dcef4e9e20e994d2a81fcabe12cb1d16bfe259b14f072", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-032", "companyId": "gulf-air-group", "sourceId": "L-S19", "dimension": "interfaces", "claim": "Gulf Air Group is working toward AI interpretation of invoices and comparison with contracts to flag discrepancies.", "url": "https://www.microsoft.com/en/customers/story/22484-gulf-air-group-microsoft-365-e5", "originalSourceUrl": "https://www.microsoft.com/en/customers/story/22484-gulf-air-group-microsoft-365-e5", "sourceTitle": "Gulf Air Group strengthens security and collaboration with Microsoft 365 E5", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2025-02-17", "accessedDate": "2026-10-10", "locator": "AI-enabled financial-services and invoice-checking passage", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Gulf Air Group Holding; source-specific Gulf Air airline executive and training applications. No completed deployment, accuracy or approval workflow established. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named internal AI workflow under implementation, with inputs and intended action.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "5deb8ac015aa365623c7437133f602a692c748c749529f30bd57d33edf9dceba", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-033", "companyId": "gulf-air-group", "sourceId": "L-S20", "dimension": "interfaces", "claim": "Gulf Air FalconEye provides executives conversational access to live operations and voice commands that open dashboards.", "url": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/public-preview-voice-native-agents-in-microsoft-foundry/4502756", "originalSourceUrl": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/public-preview-voice-native-agents-in-microsoft-foundry/4502756", "sourceTitle": "Public preview: Voice-native agents in Microsoft Foundry", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2026-03-16", "accessedDate": "2026-10-10", "locator": "Customer example: Gulf Air FalconEye and Ahmed Naeemi statement", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Gulf Air Group Holding; source-specific Gulf Air airline executive and training applications. Airline executive scope; preview product context, unreported user count and no independent performance validation. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Read retained primary Microsoft HTML JSON-LD extraction lines28–31: FalconEye live data and voice navigation opening dashboards, attributed to airline CIO. Separate readable Microsoft June2 developer article paragraph67 repeats live-data executive interaction. Retain3 for narrow interface mechanism; vendor publications are related evidence, not independent corroboration.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "fd8edd5691c0e9b26ab4c47d8a0b669ffebf8d9acaffd838a5e1affb9349ae64", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-034", "companyId": "gulf-air-group", "sourceId": "L-S20", "dimension": "knowledge", "claim": "FalconEye combines flight, passenger, crew and other operating information for executive questions.", "url": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/public-preview-voice-native-agents-in-microsoft-foundry/4502756", "originalSourceUrl": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/public-preview-voice-native-agents-in-microsoft-foundry/4502756", "sourceTitle": "Public preview: Voice-native agents in Microsoft Foundry", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2026-03-16", "accessedDate": "2026-10-10", "locator": "Gulf Air FalconEye customer example", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Gulf Air Group Holding; source-specific Gulf Air airline executive and training applications. No claim of complete data lineage, answer reliability or decision authority. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named implemented internal knowledge interface unifies multiple operating information domains.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "d96853f64c5dc57b6943d119b6ae3dabb717e9fe3add646f4951bc7b65dd455a", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-035", "companyId": "gulf-air-group", "sourceId": "L-S21", "dimension": "interfaces", "claim": "Microsoft describes Gulf Air executives receiving spoken answers from live operational data.", "url": "https://devblogs.microsoft.com/foundry/agent-service-build2026/", "originalSourceUrl": "https://devblogs.microsoft.com/foundry/agent-service-build2026/", "sourceTitle": "What is new in Microsoft Foundry Agent Service at Build 2026", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2026-06-02", "accessedDate": "2026-10-10", "locator": "Real-time voice / Gulf Air example", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Gulf Air Group Holding; source-specific Gulf Air airline executive and training applications. Does not increase points beyond the highest qualifying interface claim. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Separate official partner publication corroborates actual internal operational voice use.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "d7d3a7c93762c980c87740fcf2d2c66aecfab328754c8b035e3bb3ed9c8cced9", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-036", "companyId": "gulf-air-group", "sourceId": "L-S22", "dimension": "workforce", "claim": "Gulf Air tested avatar and voice technology for pilot, cabin-crew and engineer training.", "url": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/advancing-speech-innovation-with-azure-speech-in-microsoft-foundry/4471461", "originalSourceUrl": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/advancing-speech-innovation-with-azure-speech-in-microsoft-foundry/4471461", "sourceTitle": "Advancing speech innovation with Azure Speech in Microsoft Foundry", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2025-11-20", "accessedDate": "2026-10-10", "locator": "Gulf Air customer example and company statement concerning testing", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Gulf Air Group Holding; source-specific Gulf Air airline executive and training applications. Narrator uses stronger deployment language, but preview context and customer wording do not justify a production anchor. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific employee-training AI trial; conservative interpretation of a company quote that describes testing.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "a843ff8547dbb76431278c92ba86433f85e9c52d6a5aa400cda3d3b868f22129", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": 1, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": 2, "governance": null }, "points": 6, "score": 22, "coverage": 3, "rank": 4, "sourceCount": 4, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 6, "excludedClaims": 0, "eligibleSourceUrls": 4, "undatedEligibleClaims": 0, "latestSourceDate": "2026-06-02", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": 1, "winningClaimIds": [ "LOG-036" ], "contribution": 3.7037 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "LOG-033" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 2, "winningClaimIds": [ "LOG-034" ], "contribution": 7.4074 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/public-preview-voice-native-agents-in-microsoft-foundry/4502756", "scoreWithoutSource": 14.81 }, { "url": "https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/advancing-speech-innovation-with-azure-speech-in-microsoft-foundry/4471461", "scoreWithoutSource": 18.52 }, { "url": "https://devblogs.microsoft.com/foundry/agent-service-build2026/", "scoreWithoutSource": 22.22 }, { "url": "https://www.microsoft.com/en/customers/story/22484-gulf-air-group-microsoft-365-e5", "scoreWithoutSource": 22.22 } ], "scenarioResults": [ { "scenario": "baseline", "score": 22.22, "rank": 4 }, { "scenario": "implemented-only", "score": 18.52, "rank": 4 }, { "scenario": "detail-cap", "score": 18.52, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 23.53, "rank": 3 }, { "scenario": "ai-core-2", "score": 20.0, "rank": 6 }, { "scenario": "workforce-0.5", "score": 21.57, "rank": 5 }, { "scenario": "workforce-2", "score": 23.33, "rank": 4 }, { "scenario": "ecosystem-0.5", "score": 23.53, "rank": 4 }, { "scenario": "ecosystem-2", "score": 20.0, "rank": 6 }, { "scenario": "interfaces-0.5", "score": 17.65, "rank": 6 }, { "scenario": "interfaces-2", "score": 30.0, "rank": 3 }, { "scenario": "lineage-0.5", "score": 23.53, "rank": 4 }, { "scenario": "lineage-2", "score": 20.0, "rank": 4 }, { "scenario": "experimentation-0.5", "score": 23.53, "rank": 4 }, { "scenario": "experimentation-2", "score": 20.0, "rank": 4 }, { "scenario": "autonomy-0.5", "score": 23.53, "rank": 4 }, { "scenario": "autonomy-2", "score": 20.0, "rank": 4 }, { "scenario": "knowledge-0.5", "score": 19.61, "rank": 4 }, { "scenario": "knowledge-2", "score": 26.67, "rank": 3 }, { "scenario": "governance-0.5", "score": 23.53, "rank": 4 }, { "scenario": "governance-2", "score": 20.0, "rank": 4 } ], "scenarioRankRange": [ 3, 6 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "saudia-group", "name": "Saudia Group", "country": "KSA", "sector": "Aviation & Logistics", "scope": "Saudia Group; located implementation is Saudia Academy training operations", "scopeLimitations": "Academy evidence is a bounded training-business case; it does not establish adoption throughout the airline or other group companies.", "cluster": null, "evidence": [ { "id": "LOG-008", "companyId": "saudia-group", "sourceId": "L-S04", "dimension": "ai-core", "claim": "Saudia Academy implemented AI-enabled scheduling in its training-management system, supporting instructor and facility allocation.", "url": "https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia", "originalSourceUrl": "https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia", "sourceTitle": "Saudia Academy transforms aviation training with Dynamics 365", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2024-06-20", "accessedDate": "2026-10-10", "locator": "Technology implementation and instructor scheduling passages", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Saudia Group; located implementation is Saudia Academy training operations. Academy scope only; reported training improvements mix AI and wider platform changes. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Primary Microsoft customer account paragraphs46–50 explicitly describes implemented AI scheduling; KPI section records instructor schedules and resource allocation. Retain3 for academy workflow only. Vendor/customer report is commercially connected and broad outcome percentages are not causal AI estimates.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "3fedd3b3ebb676efef988a0bdd396361e11b9dd45df20258c460ed3bda4daa59", "review": { "decision": "accept", "record": "expansion/logistics/root-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-009", "companyId": "saudia-group", "sourceId": "L-S04", "dimension": "ecosystem", "claim": "Netways implemented Saudia Academy training operations using Microsoft business applications and AI services.", "url": "https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia", "originalSourceUrl": "https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia", "sourceTitle": "Saudia Academy transforms aviation training with Dynamics 365", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2024-06-20", "accessedDate": "2026-10-10", "locator": "Netways implementation paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Saudia Group; located implementation is Saudia Academy training operations. Does not establish an enterprise-wide research or innovation ecosystem. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named partner roles in an actual internal AI-enabled system implementation.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "1a841a7b2579d460c6d02e1d7df7355ffb1c2c730853722123b2e1888ef516ee", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-010", "companyId": "saudia-group", "sourceId": "L-S04", "dimension": "governance", "claim": "The training system gives GACA access to training records and uses security roles.", "url": "https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia", "originalSourceUrl": "https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia", "sourceTitle": "Saudia Academy transforms aviation training with Dynamics 365", "corroboratingSourceIds": [], "publisher": "Microsoft", "sourceType": "official-partner-publication", "publicationDate": "2024-06-20", "accessedDate": "2026-10-10", "locator": "GACA audit access and security roles paragraphs", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Saudia Group; located implementation is Saudia Academy training operations. AI decision review, model accountability and AI risk controls are not demonstrated. Application: excluded-general-digital-control. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "full-source-read", "rationale": "Excluded: training-record access and ordinary system security do not establish AI-specific governance. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "87fd1244ffc4eb2a77a860bab1366c70e7f1db01292eaaee2edc4720044acf36", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 2, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 5, "score": 19, "coverage": 2, "rank": 6, "sourceCount": 1, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 2, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2024-06-20", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "LOG-008" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 2, "winningClaimIds": [ "LOG-009" ], "contribution": 7.4074 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 18.52, "rank": 6 }, { "scenario": "implemented-only", "score": 18.52, "rank": 4 }, { "scenario": "detail-cap", "score": 14.81, "rank": 6 }, { "scenario": "ai-core-0.5", "score": 13.73, "rank": 6 }, { "scenario": "ai-core-2", "score": 26.67, "rank": 5 }, { "scenario": "workforce-0.5", "score": 19.61, "rank": 6 }, { "scenario": "workforce-2", "score": 16.67, "rank": 6 }, { "scenario": "ecosystem-0.5", "score": 15.69, "rank": 6 }, { "scenario": "ecosystem-2", "score": 23.33, "rank": 3 }, { "scenario": "interfaces-0.5", "score": 19.61, "rank": 5 }, { "scenario": "interfaces-2", "score": 16.67, "rank": 6 }, { "scenario": "lineage-0.5", "score": 19.61, "rank": 6 }, { "scenario": "lineage-2", "score": 16.67, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 19.61, "rank": 6 }, { "scenario": "experimentation-2", "score": 16.67, "rank": 6 }, { "scenario": "autonomy-0.5", "score": 19.61, "rank": 6 }, { "scenario": "autonomy-2", "score": 16.67, "rank": 6 }, { "scenario": "knowledge-0.5", "score": 19.61, "rank": 4 }, { "scenario": "knowledge-2", "score": 16.67, "rank": 6 }, { "scenario": "governance-0.5", "score": 19.61, "rank": 6 }, { "scenario": "governance-2", "score": 16.67, "rank": 6 } ], "scenarioRankRange": [ 3, 6 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "makhazen", "name": "Makhazen (Agility Public Warehousing)", "country": "Kuwait", "sector": "Aviation & Logistics", "scope": "Agility Public Warehousing Company K.S.C.P., branded Makhazen; 2024 historical group disclosures and Kuwait logistics parks", "scopeLimitations": "The 2024 consolidated perimeter predates the 2025 restructuring. UAE-listed Agility Global employee-AI disclosures are not attributed to this Kuwait entity.", "cluster": "agility-family", "evidence": [ { "id": "LOG-028", "companyId": "makhazen", "sourceId": "L-S16", "dimension": "interfaces", "claim": "The 2024 annual report lists AI-based antivirus among implemented information-security controls.", "url": "https://makhazen.com/wp-content/uploads/2025/05/Agility-Annual-Report-2024-En.pdf", "originalSourceUrl": "https://makhazen.com/wp-content/uploads/2025/05/Agility-Annual-Report-2024-En.pdf", "sourceTitle": "Agility Annual Report 2024", "corroboratingSourceIds": [], "publisher": "Makhazen", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF page 27, IT security controls list", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Agility Public Warehousing Company K.S.C.P., branded Makhazen; 2024 historical group disclosures and Kuwait logistics parks. Historical consolidated group perimeter; scope changed in 2025. Ordinary AI antivirus is not evidence of AI governance. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "A specific AI-enabled internal security tool is listed as in place.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "643c4e171bfcc2857b06e76a3cea068e3ebaef47a67ec32687bca1645156e69e", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-029", "companyId": "makhazen", "sourceId": "L-S18", "dimension": "ecosystem", "claim": "Agility and Huawei specified a Kuwait smart-campus programme including AI-enabled visitor, vehicle and security applications.", "url": "https://makhazen.com/en/agility-signs-mou-with-huawei-to-transform-its-logistics-industrial-parks-to-smart-campuses/", "originalSourceUrl": "https://makhazen.com/en/agility-signs-mou-with-huawei-to-transform-its-logistics-industrial-parks-to-smart-campuses/", "sourceTitle": "Agility signs MOU with Huawei to transform logistics industrial parks to smart campuses", "corroboratingSourceIds": [], "publisher": "Makhazen", "sourceType": "corporate-disclosure", "publicationDate": "2021-10-31", "accessedDate": "2026-10-10", "locator": "Technology evaluation and Mina Abdullah campus paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Agility Public Warehousing Company K.S.C.P., branded Makhazen; 2024 historical group disclosures and Kuwait logistics parks. Some technology implementation had begun, but the source does not establish which AI components were live. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named site and partner programme with specific intended AI uses; exceeds a generic logo-only agreement.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "521120f5dbbd43c846f6f30811ee1c88cbc95e9ff16bbc588a3441800609f89e", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-030", "companyId": "makhazen", "sourceId": "L-S17", "dimension": "governance", "claim": "The 2025 report describes corporate risk and control arrangements.", "url": "https://makhazen.com/wp-content/uploads/2026/05/Makhazen-Annual-Report-2025-English.pdf", "originalSourceUrl": "https://makhazen.com/wp-content/uploads/2026/05/Makhazen-Annual-Report-2025-English.pdf", "sourceTitle": "Makhazen Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Makhazen", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Corporate governance and internal-control sections", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Agility Public Warehousing Company K.S.C.P., branded Makhazen; 2024 historical group disclosures and Kuwait logistics parks. No claim is made that AI governance is absent. Application: excluded-general-corporate-governance. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "full-source-read", "rationale": "Excluded: ordinary corporate controls cannot establish AI-specific governance. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "b1626a6e56d59165e36d10ad078df3fdd6065d4f4e288f3516c7af38775dec25", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": 1, "interfaces": 2, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 3, "score": 11, "coverage": 2, "rank": 7, "sourceCount": 3, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 2, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 1, "latestSourceDate": "2021-10-31", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "LOG-029" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "LOG-028" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://makhazen.com/wp-content/uploads/2025/05/Agility-Annual-Report-2024-En.pdf", "scoreWithoutSource": 3.7 }, { "url": "https://makhazen.com/en/agility-signs-mou-with-huawei-to-transform-its-logistics-industrial-parks-to-smart-campuses/", "scoreWithoutSource": 7.41 } ], "scenarioResults": [ { "scenario": "baseline", "score": 11.11, "rank": 7 }, { "scenario": "implemented-only", "score": 7.41, "rank": 7 }, { "scenario": "detail-cap", "score": 11.11, "rank": 7 }, { "scenario": "ai-core-0.5", "score": 11.76, "rank": 7 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 8 }, { "scenario": "workforce-0.5", "score": 11.76, "rank": 7 }, { "scenario": "workforce-2", "score": 10.0, "rank": 7 }, { "scenario": "ecosystem-0.5", "score": 9.8, "rank": 7 }, { "scenario": "ecosystem-2", "score": 13.33, "rank": 7 }, { "scenario": "interfaces-0.5", "score": 7.84, "rank": 7 }, { "scenario": "interfaces-2", "score": 16.67, "rank": 6 }, { "scenario": "lineage-0.5", "score": 11.76, "rank": 7 }, { "scenario": "lineage-2", "score": 10.0, "rank": 7 }, { "scenario": "experimentation-0.5", "score": 11.76, "rank": 7 }, { "scenario": "experimentation-2", "score": 10.0, "rank": 7 }, { "scenario": "autonomy-0.5", "score": 11.76, "rank": 7 }, { "scenario": "autonomy-2", "score": 10.0, "rank": 7 }, { "scenario": "knowledge-0.5", "score": 11.76, "rank": 7 }, { "scenario": "knowledge-2", "score": 10.0, "rank": 7 }, { "scenario": "governance-0.5", "score": 11.76, "rank": 7 }, { "scenario": "governance-2", "score": 10.0, "rank": 7 } ], "scenarioRankRange": [ 6, 8 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "jazeera-airways", "name": "Jazeera Airways", "country": "Kuwait", "sector": "Aviation & Logistics", "scope": "Jazeera Airways K.S.C.P.; airline fuel-efficiency operations", "scopeLimitations": "AI identity of SkyBreathe is described by the supplier; continued programme use is corroborated in the airline annual report. Configuration and use coverage are undisclosed.", "cluster": null, "evidence": [ { "id": "LOG-025", "companyId": "jazeera-airways", "sourceId": "L-S14", "dimension": "ai-core", "claim": "Jazeera selected SkyBreathe, an AI/ML fuel-analysis platform using flight, payload, weather and maintenance information.", "url": "https://www.prnewswire.com/news-releases/jazeera-airways-chooses-skybreathe-360-eco-flying-platform-to-cut-carbon-emissions-301567557.html", "originalSourceUrl": "https://www.prnewswire.com/news-releases/jazeera-airways-chooses-skybreathe-360-eco-flying-platform-to-cut-carbon-emissions-301567557.html", "sourceTitle": "Jazeera Airways chooses SkyBreathe 360 eco-flying platform", "corroboratingSourceIds": [], "publisher": "OpenAirlines", "sourceType": "official-partner-publication", "publicationDate": "2022-06-14", "accessedDate": "2026-10-10", "locator": "Platform selection and analytics-description paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Jazeera Airways K.S.C.P.; airline fuel-efficiency operations. 2022 future implementation wording; later continued use is assessed separately. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific airline procurement commitment for an internal AI operating use.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "4bf2d72c36162804ac510b554c548a55ce6499702dcd07be6f29ec6a2e70c27d", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-026", "companyId": "jazeera-airways", "sourceId": "L-S13", "dimension": "ai-core", "claim": "Jazeera reports participation in the SkyBreathe fuel-efficiency programme during 2024; the supplier identifies that platform as AI/ML-enabled.", "url": "https://investorrelations.jazeeraairways.com/media/1367/j9-fy24-annual-report-english-mar-4-2025.pdf", "originalSourceUrl": "https://investorrelations.jazeeraairways.com/media/1367/j9-fy24-annual-report-english-mar-4-2025.pdf", "sourceTitle": "Jazeera Airways Annual Report 2024", "corroboratingSourceIds": [ "L-S14" ], "publisher": "Jazeera Airways", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF pages 5-6 and 11, SkyBreathe fuel-efficiency references; corroboration L-S14", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Jazeera Airways K.S.C.P.; airline fuel-efficiency operations. AI configuration, workflow coverage and causal savings are undisclosed. Provider ranking is not a Mayden ranking. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Issuer report establishes active programme use; separately read supplier source establishes AI identity.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "3418f627bd7933563ac70ccc110ff4d95a785b54c42c9036e314ee7953b2081e", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-027", "companyId": "jazeera-airways", "sourceId": "L-S15", "dimension": "interfaces", "claim": "The TCS agreement proposes AI-enabled website, app and chatbot changes for passengers.", "url": "https://www.tcs.com/who-we-are/newsroom/press-release/jazeera-airways-partners-with-tcs-to-power-ai-led-transformation-reimagine-digital-customer-experience", "originalSourceUrl": "https://www.tcs.com/who-we-are/newsroom/press-release/jazeera-airways-partners-with-tcs-to-power-ai-led-transformation-reimagine-digital-customer-experience", "sourceTitle": "Jazeera Airways partners with TCS to power AI-led transformation", "corroboratingSourceIds": [], "publisher": "TCS", "sourceType": "corporate-disclosure", "publicationDate": "2025-05-03", "accessedDate": "2026-10-10", "locator": "Digital customer experience and AI-led architecture paragraphs", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Jazeera Airways K.S.C.P.; airline fuel-efficiency operations. No actual internal staff process or production deployment established. Application: excluded-customer-product-only. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "full-source-read", "rationale": "Excluded: proposed customer channels alone do not demonstrate internal AI operating capability. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "799caa711864b0ed89ae561988225e7587b2f184bc670b555384907d05b4544b", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 2, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 2, "score": 7, "coverage": 1, "rank": 8, "sourceCount": 3, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 2, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 1, "latestSourceDate": "2022-06-14", "lineage": [ { "dimension": "ai-core", "level": 2, "winningClaimIds": [ "LOG-026" ], "contribution": 7.4074 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://investorrelations.jazeeraairways.com/media/1367/j9-fy24-annual-report-english-mar-4-2025.pdf", "scoreWithoutSource": 3.7 }, { "url": "https://www.prnewswire.com/news-releases/jazeera-airways-chooses-skybreathe-360-eco-flying-platform-to-cut-carbon-emissions-301567557.html", "scoreWithoutSource": 7.41 } ], "scenarioResults": [ { "scenario": "baseline", "score": 7.41, "rank": 8 }, { "scenario": "implemented-only", "score": 7.41, "rank": 7 }, { "scenario": "detail-cap", "score": 7.41, "rank": 8 }, { "scenario": "ai-core-0.5", "score": 3.92, "rank": 9 }, { "scenario": "ai-core-2", "score": 13.33, "rank": 7 }, { "scenario": "workforce-0.5", "score": 7.84, "rank": 8 }, { "scenario": "workforce-2", "score": 6.67, "rank": 8 }, { "scenario": "ecosystem-0.5", "score": 7.84, "rank": 8 }, { "scenario": "ecosystem-2", "score": 6.67, "rank": 9 }, { "scenario": "interfaces-0.5", "score": 7.84, "rank": 7 }, { "scenario": "interfaces-2", "score": 6.67, "rank": 8 }, { "scenario": "lineage-0.5", "score": 7.84, "rank": 8 }, { "scenario": "lineage-2", "score": 6.67, "rank": 8 }, { "scenario": "experimentation-0.5", "score": 7.84, "rank": 8 }, { "scenario": "experimentation-2", "score": 6.67, "rank": 8 }, { "scenario": "autonomy-0.5", "score": 7.84, "rank": 8 }, { "scenario": "autonomy-2", "score": 6.67, "rank": 8 }, { "scenario": "knowledge-0.5", "score": 7.84, "rank": 8 }, { "scenario": "knowledge-2", "score": 6.67, "rank": 8 }, { "scenario": "governance-0.5", "score": 7.84, "rank": 8 }, { "scenario": "governance-2", "score": 6.67, "rank": 8 } ], "scenarioRankRange": [ 7, 9 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "milaha", "name": "Milaha", "country": "Qatar", "sector": "Aviation & Logistics", "scope": "Qatar Navigation Q.P.S.C. 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(Milaha), group; cited logistics-centre integration. Conservative in-progress anchor; no deployed model, live coverage or measured result identified. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named AI integration activity at an internal site; current wording does not clearly establish production operation.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "12834203f145f88bdd4b167167a5422c96df3d868f4230e4dd8c10c7801f80a4", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-022", "companyId": "milaha", "sourceId": "L-S11", "dimension": "ecosystem", "claim": "Milaha and NEXX announced a warehouse-automation programme involving AI-enabled inventory and order processes.", "url": "https://www.milaha.com/sites/default/files/2026-02/Milaha-Annual-Report-2025-EN.pdf", "originalSourceUrl": "https://www.milaha.com/sites/default/files/2026-02/Milaha-Annual-Report-2025-EN.pdf", "sourceTitle": "Milaha Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Milaha", "sourceType": "corporate-disclosure", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF page 16, Advancing AI and logistics collaboration", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Qatar Navigation Q.P.S.C. (Milaha), group; cited logistics-centre integration. Commitment/integration language; no evidence of fully operational AI components. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific partner programme targets internal warehouse AI uses.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "4e069b0e207e523962f059c0e1ced75e5f058ca7fd83891f606c8ccb6d7851b2", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-023", "companyId": "milaha", "sourceId": "L-S12", "dimension": "ecosystem", "claim": "Milaha announced Google Cloud collaboration for data analytics and generative-AI applications.", "url": "https://milaha.com/en/news-and-media/milaha-enhances-data-ai-solutions-google-cloud", "originalSourceUrl": "https://milaha.com/en/news-and-media/milaha-enhances-data-ai-solutions-google-cloud", "sourceTitle": "Milaha enhances data and AI solutions with Google Cloud", "corroboratingSourceIds": [], "publisher": "Milaha", "sourceType": "corporate-disclosure", "publicationDate": "2024-11-24", "accessedDate": "2026-10-10", "locator": "Data and AI collaboration paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Qatar Navigation Q.P.S.C. (Milaha), group; cited logistics-centre integration. Does not independently demonstrate deployment; overlaps the ecosystem dimension already supported. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named partner and internal data/AI application programme.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "6b9d8cc128cad64bc8f0e9a1691b004e8e9a434e95b277e91b51d81bf39946ec", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "LOG-024", "companyId": "milaha", "sourceId": "L-S12", "dimension": "workforce", "claim": "The collaboration includes a planned employee Skill Boost programme.", "url": "https://milaha.com/en/news-and-media/milaha-enhances-data-ai-solutions-google-cloud", "originalSourceUrl": "https://milaha.com/en/news-and-media/milaha-enhances-data-ai-solutions-google-cloud", "sourceTitle": "Milaha enhances data and AI solutions with Google Cloud", "corroboratingSourceIds": [], "publisher": "Milaha", "sourceType": "corporate-disclosure", "publicationDate": "2024-11-24", "accessedDate": "2026-10-10", "locator": "Skill Boost programme paragraph", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Qatar Navigation Q.P.S.C. (Milaha), group; cited logistics-centre integration. Generic cloud/digital training cannot establish an augmented-AI workforce. Application: excluded-non-specific-training. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "full-source-read", "rationale": "Excluded: the retrieved passage does not establish AI-specific employee curriculum or completed participation. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "fa7af41880f65bb9892fe5faefa3f04a3387d78c75a657b4ebfdf54b549132d6", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 1, "workforce": null, "ecosystem": 1, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 2, "score": 7, "coverage": 2, "rank": 8, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 3, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 2, "latestSourceDate": "2024-11-24", "lineage": [ { "dimension": "ai-core", "level": 1, "winningClaimIds": [ "LOG-021" ], "contribution": 3.7037 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "LOG-022", "LOG-023" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.milaha.com/sites/default/files/2026-02/Milaha-Annual-Report-2025-EN.pdf", "scoreWithoutSource": 3.7 }, { "url": "https://milaha.com/en/news-and-media/milaha-enhances-data-ai-solutions-google-cloud", "scoreWithoutSource": 7.41 } ], "scenarioResults": [ { "scenario": "baseline", "score": 7.41, "rank": 8 }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 7.41, "rank": 8 }, { "scenario": "ai-core-0.5", "score": 5.88, "rank": 8 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 8 }, { "scenario": "workforce-0.5", "score": 7.84, "rank": 8 }, { "scenario": "workforce-2", "score": 6.67, "rank": 8 }, { "scenario": "ecosystem-0.5", "score": 5.88, "rank": 9 }, { "scenario": "ecosystem-2", "score": 10.0, "rank": 8 }, { "scenario": "interfaces-0.5", "score": 7.84, "rank": 7 }, { "scenario": "interfaces-2", "score": 6.67, "rank": 8 }, { "scenario": "lineage-0.5", "score": 7.84, "rank": 8 }, { "scenario": "lineage-2", "score": 6.67, "rank": 8 }, { "scenario": "experimentation-0.5", "score": 7.84, "rank": 8 }, { "scenario": "experimentation-2", "score": 6.67, "rank": 8 }, { "scenario": "autonomy-0.5", "score": 7.84, "rank": 8 }, { "scenario": "autonomy-2", "score": 6.67, "rank": 8 }, { "scenario": "knowledge-0.5", "score": 7.84, "rank": 8 }, { "scenario": "knowledge-2", "score": 6.67, "rank": 8 }, { "scenario": "governance-0.5", "score": 7.84, "rank": 8 }, { "scenario": "governance-2", "score": 6.67, "rank": 8 } ], "scenarioRankRange": [ 7, 9 ], "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "bahrain-airport-company", "name": "Bahrain Airport Company", "country": "Bahrain", "sector": "Aviation & Logistics", "scope": "Bahrain Airport Company W.L.L., operator of Bahrain International Airport and subsidiary of Gulf Air Group", "scopeLimitations": "Overlaps the Gulf Air Group parent. Parent/airline AI evidence is not inherited by this subsidiary. Retrieved airport material does not establish a qualifying operating case.", "cluster": "gulf-air-family", "evidence": [ { "id": "LOG-037", "companyId": "bahrain-airport-company", "sourceId": "L-S23", "dimension": "ai-core", "claim": "Indexed town-hall coverage mentions ambitions involving AI alongside other digital technologies.", "url": "https://www.bahrainairport.bh/corporate/news/bahrain-airport-company-bac-hosts-journey-to-excellence-town-hall-and-launches-the-tamayouz-programme", "originalSourceUrl": "https://www.bahrainairport.bh/corporate/news/bahrain-airport-company-bac-hosts-journey-to-excellence-town-hall-and-launches-the-tamayouz-programme", "sourceTitle": "BAC hosts Journey to Excellence town hall and launches Tamayouz programme", "corroboratingSourceIds": [], "publisher": "Bahrain Airport Company", "sourceType": "corporate-disclosure", "publicationDate": "2024-03-05", "accessedDate": "2026-10-10", "locator": "Search-indexed article passage; full page unavailable", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Bahrain Airport Company W.L.L., operator of Bahrain International Airport and subsidiary of Gulf Air Group. No exact full-source passage read. Parent and airline implementations are not attributed to BAC. Application: excluded-inaccessible-and-generic. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "snippet-only", "rationale": "Excluded: inaccessible source and generic ambition do not establish a named internal AI practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "c47decc68f0a6adc6191afe78ae4e852ed5a71f1f800c3ca071c3d863eb9e2aa", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "emirates-nbd", "name": "Emirates NBD", "country": "UAE", "sector": "Banking", "scope": "Emirates NBD Group; named platform/process scope", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "emirates-nbd", "evidence": [ { "id": "bank-emirates-nbd-01", "companyId": "emirates-nbd", "sourceId": "enbd-pca", "dimension": "ai-core", "claim": "Emirates NBD describes PCA analysing customer calls into structured service-improvement intelligence.", "url": "https://www.emiratesnbd.com/en/innovation/insights/turning-millions-of-customer-conversations-into-better-banking-experiences-with-ai", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/insights/turning-millions-of-customer-conversations-into-better-banking-experiences-with-ai", "sourceTitle": "Turning millions of customer conversations into better banking experiences with AI", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-operating-case", "publicationDate": "2026-07", "accessedDate": "2026-10-10", "locator": "PCA operating description; English language scope", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. English support currently described; Arabic is future. Six million annual calls is addressable volume, not verified processed count. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "PCA page explicitly describes implemented call-analysis workflow and structured themes. English currently supported; Arabic described as future.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "9d2bc21f2e032bca6f2f2363310de395833f57611f405936a7afc8142c3289b0", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-02", "companyId": "emirates-nbd", "sourceId": "enbd-pca", "dimension": "knowledge", "claim": "PCA turns unstructured conversations into recurring themes, customer intent and operational issues shared across teams.", "url": "https://www.emiratesnbd.com/en/innovation/insights/turning-millions-of-customer-conversations-into-better-banking-experiences-with-ai", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/insights/turning-millions-of-customer-conversations-into-better-banking-experiences-with-ai", "sourceTitle": "Turning millions of customer conversations into better banking experiences with AI", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-operating-case", "publicationDate": "2026-07", "accessedDate": "2026-10-10", "locator": "PCA operating description; English language scope", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. No retrieval quality or decision trace sample supplied. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "PCA converts unstructured calls into reusable themes and intent for operational teams; distinguish shared intelligence from independently validated knowledge quality.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "1bf317736966c7a79087cab71f68506b2e8e6686085e34041365c88eee1ca96f", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-03", "companyId": "emirates-nbd", "sourceId": "enbd-leap", "dimension": "lineage", "claim": "Leap supplies agent audit logging, observability, prompt management and model-governance controls.", "url": "https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework", "sourceTitle": "Why banks should build their own Agentic AI Framework", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-operating-case", "publicationDate": "2026-07", "accessedDate": "2026-10-10", "locator": "Leap platform controls; enterprise abstraction", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. Engineering article is bank-authored; audit effectiveness and actual agent coverage not independently established. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Leap page describes default audit, observability and model controls on internal platform. This is a company assertion, not an inspected decision log.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "1d24c7a5e8873b9c792d18ea1c9d6a27bbf0fe2e9c747d2e6ea380b929a5ba1a", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-04", "companyId": "emirates-nbd", "sourceId": "enbd-leap", "dimension": "experimentation", "claim": "Leap provides evaluation and cost/token controls through a shared agent engineering platform.", "url": "https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework", "sourceTitle": "Why banks should build their own Agentic AI Framework", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-operating-case", "publicationDate": "2026-07", "accessedDate": "2026-10-10", "locator": "Leap platform controls; enterprise abstraction", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. No test results, kill thresholds or failed experiments disclosed. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Evaluation and cost/token controls are specified as inherited platform mechanisms. No evidence of a controlled experiment, quality threshold or causal impact.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "3dc022e6483b0da7c56dd2380a934ad10469ba13d1bf5a94115ec229716792cc", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-05", "companyId": "emirates-nbd", "sourceId": "enbd-leap", "dimension": "ecosystem", "claim": "Leap separates business applications from underlying model and orchestration technologies.", "url": "https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework", "sourceTitle": "Why banks should build their own Agentic AI Framework", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-operating-case", "publicationDate": "2026-07", "accessedDate": "2026-10-10", "locator": "Leap platform controls; enterprise abstraction", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. Architecture claim does not establish switching cost or successful provider change. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Leap abstraction separates business applications from model/orchestration changes. Architecture mechanism reported; actual switching performance untested.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "9bc40ad5bab3caee6564ac12c7d6fe1b38c042702528330102875feea503a2e8", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-06", "companyId": "emirates-nbd", "sourceId": "enbd-people", "dimension": "workforce", "claim": "Role-based AI pathways combine approved tools, prompt libraries, masterclasses and communities of practice.", "url": "https://www.emiratesnbd.com/en/innovation/people", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/people", "sourceTitle": "People & Capability", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-program-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Learning built into everyday work; learning pathways", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. No validated competence outcomes or comparable per-role adoption denominators. Application: internal-operating-practice.", "access": "Indexed full page excerpt | Review: full-primary-page-or-document-read", "rationale": "Full People page describes role-specific pathways and supporting practice infrastructure. No demonstrated competence or adoption denominator.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "5fead8590ab37433a5ea9192c2f57b2ce455096ca55bd973daf1b0819a71c6ba", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-07", "companyId": "emirates-nbd", "sourceId": "enbd-action", "dimension": "governance", "claim": "Published AI principles assign accountable human owners and constrain use to approved purposes.", "url": "https://www.emiratesnbd.com/en/innovation/innovation-in-action", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/innovation-in-action", "sourceTitle": "Innovation in Action", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-policy-and-program-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Our AI principles; Our AI journey", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. Policy/operating declaration; no independent effectiveness test. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Full principles page states use-case accountability and approved purpose controls apply. Do not present policy statement as independently audited enforcement.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "66b327977738d55baffb797afef5ce029cd4f5240bcebc8a8635d9b1ba2a9623", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-08", "companyId": "emirates-nbd", "sourceId": "enbd-action", "dimension": "autonomy", "claim": "AI oversight is matched to decision impact and failures are described as falling back safely.", "url": "https://www.emiratesnbd.com/en/innovation/innovation-in-action", "originalSourceUrl": "https://www.emiratesnbd.com/en/innovation/innovation-in-action", "sourceTitle": "Innovation in Action", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-policy-and-program-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Our AI principles; Our AI journey", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. Does not establish autonomous authority or successful fallback testing. Application: internal-operating-practice.", "access": "Full page or document text retrieved", "rationale": "Declared operating boundaries but no named delegated decision or threshold.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "7e1668773b8db3227134896556ec2d07b2bbc6e046d999235e9ef3003eabddfa", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-emirates-nbd-09", "companyId": "emirates-nbd", "sourceId": "enbd-ar25", "dimension": "interfaces", "claim": "Project Trinity embedded generative AI in high-volume processes including voice operations.", "url": "https://cdn.emiratesnbd.com/en/assets/file/ir/annual_report_2025.pdf", "originalSourceUrl": "https://cdn.emiratesnbd.com/en/assets/file/ir/annual_report_2025.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Emirates NBD", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF spread p.32 (zero-based31), printed pp.58–59, Project Trinity", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Emirates NBD Group; named platform/process scope. Detailed user interaction and scope are unspecified; report outcome kept separately. Application: internal-operating-practice.", "access": "Full PDF text retrieved in second pass; 132 PDF pages/spreads. Technology, HR, compliance and Project Trinity AI passages reviewed. | Review: full-primary-page-or-document-read", "rationale": "Annual report fully readable: PDF index31, Project Trinity, generative AI embedded in high-volume voice processes. Preserve separate unaudited impact claim.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "fb669ddfc884c5ed17a1884714df8cd17e6ce2de7980d80c15b1c9db393456d4", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 3, "interfaces": 2, "lineage": 3, "experimentation": 3, "autonomy": 2, "knowledge": 3, "governance": 3 }, "points": 25, "score": 93, "coverage": 9, "rank": 1, "sourceCount": 5, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "enbd-pca", "enbd-leap", "enbd-people", "enbd-action", "enbd-ar25" ], "secondPass": { "date": "2026-10-10", "sources": [ "enbd-ar25", "enbd-pca", "enbd-leap", "enbd-people", "enbd-action" ], "passages": [ "Annual2025 AI/artificial intelligence matches: strategy/HR (PDF zero18), ProjectTrinity(zero31), technology/digital(zero32), compliance(zero34).", "Current innovation pages read for PCA, Leap architecture, workforce pathway and responsibleAI principles." ], "access": "Full annual PDF text recovered on second pass. No system inspection." } }, "audit": { "eligibleClaims": 9, "excludedClaims": 0, "eligibleSourceUrls": 5, "undatedEligibleClaims": 3, "latestSourceDate": "2026-07", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "bank-emirates-nbd-01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "bank-emirates-nbd-06" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 3, "winningClaimIds": [ "bank-emirates-nbd-05" ], "contribution": 11.1111 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "bank-emirates-nbd-09" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": 3, "winningClaimIds": [ "bank-emirates-nbd-03" ], "contribution": 11.1111 }, { "dimension": "experimentation", "level": 3, "winningClaimIds": [ "bank-emirates-nbd-04" ], "contribution": 11.1111 }, { "dimension": "autonomy", "level": 2, "winningClaimIds": [ "bank-emirates-nbd-08" ], "contribution": 7.4074 }, { "dimension": "knowledge", "level": 3, "winningClaimIds": [ "bank-emirates-nbd-02" ], "contribution": 11.1111 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "bank-emirates-nbd-07" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework", "scoreWithoutSource": 59.26 }, { "url": "https://www.emiratesnbd.com/en/innovation/insights/turning-millions-of-customer-conversations-into-better-banking-experiences-with-ai", "scoreWithoutSource": 70.37 }, { "url": "https://www.emiratesnbd.com/en/innovation/innovation-in-action", "scoreWithoutSource": 74.07 }, { "url": "https://www.emiratesnbd.com/en/innovation/people", "scoreWithoutSource": 81.48 }, { "url": "https://cdn.emiratesnbd.com/en/assets/file/ir/annual_report_2025.pdf", "scoreWithoutSource": 85.19 } ], "scenarioResults": [ { "scenario": "baseline", "score": 92.59, "rank": 1 }, { "scenario": "implemented-only", "score": 92.59, "rank": 1 }, { "scenario": "detail-cap", "score": 66.67, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 92.16, "rank": 1 }, { "scenario": "ai-core-2", "score": 93.33, "rank": 1 }, { "scenario": "workforce-0.5", "score": 92.16, "rank": 1 }, { "scenario": "workforce-2", "score": 93.33, "rank": 1 }, { "scenario": "ecosystem-0.5", "score": 92.16, "rank": 1 }, { "scenario": "ecosystem-2", "score": 93.33, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 94.12, "rank": 1 }, { "scenario": "interfaces-2", "score": 90.0, "rank": 1 }, { "scenario": "lineage-0.5", "score": 92.16, "rank": 1 }, { "scenario": "lineage-2", "score": 93.33, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 92.16, "rank": 1 }, { "scenario": "experimentation-2", "score": 93.33, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 94.12, "rank": 1 }, { "scenario": "autonomy-2", "score": 90.0, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 92.16, "rank": 1 }, { "scenario": "knowledge-2", "score": 93.33, "rank": 1 }, { "scenario": "governance-0.5", "score": 92.16, "rank": 1 }, { "scenario": "governance-2", "score": 93.33, "rank": 1 } ], "scenarioRankRange": [ 1, 1 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "fab", "name": "First Abu Dhabi Bank", "country": "UAE", "sector": "Banking", "scope": "FAB Group; individual use-case scope not presumed universal", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "fab", "evidence": [ { "id": "bank-fab-01", "companyId": "fab", "sourceId": "fab-feb", "dimension": "ai-core", "claim": "FAB reports AI agents embedded in departmental routines and over 30 agentic cases progressing in several business functions.", "url": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "originalSourceUrl": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "sourceTitle": "FAB accelerates enterprise AI adoption through group-wide innovation and talent enablement", "corroboratingSourceIds": [], "publisher": "First Abu Dhabi Bank", "sourceType": "company-announcement", "publicationDate": "2026-02-17", "accessedDate": "2026-10-10", "locator": "AI Innovation Hub; structured data; responsible AI framework", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: FAB Group; individual use-case scope not presumed universal. Thirty cases are progressing, not thirty verified live cases. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-source-read", "rationale": "The primary page explicitly places agents in routine departmental operations. Retain level 2; the separate count above 30 denotes progressing cases, not confirmed live deployments.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "a701349f118e08edaec3398e7cd1c39092f2c62166a93df3ae1aa9b038dbfc5f", "review": { "decision": "retain", "record": "research/depth-banking-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-fab-02", "companyId": "fab", "sourceId": "fab-feb", "dimension": "governance", "claim": "FAB says all AI initiatives operate under a group Responsible AI framework and the Innovation Hub supplies governance.", "url": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "originalSourceUrl": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "sourceTitle": "FAB accelerates enterprise AI adoption through group-wide innovation and talent enablement", "corroboratingSourceIds": [], "publisher": "First Abu Dhabi Bank", "sourceType": "company-announcement", "publicationDate": "2026-02-17", "accessedDate": "2026-10-10", "locator": "AI Innovation Hub; structured data; responsible AI framework", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: FAB Group; individual use-case scope not presumed universal. Control details, authority thresholds and effectiveness tests absent. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-source-read", "rationale": "A named AI-specific Responsible AI framework applies to all initiatives; the Hub supplies governance. Retain level 2 for declared operating ownership and framework, not tested controls or effectiveness.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "8697b0bdf5e32d0753f37e63abe73ea3854e790d46292f67e865d14d05dac7d8", "review": { "decision": "retain", "record": "research/depth-banking-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-fab-03", "companyId": "fab", "sourceId": "fab-feb", "dimension": "knowledge", "claim": "FAB reports integrating over 90% of structured data into an enterprise platform with an agentic layer.", "url": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "originalSourceUrl": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "sourceTitle": "FAB accelerates enterprise AI adoption through group-wide innovation and talent enablement", "corroboratingSourceIds": [], "publisher": "First Abu Dhabi Bank", "sourceType": "company-announcement", "publicationDate": "2026-02-17", "accessedDate": "2026-10-10", "locator": "AI Innovation Hub; structured data; responsible AI framework", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: FAB Group; individual use-case scope not presumed universal. Denominator definition and data quality not disclosed; integration does not prove provenance. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-primary-page-or-document-read", "rationale": "17 February 2026 announcement reports data coverage above 90% and agentic layer. Structured-data denominator and quality not independently checked.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "e9613033462bd66bf88dc1149c303806737e1244e2537449a36e0e46e508e397", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-fab-04", "companyId": "fab", "sourceId": "fab-feb", "dimension": "workforce", "claim": "FAB reports applied-AI employee training alongside specialist recruitment and upskilling.", "url": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "originalSourceUrl": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "sourceTitle": "FAB accelerates enterprise AI adoption through group-wide innovation and talent enablement", "corroboratingSourceIds": [], "publisher": "First Abu Dhabi Bank", "sourceType": "company-announcement", "publicationDate": "2026-02-17", "accessedDate": "2026-10-10", "locator": "AI Innovation Hub; structured data; responsible AI framework", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: FAB Group; individual use-case scope not presumed universal. Participation, completion and competency denominators absent. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-source-read", "rationale": "The page explicitly reports completed applied-AI staff training, continued upskilling and specialist recruitment. Retain level 2; participation and proficiency denominators remain absent.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "95a68f5a2c6471642c7c451f8a6f77f3d5b520085e0a1e054c4480b5013f094f", "review": { "decision": "retain", "record": "research/depth-banking-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-fab-05", "companyId": "fab", "sourceId": "fab-hub", "dimension": "interfaces", "claim": "FAB's case index describes DANA as an AI assistant through which employees and managers access HR support.", "url": "https://www.bankfab.com/en-ae/about-fab/innovation-and-ai", "originalSourceUrl": "https://www.bankfab.com/en-ae/about-fab/innovation-and-ai", "sourceTitle": "AI in Banking: Driving Innovation & Growth", "corroboratingSourceIds": [], "publisher": "First Abu Dhabi Bank", "sourceType": "company-program-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Practical applications; 6 October 2026 DANA case summary", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: FAB Group; individual use-case scope not presumed universal. Underlying one-page PDF could not be text-verified; no utilisation or resolution metrics retained. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Full official AI hub lists DANA employee/manager HR assistant dated 6 October 2026, before cutoff. Brief source-level implementation evidence only.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "eb5caf39dbe91d3dfe6330a1adf716aa06f6661f5aa74ead60e2813a54d9c9bb", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-fab-06", "companyId": "fab", "sourceId": "fab-feb", "dimension": "experimentation", "claim": "The AI Innovation Hub operates a structured use-case pipeline with reusable capabilities.", "url": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "originalSourceUrl": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "sourceTitle": "FAB accelerates enterprise AI adoption through group-wide innovation and talent enablement", "corroboratingSourceIds": [], "publisher": "First Abu Dhabi Bank", "sourceType": "company-announcement", "publicationDate": "2026-02-17", "accessedDate": "2026-10-10", "locator": "AI Innovation Hub; structured data; responsible AI framework", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: FAB Group; individual use-case scope not presumed universal. Does not reveal controlled trials or stop/scale criteria. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-source-read", "rationale": "The Hub is described as operating a structured use-case pipeline with reusable capabilities. Retain level 2 for an implemented development pipeline; no controlled evaluation or stop/scale rule is established.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "405715c742243ffa9ff5191b48de488a53ba1861964713530ab8a067be9510da", "review": { "decision": "retain", "record": "research/depth-banking-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 2, "workforce": 2, "ecosystem": null, "interfaces": 2, "lineage": null, "experimentation": 2, "autonomy": null, "knowledge": 3, "governance": 2 }, "points": 13, "score": 48, "coverage": 6, "rank": 2, "sourceCount": 2, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "fab-feb", "fab-hub" ], "secondPass": { "date": "2026-10-10", "sources": [ "fab-feb", "fab-hub" ], "passages": [ "February2026 enterprise announcement: deployed agents versus >30progressing cases, commonplatform, AIHub, responsibleAI, training.", "October2026 innovation hub case index including DANA. Case PDF extraction insufficient." ], "access": "Annual2025 index and AGM link found; direct annual PDF and both official download links failed. Image-only DANA case and garbled AI-at-scale PDF not used beyond readable index. Nine-theme review is limited to retrieved operating announcements." } }, "audit": { "eligibleClaims": 6, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 1, "latestSourceDate": "2026-02-17", "lineage": [ { "dimension": "ai-core", "level": 2, "winningClaimIds": [ "bank-fab-01" ], "contribution": 7.4074 }, { "dimension": "workforce", "level": 2, "winningClaimIds": [ "bank-fab-04" ], "contribution": 7.4074 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "bank-fab-05" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 2, "winningClaimIds": [ "bank-fab-06" ], "contribution": 7.4074 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 3, "winningClaimIds": [ "bank-fab-03" ], "contribution": 11.1111 }, { "dimension": "governance", "level": 2, "winningClaimIds": [ "bank-fab-02" ], "contribution": 7.4074 } ], "singleSourceRemoval": [ { "url": "https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption", "scoreWithoutSource": 7.41 }, { "url": "https://www.bankfab.com/en-ae/about-fab/innovation-and-ai", "scoreWithoutSource": 40.74 } ], "scenarioResults": [ { "scenario": "baseline", "score": 48.15, "rank": 2 }, { "scenario": "implemented-only", "score": 48.15, "rank": 2 }, { "scenario": "detail-cap", "score": 44.44, "rank": 2 }, { "scenario": "ai-core-0.5", "score": 47.06, "rank": 2 }, { "scenario": "ai-core-2", "score": 50.0, "rank": 3 }, { "scenario": "workforce-0.5", "score": 47.06, "rank": 3 }, { "scenario": "workforce-2", "score": 50.0, "rank": 2 }, { "scenario": "ecosystem-0.5", "score": 50.98, "rank": 2 }, { "scenario": "ecosystem-2", "score": 43.33, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 47.06, "rank": 2 }, { "scenario": "interfaces-2", "score": 50.0, "rank": 2 }, { "scenario": "lineage-0.5", "score": 50.98, "rank": 2 }, { "scenario": "lineage-2", "score": 43.33, "rank": 3 }, { "scenario": "experimentation-0.5", "score": 47.06, "rank": 3 }, { "scenario": "experimentation-2", "score": 50.0, "rank": 2 }, { "scenario": "autonomy-0.5", "score": 50.98, "rank": 2 }, { "scenario": "autonomy-2", "score": 43.33, "rank": 3 }, { "scenario": "knowledge-0.5", "score": 45.1, "rank": 2 }, { "scenario": "knowledge-2", "score": 53.33, "rank": 2 }, { "scenario": "governance-0.5", "score": 47.06, "rank": 2 }, { "scenario": "governance-2", "score": 50.0, "rank": 3 } ], "scenarioRankRange": [ 2, 3 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "kfh", "name": "Kuwait Finance House", "country": "Kuwait", "sector": "Banking", "scope": "KFH Kuwait bank; no inference to subsidiaries from local deployments", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "kfh", "evidence": [ { "id": "bank-kfh-01", "companyId": "kfh", "sourceId": "kfh-ai", "dimension": "knowledge", "claim": "ShariaBot answers employee questions using specific fatwas from KFH's Sharia Supervisory Board; ComplianceBot supports regulatory queries.", "url": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html", "originalSourceUrl": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html", "sourceTitle": "KFH Reinforces Banking Leadership with Forward-Looking AI Initiatives", "corroboratingSourceIds": [], "publisher": "Kuwait Finance House", "sourceType": "company-announcement", "publicationDate": "2025-08-30", "accessedDate": "2026-10-10", "locator": "Specialized virtual assistant; integrating human expertise", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: KFH Kuwait bank; no inference to subsidiaries from local deployments. No sampled citation fidelity, currency-control or answer quality audit. Application: internal-operating-practice.", "access": "Full indexed article; direct rendered retrieval omitted body | Review: full-primary-page-or-document-read", "rationale": "Full article body now readable: ShariaBot references specific board fatwas and ComplianceBot supports regulatory enquiries. Retain provenance as company assertion.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "12030fb0ec094f37ede64da09a2fc61cc595144187ae7f05277fd272ab646c8a", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-kfh-02", "companyId": "kfh", "sourceId": "kfh-ai", "dimension": "interfaces", "claim": "KFH says it deployed specialised employee assistants and an AI Copilot for daily work.", "url": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html", "originalSourceUrl": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html", "sourceTitle": "KFH Reinforces Banking Leadership with Forward-Looking AI Initiatives", "corroboratingSourceIds": [], "publisher": "Kuwait Finance House", "sourceType": "company-announcement", "publicationDate": "2025-08-30", "accessedDate": "2026-10-10", "locator": "Specialized virtual assistant; integrating human expertise", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: KFH Kuwait bank; no inference to subsidiaries from local deployments. No employee coverage or active-use data. Application: internal-operating-practice.", "access": "Full indexed article; direct rendered retrieval omitted body | Review: full-source-read", "rationale": "The primary body states introduction of employee Copilot and deployment of specialist assistants. Retain implemented internal interface level 2; coverage and utilization remain unspecified.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "29322f56a15059e974b4da81deb7b48ed3019a91dc2617e306fe247da3173ce8", "review": { "decision": "retain", "record": "research/depth-banking-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-kfh-03", "companyId": "kfh", "sourceId": "kfh-ai", "dimension": "lineage", "claim": "ShariaBot references specific KFH supervisory-board fatwas in employee guidance.", "url": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html", "originalSourceUrl": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html", "sourceTitle": "KFH Reinforces Banking Leadership with Forward-Looking AI Initiatives", "corroboratingSourceIds": [], "publisher": "Kuwait Finance House", "sourceType": "company-announcement", "publicationDate": "2025-08-30", "accessedDate": "2026-10-10", "locator": "Specialized virtual assistant; integrating human expertise", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: KFH Kuwait bank; no inference to subsidiaries from local deployments. Source linking alone does not reconstruct decision history or model version. Application: internal-operating-practice.", "access": "Full indexed article; direct rendered retrieval omitted body | Review: full-source-read", "rationale": "The primary body explicitly identifies references to particular supervisory-board fatwas in employee guidance. Retain level 2 narrowly for input-source traceability; this does not establish a reconstructable decision history, model versions, approvals or audit logs.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "3616b9b8002e6b6679dc49e000c0bdcf06202cccb1524d258393a6e7f91e7b56", "review": { "decision": "retain", "record": "research/depth-banking-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-kfh-04", "companyId": "kfh", "sourceId": "kfh-calls", "dimension": "ai-core", "claim": "KFH's contact centre uses speech analytics and automated call-quality assessment against predefined standards.", "url": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Adopts-Advanced-AI-Technologies-to-Enhance-Customer-Experience.html", "originalSourceUrl": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Adopts-Advanced-AI-Technologies-to-Enhance-Customer-Experience.html", "sourceTitle": "KFH Adopts Advanced AI Technologies to Enhance Customer Experience", "corroboratingSourceIds": [], "publisher": "Kuwait Finance House", "sourceType": "company-operating-case", "publicationDate": "2025-07-27", "accessedDate": "2026-10-10", "locator": "Speech Analytics; Automated Quality Management", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: KFH Kuwait bank; no inference to subsidiaries from local deployments. Bank claims all-call coverage; no validation of sentiment or quality ratings. Application: internal-operating-practice.", "access": "Full indexed article | Review: full-primary-page-or-document-read", "rationale": "27 July2025 article describes deployed speech analytics and automated quality evaluation against pre-approved standards; defined internal service workflow.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "b4b9c83159c762f25050a1d9f6d4bf03b393bdcad54acb94bd101712d3220b76", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-kfh-05", "companyId": "kfh", "sourceId": "kfh-esg25", "dimension": "governance", "claim": "KFH's transformation team participates in the Artificial Intelligence Technology Program Steering structure.", "url": "https://www.kfh.com/dam/jcr:6d21db7a-84cb-4487-b3f2-7bcf64d4c382/KFH%2BSustainability%2BReport%2B2025.pdf", "originalSourceUrl": "https://www.kfh.com/dam/jcr:6d21db7a-84cb-4487-b3f2-7bcf64d4c382/KFH%2BSustainability%2BReport%2B2025.pdf", "sourceTitle": "KFH Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "Kuwait Finance House", "sourceType": "company-sustainability-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Printed p.95; PDF p.94 (zero-based93)", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: KFH Kuwait bank; no inference to subsidiaries from local deployments. Mandate, decision thresholds and effectiveness not independently tested. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Sustainability PDF index93 specifies DTI membership in AI Technology Program Steering and oversight/reporting responsibility.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "050a328beb6af3f3cea4a062c750b9b9aa5882a3b9be836723c99b8d697e1a1a", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-kfh-06", "companyId": "kfh", "sourceId": "kfh-esg25", "dimension": "ai-core", "claim": "KFH reports deploying AI-enabled endpoint threat detection and response.", "url": "https://www.kfh.com/dam/jcr:6d21db7a-84cb-4487-b3f2-7bcf64d4c382/KFH%2BSustainability%2BReport%2B2025.pdf", "originalSourceUrl": "https://www.kfh.com/dam/jcr:6d21db7a-84cb-4487-b3f2-7bcf64d4c382/KFH%2BSustainability%2BReport%2B2025.pdf", "sourceTitle": "KFH Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "Kuwait Finance House", "sourceType": "company-sustainability-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Printed p.60; PDF p.59 (zero-based58)", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: KFH Kuwait bank; no inference to subsidiaries from local deployments. No threat-detection performance or human escalation detail. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Sustainability PDF index58 identifies deployed AI endpoint threat detection/response. Concrete internal security mechanism; not governance of AI models.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "a9bb62c5ebfc97d22baa9414445798457af1cd200d2a7fb4bcff4ded2e2d1051", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": null, "interfaces": 2, "lineage": 2, "experimentation": null, "autonomy": null, "knowledge": 3, "governance": 3 }, "points": 13, "score": 48, "coverage": 5, "rank": 2, "sourceCount": 3, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "kfh-ai", "kfh-calls", "kfh-esg25" ], "secondPass": { "date": "2026-10-10", "sources": [ "kfh-ai", "kfh-calls", "kfh-esg25" ], "passages": [ "Sustainability2025 AI/artificial intelligence matches: cybersecurityprinted60(PDF59), digitaltransformationprinted91(PDF90), governanceprinted95(PDF94), customerfraudawareness(PDF126).", "Internal assistants and fullcall quality assessment releases. Generic digital experiments not scored as AIexperimentation." ], "access": "Full sustainability PDF text; release indexed primary text. Broader group subsidiaries not inferred." } }, "audit": { "eligibleClaims": 6, "excludedClaims": 0, "eligibleSourceUrls": 3, "undatedEligibleClaims": 0, "latestSourceDate": "2026", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "bank-kfh-04", "bank-kfh-06" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "bank-kfh-02" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": 2, "winningClaimIds": [ "bank-kfh-03" ], "contribution": 7.4074 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 3, "winningClaimIds": [ "bank-kfh-01" ], "contribution": 11.1111 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "bank-kfh-05" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html", "scoreWithoutSource": 22.22 }, { "url": "https://www.kfh.com/dam/jcr:6d21db7a-84cb-4487-b3f2-7bcf64d4c382/KFH%2BSustainability%2BReport%2B2025.pdf", "scoreWithoutSource": 37.04 }, { "url": "https://www.kfh.com/en/home/Personal/news/2025/KFH-Adopts-Advanced-AI-Technologies-to-Enhance-Customer-Experience.html", "scoreWithoutSource": 48.15 } ], "scenarioResults": [ { "scenario": "baseline", "score": 48.15, "rank": 2 }, { "scenario": "implemented-only", "score": 48.15, "rank": 2 }, { "scenario": "detail-cap", "score": 37.04, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 45.1, "rank": 3 }, { "scenario": "ai-core-2", "score": 53.33, "rank": 2 }, { "scenario": "workforce-0.5", "score": 50.98, "rank": 2 }, { "scenario": "workforce-2", "score": 43.33, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 50.98, "rank": 2 }, { "scenario": "ecosystem-2", "score": 43.33, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 47.06, "rank": 2 }, { "scenario": "interfaces-2", "score": 50.0, "rank": 2 }, { "scenario": "lineage-0.5", "score": 47.06, "rank": 3 }, { "scenario": "lineage-2", "score": 50.0, "rank": 2 }, { "scenario": "experimentation-0.5", "score": 50.98, "rank": 2 }, { "scenario": "experimentation-2", "score": 43.33, "rank": 3 }, { "scenario": "autonomy-0.5", "score": 50.98, "rank": 2 }, { "scenario": "autonomy-2", "score": 43.33, "rank": 3 }, { "scenario": "knowledge-0.5", "score": 45.1, "rank": 2 }, { "scenario": "knowledge-2", "score": 53.33, "rank": 2 }, { "scenario": "governance-0.5", "score": 45.1, "rank": 4 }, { "scenario": "governance-2", "score": 53.33, "rank": 2 } ], "scenarioRankRange": [ 2, 5 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "cbq", "name": "Commercial Bank of Qatar", "country": "Qatar", "sector": "Banking", "scope": "Commercial Bank (Qatar); no inference to Alternatif Bank/associates", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "cbq", "evidence": [ { "id": "bank-cbq-01", "companyId": "cbq", "sourceId": "cbq-ar25", "dimension": "ai-core", "claim": "Commercial Bank reports deployed AI character recognition in onboarding, trade-finance and loan-document workflows.", "url": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf", "originalSourceUrl": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf", "sourceTitle": "Commercial Bank Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Commercial Bank of Qatar", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.37, Data & Artificial Intelligence", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Commercial Bank (Qatar); no inference to Alternatif Bank/associates. Savings claim lacks amount and baseline. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Annual report PDF index36 describes deployed recognition across onboarding, trade and loan documents by internal Data Lab.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "a52001730399fa54bd768e912e61cef471771d8877eadd95cd86456e02a2e385", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-cbq-02", "companyId": "cbq", "sourceId": "cbq-ar25", "dimension": "interfaces", "claim": "Smart Email uses language models and retrieval to draft responses for a substantial share of customer emails.", "url": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf", "originalSourceUrl": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf", "sourceTitle": "Commercial Bank Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Commercial Bank of Qatar", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.37, Data & Artificial Intelligence", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Commercial Bank (Qatar); no inference to Alternatif Bank/associates. Share is qualitative; accuracy and human review controls unspecified. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Same section specifies deployed Smart Email response suggestions using LLM and retrieval-augmented generation. Qualitative coverage, not a measured fraction.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "a27cc7f3c18005944268a0e9c3546e65c3b921874dcaa02c81d4e78b902db8c5", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-cbq-03", "companyId": "cbq", "sourceId": "cbq-ar25", "dimension": "knowledge", "claim": "Commercial Bank runs language models on its own GPU cluster and brought its enterprise lakehouse live in 2025.", "url": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf", "originalSourceUrl": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf", "sourceTitle": "Commercial Bank Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Commercial Bank of Qatar", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.37, Data & Artificial Intelligence", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Commercial Bank (Qatar); no inference to Alternatif Bank/associates. Data integration does not itself prove lineage; agentic capabilities are future. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Own GPU/running models and live lakehouse are documented enabling infrastructure. They do not specify maintaining organizational knowledge. Root may separately code deployed Smart Email retrieval as knowledge evidence with an explicit revised claim.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ba4bdb16c1a647bc9da43581942edc101ce45b7f686d16642ac6f0f8bd2c9c3a", "review": { "decision": "downgrade", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-cbq-04", "companyId": "cbq", "sourceId": "cbq-ip25", "dimension": "autonomy", "claim": "The investor presentation identifies AI-optimised credit-limit increases as live.", "url": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/financial-reports-en/2025/q4/investor-presentation---fy-2025.pdf", "originalSourceUrl": "https://cbq.qa/-/media/project/cbq/cbqwebsite/documents/financial-reports-en/2025/q4/investor-presentation---fy-2025.pdf", "sourceTitle": "Investor Presentation FY 2025", "corroboratingSourceIds": [], "publisher": "Commercial Bank of Qatar", "sourceType": "company-investor-presentation", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Strategy slide 9, Accelerating our AI Journey", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Commercial Bank (Qatar); no inference to Alternatif Bank/associates. Credit authority limits, human overrides and exception controls not described. Application: internal-operating-practice.", "access": "Full indexed slide text | Review: full-primary-page-or-document-read | Readable corroboration: https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/financial-reports-en/2025/q4/investor-presentation---fy-2025.pdf", "rationale": "Investor slide PDF index8 explicitly labels credit-limit automation live. Readable www.cbq.qa alternate; bare domain retrieval failed.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "55f2e457486c12bce606bb2f56a2ba79e90f1e9796bdf12457ed7fb14bc24093", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-cbq-05", "companyId": "cbq", "sourceId": "cbq-news", "dimension": "workforce", "claim": "Commercial Bank describes internal data-science, engineering and governance teams supporting its AI strategy.", "url": "https://www.cbq.com.qa/en/about-us/news/list/commercial-bank-awarded-most-innovative-use-of-ai-technology-by-international-finance", "originalSourceUrl": "https://www.cbq.com.qa/en/about-us/news/list/commercial-bank-awarded-most-innovative-use-of-ai-technology-by-international-finance", "sourceTitle": "Commercial Bank Awarded Most Innovative Use of AI Technology by International Finance", "corroboratingSourceIds": [], "publisher": "Commercial Bank of Qatar", "sourceType": "company-announcement", "publicationDate": "2025-07-03", "accessedDate": "2026-10-10", "locator": "Data Strategy and internal teams paragraphs", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Commercial Bank (Qatar); no inference to Alternatif Bank/associates. No AI role counts or verified workforce redesign. Application: internal-operating-practice.", "access": "Full indexed article; award itself excluded from coding | Review: full-primary-page-or-document-read", "rationale": "Readable 3 July 2025 article describes internal data-science, engineering and governance teams. Award is not evidence of capability.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "0a02004b18014b4b6abb79e8cd05700b2620ab8758a7e15e022bcd4422d3bf65", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 2, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": 2, "knowledge": 2, "governance": null }, "points": 12, "score": 44, "coverage": 5, "rank": 4, "sourceCount": 3, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "cbq-ar25", "cbq-ip25", "cbq-news" ], "secondPass": { "date": "2026-10-10", "sources": [ "cbq-ar25", "cbq-ip25", "cbq-news" ], "passages": [ "Annual2025 AI matches: strategy(PDF24), retail data/insights(PDF31), digital/AI(PDF37).", "FY2025 investor slide9 credit-limit AI; official July2025 release internaldataAIteams." ], "access": "Full official annual and investor PDF text. Futureagentic capabilities kept separate from deployed tools." } }, "audit": { "eligibleClaims": 5, "excludedClaims": 0, "eligibleSourceUrls": 3, "undatedEligibleClaims": 0, "latestSourceDate": "2026", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "bank-cbq-01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 2, "winningClaimIds": [ "bank-cbq-05" ], "contribution": 7.4074 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "bank-cbq-02" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": 2, "winningClaimIds": [ "bank-cbq-04" ], "contribution": 7.4074 }, { "dimension": "knowledge", "level": 2, "winningClaimIds": [ "bank-cbq-03" ], "contribution": 7.4074 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf", "scoreWithoutSource": 14.81 }, { "url": "https://www.cbq.com.qa/en/about-us/news/list/commercial-bank-awarded-most-innovative-use-of-ai-technology-by-international-finance", "scoreWithoutSource": 37.04 }, { "url": "https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/financial-reports-en/2025/q4/investor-presentation---fy-2025.pdf", "scoreWithoutSource": 37.04 } ], "scenarioResults": [ { "scenario": "baseline", "score": 44.44, "rank": 4 }, { "scenario": "implemented-only", "score": 44.44, "rank": 4 }, { "scenario": "detail-cap", "score": 37.04, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 41.18, "rank": 4 }, { "scenario": "ai-core-2", "score": 50.0, "rank": 3 }, { "scenario": "workforce-0.5", "score": 43.14, "rank": 4 }, { "scenario": "workforce-2", "score": 46.67, "rank": 3 }, { "scenario": "ecosystem-0.5", "score": 47.06, "rank": 4 }, { "scenario": "ecosystem-2", "score": 40.0, "rank": 5 }, { "scenario": "interfaces-0.5", "score": 41.18, "rank": 4 }, { "scenario": "interfaces-2", "score": 50.0, "rank": 2 }, { "scenario": "lineage-0.5", "score": 47.06, "rank": 3 }, { "scenario": "lineage-2", "score": 40.0, "rank": 4 }, { "scenario": "experimentation-0.5", "score": 47.06, "rank": 3 }, { "scenario": "experimentation-2", "score": 40.0, "rank": 4 }, { "scenario": "autonomy-0.5", "score": 43.14, "rank": 4 }, { "scenario": "autonomy-2", "score": 46.67, "rank": 2 }, { "scenario": "knowledge-0.5", "score": 43.14, "rank": 4 }, { "scenario": "knowledge-2", "score": 46.67, "rank": 4 }, { "scenario": "governance-0.5", "score": 47.06, "rank": 2 }, { "scenario": "governance-2", "score": 40.0, "rank": 6 } ], "scenarioRankRange": [ 2, 6 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "nbk", "name": "National Bank of Kuwait", "country": "Kuwait", "sector": "Banking", "scope": "NBK Kuwait disclosures; all international entities not presumed covered", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "nbk", "evidence": [ { "id": "bank-nbk-01", "companyId": "nbk", "sourceId": "nbk-copilot", "dimension": "workforce", "claim": "NBK announced Copilot availability to employees across all divisions.", "url": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-launches-microsoft-copilot-across-all-divisions--pioneering-ai-integration-and-empowering-employees-", "originalSourceUrl": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-launches-microsoft-copilot-across-all-divisions--pioneering-ai-integration-and-empowering-employees-", "sourceTitle": "NBK Launches Microsoft Copilot Across All Divisions", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-announcement", "publicationDate": "2025-07-27", "accessedDate": "2026-10-10", "locator": "Rollout and employee workspace paragraphs", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: NBK Kuwait disclosures; all international entities not presumed covered. Availability is not active use or productivity; international coverage not independently established. Application: internal-operating-practice.", "access": "Full indexed article | Review: full-primary-page-or-document-read", "rationale": "Readable article dated 27 July2025 explicitly reports Copilot rollout to employees across divisions. Availability does not imply active use or benefit.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "5a5097b0a90160ce2bfbb069be3cf6dec5dd4315d9b63ee951d25f3e4409e30f", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-02", "companyId": "nbk", "sourceId": "nbk-copilot", "dimension": "interfaces", "claim": "Copilot is embedded in the employee workspace for drafting, summarisation and analytical assistance.", "url": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-launches-microsoft-copilot-across-all-divisions--pioneering-ai-integration-and-empowering-employees-", "originalSourceUrl": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-launches-microsoft-copilot-across-all-divisions--pioneering-ai-integration-and-empowering-employees-", "sourceTitle": "NBK Launches Microsoft Copilot Across All Divisions", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-announcement", "publicationDate": "2025-07-27", "accessedDate": "2026-10-10", "locator": "Rollout and employee workspace paragraphs", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: NBK Kuwait disclosures; all international entities not presumed covered. No end-to-end process redesign or verified effect. Application: internal-operating-practice.", "access": "Full indexed article | Review: full-source-read", "rationale": "The primary article explicitly reports completed Copilot rollout and embedding in staff workspaces for drafting, data analysis and summarization. Retain implemented-interface level 2; do not infer active-use rates or measured benefit.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "c4e39d35b9c16eab9083f0dc38d44dd5ff141193ae5e3c8ae25b5501324b8ccd", "review": { "decision": "retain", "record": "research/depth-banking-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-03", "companyId": "nbk", "sourceId": "nbk-learning", "dimension": "workforce", "claim": "NBK launched an executive AI Leadership Programme with interactive awareness masterclasses.", "url": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?month=09&year=2025", "originalSourceUrl": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?month=09&year=2025", "sourceTitle": "NBK Organizes a Series of AI Mindset and Awareness Masterclasses", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-announcement", "publicationDate": "2025-09-14", "accessedDate": "2026-10-10", "locator": "Named article under September 2025 archive", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: NBK Kuwait disclosures; all international entities not presumed covered. Planned future workshops intermixed with launch; attendance and learning outcomes not disclosed. Application: internal-operating-practice. Exclusion: Complete readable source support not established; indexed-only evidence cannot score.", "access": "Full indexed article; archive URL contains multiple articles", "rationale": "Specific leadership programme. Exclusion: Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 2, "exclusionReasons": [ "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "fd82de0296ab7e070c02fa5c5e0618199a33442ef97414e949059cd92dbd11e2", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-04", "companyId": "nbk", "sourceId": "nbk-yearend", "dimension": "governance", "claim": "The AI Mindset Curriculum includes AI concepts and ethics through e-learning and workshops.", "url": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-closes-exceptional-2025-marked-by-unprecedented-achievements", "originalSourceUrl": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-closes-exceptional-2025-marked-by-unprecedented-achievements", "sourceTitle": "NBK Closes Exceptional 2025 Marked by Unprecedented Achievements", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-announcement", "publicationDate": "2025-12-25", "accessedDate": "2026-10-10", "locator": "AI Mindset Curriculum paragraph", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: NBK Kuwait disclosures; all international entities not presumed covered. Do not score governance from course content alone. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "Full indexed article", "rationale": "Ethics learning is evidence of training, not governance authority. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "18b7613cc31e99bc4b7602f4003aa192c531541982b2794f25ae3c03e351a262", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-05", "companyId": "nbk", "sourceId": "nbk-yearend", "dimension": "workforce", "claim": "NBK reports introducing an AI Mindset Curriculum combining e-learning and interactive workshops.", "url": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-closes-exceptional-2025-marked-by-unprecedented-achievements", "originalSourceUrl": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-closes-exceptional-2025-marked-by-unprecedented-achievements", "sourceTitle": "NBK Closes Exceptional 2025 Marked by Unprecedented Achievements", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-announcement", "publicationDate": "2025-12-25", "accessedDate": "2026-10-10", "locator": "AI Mindset Curriculum paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: NBK Kuwait disclosures; all international entities not presumed covered. All-training hours elsewhere in article must not be labelled AI-training hours. Application: internal-operating-practice.", "access": "Full indexed article | Review: full-primary-page-or-document-read", "rationale": "Year-end article supports launched curriculum with e-learning and workshops, but no coverage or concrete assessment mechanism. Separate May2026 article in same dynamic page describes three role paths/progress tracking; use separate dated claim if coding3.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "d8cd41ed1fc944c304360fa98d229b564342a96cfc01463322167a574332f9fa", "review": { "decision": "downgrade", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-06", "companyId": "nbk", "sourceId": "nbk-ar25", "dimension": "ai-core", "claim": "NBK reports using machine learning in lead targeting, personalized engagement and contact-centre operations.", "url": "https://www.nbk.com/dam/jcr:73b1509e-5280-4ef3-a29a-392c1b565299/nbk-annual-report-2025-e.pdf", "originalSourceUrl": "https://www.nbk.com/dam/jcr:73b1509e-5280-4ef3-a29a-392c1b565299/nbk-annual-report-2025-e.pdf", "sourceTitle": "NBK Annual Report 2025", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-annual-report", "publicationDate": "2026-02-22", "accessedDate": "2026-10-10", "locator": "Printed p.37; PDF p.38 (zero-based37)", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: NBK Kuwait disclosures; all international entities not presumed covered. No operating mechanism, coverage or evaluation results disclosed in this passage. Application: internal-operating-practice.", "access": "Full page or document text retrieved", "rationale": "Explicit implementation in named operating activities.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "702a65d760628d53af361807ef3284667812f412bfd596596184694bfda81e9c", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-07", "companyId": "nbk", "sourceId": "nbk-esg25", "dimension": "governance", "claim": "Copilot Web excludes confidential data; document upload and download are disabled. AI risk assessment and model-accountability frameworks remain on the roadmap.", "url": "https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf", "originalSourceUrl": "https://www.nbk.com/dam/jcr%3Ae9a25c2e-890b-4ce2-b3ee-0f9fa4c3999f/nbk-sustainability-report-2025.pdf", "sourceTitle": "NBK Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-sustainability-report", "publicationDate": "2026-04-30", "accessedDate": "2026-10-10", "locator": "Printed p.192, Responsible AI & Data Governance; mirror PDF zero-based96", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: NBK Kuwait operations unless report states otherwise. Control effectiveness not tested; planned broader framework is not treated as implemented. Application: internal-operating-practice.", "access": "Official indexed report and issuer-authored full PDF mirror read; official full PDF retrieval failed (size). Mirror https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf | Review: full-company-authored-mirror-read-not-hashmatched | Readable corroboration: https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf", "rationale": "Argaam mirror PDF index96 directly specifies confidential-data prohibition and disabled document transfer; broader model accountability framework remains planned. Official URL failed; mirror not hash-matched.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "57b5a5748f8c57b742dfbf78011d82edf4940a022cd0aaf57a9261900b17ebda", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-08", "companyId": "nbk", "sourceId": "nbk-esg25", "dimension": "workforce", "claim": "About one-third of total staff were consistent Copilot Web users in 2025.", "url": "https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf", "originalSourceUrl": "https://www.nbk.com/dam/jcr%3Ae9a25c2e-890b-4ce2-b3ee-0f9fa4c3999f/nbk-sustainability-report-2025.pdf", "sourceTitle": "NBK Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-sustainability-report", "publicationDate": "2026-04-30", "accessedDate": "2026-10-10", "locator": "Printed p.192, Responsible AI & Data Governance; mirror PDF zero-based96", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: NBK Kuwait operations unless report states otherwise. Consistent user definition and denominator count not supplied; no productivity inference. Application: internal-operating-practice.", "access": "Official indexed report and issuer-authored full PDF mirror read; official full PDF retrieval failed (size). Mirror https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf | Review: full-company-authored-mirror-read-not-hashmatched | Readable corroboration: https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf", "rationale": "Same readable mirror states approximately one-third consistent Copilot Web users in2025. Usage definition absent; no productivity inference. Default report boundary Kuwait operations.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "4e2808e062fbe847a132b034722e6fe8427ffa95fdf90839a4d6fb77923a5847", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbk-09", "companyId": "nbk", "sourceId": "nbk-esg25", "dimension": "interfaces", "claim": "Copilot Web is available to all employees for general knowledge and non-confidential productivity tasks.", "url": "https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf", "originalSourceUrl": "https://www.nbk.com/dam/jcr%3Ae9a25c2e-890b-4ce2-b3ee-0f9fa4c3999f/nbk-sustainability-report-2025.pdf", "sourceTitle": "NBK Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "National Bank of Kuwait", "sourceType": "company-sustainability-report", "publicationDate": "2026-04-30", "accessedDate": "2026-10-10", "locator": "Printed p.192, Responsible AI & Data Governance; mirror PDF zero-based96", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: NBK Kuwait operations unless report states otherwise. Not evidence of access to internal institutional knowledge. Application: internal-operating-practice.", "access": "Official indexed report and issuer-authored full PDF mirror read; official full PDF retrieval failed (size). Mirror https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf | Review: full-company-authored-mirror-read-not-hashmatched | Readable corroboration: https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf", "rationale": "Same readable mirror specifies availability and permitted general-knowledge/non-confidential task boundaries. Scores employee interface, not internal institutional knowledge access.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "393888ff8448b13e3dd1e6d9bedc8d2a5261d8af6dd77a94324c43881c3126b1", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 2, "workforce": 3, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": 3 }, "points": 11, "score": 41, "coverage": 4, "rank": 5, "sourceCount": 5, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "nbk-copilot", "nbk-learning", "nbk-yearend", "nbk-ar25", "nbk-esg25" ], "secondPass": { "date": "2026-10-10", "sources": [ "nbk-ar25", "nbk-esg25", "nbk-copilot", "nbk-learning", "nbk-yearend" ], "passages": [ "Annual2025 printedp37 AIworkflows; p62 workforce; governancepp85–101 checked, general controls not treated as AIcontrols.", "Sustainability2025 printed134–135 Copilotdesign; printed192 responsibleAI restrictions/consistentusage/roadmap; scopeprinted7.", "Copilotrollout, executivelearning and year-endcurriculum releases." ], "access": "Annual official PDF text. Sustainability official indexed pages plus complete issuer-authored Argaam mirror; official largePDF fetch failed. Kuwait boundary retained." } }, "audit": { "eligibleClaims": 7, "excludedClaims": 2, "eligibleSourceUrls": 4, "undatedEligibleClaims": 0, "latestSourceDate": "2026-04-30", "lineage": [ { "dimension": "ai-core", "level": 2, "winningClaimIds": [ "bank-nbk-06" ], "contribution": 7.4074 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "bank-nbk-01", "bank-nbk-08" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "bank-nbk-09" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "bank-nbk-07" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf", "scoreWithoutSource": 25.93 }, { "url": "https://www.nbk.com/dam/jcr:73b1509e-5280-4ef3-a29a-392c1b565299/nbk-annual-report-2025-e.pdf", "scoreWithoutSource": 33.33 }, { "url": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-closes-exceptional-2025-marked-by-unprecedented-achievements", "scoreWithoutSource": 40.74 }, { "url": "https://www.nbk.com/kuwait/news-and-insights/media-relations/news.html?news=nbk-launches-microsoft-copilot-across-all-divisions--pioneering-ai-integration-and-empowering-employees-", "scoreWithoutSource": 40.74 } ], "scenarioResults": [ { "scenario": "baseline", "score": 40.74, "rank": 5 }, { "scenario": "implemented-only", "score": 40.74, "rank": 5 }, { "scenario": "detail-cap", "score": 29.63, "rank": 5 }, { "scenario": "ai-core-0.5", "score": 39.22, "rank": 5 }, { "scenario": "ai-core-2", "score": 43.33, "rank": 5 }, { "scenario": "workforce-0.5", "score": 37.25, "rank": 5 }, { "scenario": "workforce-2", "score": 46.67, "rank": 3 }, { "scenario": "ecosystem-0.5", "score": 43.14, "rank": 5 }, { "scenario": "ecosystem-2", "score": 36.67, "rank": 6 }, { "scenario": "interfaces-0.5", "score": 37.25, "rank": 6 }, { "scenario": "interfaces-2", "score": 46.67, "rank": 5 }, { "scenario": "lineage-0.5", "score": 43.14, "rank": 5 }, { "scenario": "lineage-2", "score": 36.67, "rank": 5 }, { "scenario": "experimentation-0.5", "score": 43.14, "rank": 5 }, { "scenario": "experimentation-2", "score": 36.67, "rank": 5 }, { "scenario": "autonomy-0.5", "score": 43.14, "rank": 4 }, { "scenario": "autonomy-2", "score": 36.67, "rank": 5 }, { "scenario": "knowledge-0.5", "score": 43.14, "rank": 4 }, { "scenario": "knowledge-2", "score": 36.67, "rank": 5 }, { "scenario": "governance-0.5", "score": 37.25, "rank": 5 }, { "scenario": "governance-2", "score": 46.67, "rank": 4 } ], "scenarioRankRange": [ 3, 6 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "al-rajhi", "name": "Al Rajhi Bank", "country": "KSA", "sector": "Banking", "scope": "Al Rajhi Bank; group context distinguished in claims", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "al-rajhi", "evidence": [ { "id": "bank-al-rajhi-01", "companyId": "al-rajhi", "sourceId": "ar-innovation", "dimension": "ai-core", "claim": "Al Rajhi reports embedding machine learning in credit scoring, fraud detection and customer lifecycle decision engines.", "url": "https://www.alrajhibank.com.sa/ir25/customer_engagement_and_responsible_banking/innovation_and_product_development.html", "originalSourceUrl": "https://www.alrajhibank.com.sa/ir25/customer_engagement_and_responsible_banking/innovation_and_product_development.html", "sourceTitle": "Integrated Annual Report 2025: Innovation and product development", "corroboratingSourceIds": [], "publisher": "Al Rajhi Bank", "sourceType": "company-annual-report-html", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Group-wide insights and real-time marketing", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Al Rajhi Bank; group context distinguished in claims. No deployment coverage, independent accuracy assessment or outcomes attributable to AI. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-primary-page-or-document-read", "rationale": "Annual report explicitly embeds ML in named decision engines; no inference of independently verified performance.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "58e3a4ce99d2d2cc8c9fa582100fbab4483527b2d8c91ff7e4e332c907361385", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-al-rajhi-02", "companyId": "al-rajhi", "sourceId": "ar-innovation", "dimension": "ecosystem", "claim": "Customer profiles combine bank, subsidiary, fintech and consented open-banking data for analytics and engagement.", "url": "https://www.alrajhibank.com.sa/ir25/customer_engagement_and_responsible_banking/innovation_and_product_development.html", "originalSourceUrl": "https://www.alrajhibank.com.sa/ir25/customer_engagement_and_responsible_banking/innovation_and_product_development.html", "sourceTitle": "Integrated Annual Report 2025: Innovation and product development", "corroboratingSourceIds": [], "publisher": "Al Rajhi Bank", "sourceType": "company-annual-report-html", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Group-wide insights and real-time marketing", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Al Rajhi Bank; group context distinguished in claims. Data flow rights described at high level; no implementation audit or partner-level controls. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-primary-page-or-document-read", "rationale": "Same AI analytics section specifies partner and consented open-banking data integration; supports ecosystem mechanism, not universal governance.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "053bf66ef7fc5b4f528f1588002aedefa7641108f4efb69010a0b2b10eee6963", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-al-rajhi-03", "companyId": "al-rajhi", "sourceId": "ar-learning", "dimension": "workforce", "claim": "The 2024 report describes over 10,000 employees using the AI-enabled METAKKED learning application.", "url": "https://www.alrajhibank.com.sa/ir24/cultivating_a_future_forward_workforce/learning_and_development_at_alrajhi_group.html", "originalSourceUrl": "https://www.alrajhibank.com.sa/ir24/cultivating_a_future_forward_workforce/learning_and_development_at_alrajhi_group.html", "sourceTitle": "Integrated Annual Report 2024: Learning and development at alrajhi group", "corroboratingSourceIds": [], "publisher": "Al Rajhi Bank", "sourceType": "company-annual-report-html", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "METAKKED paragraph", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: Al Rajhi Bank; group context distinguished in claims. AI's learning function and denominator not explained; 2024 observation requires refresh. Application: internal-operating-practice.", "access": "Indexed relevant passage | Review: full-primary-page-or-document-read", "rationale": "Full 2024 page reports AI-embedded learning application and over 10,000 engaged employees. This measures tool exposure, not AI competence.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "707adb9b8af3c29fc7f4e0064ca0d505060498871ad25a5a1ff7ae36b2035135", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-al-rajhi-04", "companyId": "al-rajhi", "sourceId": "ar-risk", "dimension": "governance", "claim": "A three-year cybersecurity initiative includes secure AI adoption.", "url": "https://www.alrajhibank.com.sa/ir25/corporate_governance/risk_management.html", "originalSourceUrl": "https://www.alrajhibank.com.sa/ir25/corporate_governance/risk_management.html", "sourceTitle": "Integrated Annual Report 2025: Risk management", "corroboratingSourceIds": [], "publisher": "Al Rajhi Bank", "sourceType": "company-annual-report-html", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Cybersecurity risk; Information Technology Risk", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Al Rajhi Bank; group context distinguished in claims. Not proof that AI-specific controls were operating in 2025. Application: internal-operating-practice. Exclusion: Complete readable source support not established; indexed-only evidence cannot score.", "access": "Full indexed page text", "rationale": "Explicit forward programme in a named governance domain. Exclusion: Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 1, "exclusionReasons": [ "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "058b543ba4b28ad15b34b9c9f4867c508e91a6182c0e98c9c68e0a3cbeefc558", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-al-rajhi-05", "companyId": "al-rajhi", "sourceId": "ar-learning25", "dimension": "workforce", "claim": "The AI-powered METAKKED learning application engaged 4,102 employees in 2025.", "url": "https://www.alrajhibank.com.sa/ir25/cultivating_a_future_forward_workforce/learning_and_development.html", "originalSourceUrl": "https://www.alrajhibank.com.sa/ir25/cultivating_a_future_forward_workforce/learning_and_development.html", "sourceTitle": "Al Rajhi Integrated Annual Report 2025 — Learning and Development", "corroboratingSourceIds": [], "publisher": "Al Rajhi Bank", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Digital learning platforms, METAKKED", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Al Rajhi Bank; group context distinguished in claims. AI-enabled learning is not proof that employees can develop or govern AI. 2024 and 2025 participation definitions may differ. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Readable 2025 learning page specifies AI gamification and 4,102 engaged employees. Latest-period figure supersedes use of 2024 count as current; no competence inference.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "79c2bd11a4af16b4bd967210ea4eac623b3daf51b5adea4f4bacd7cd99cc67e0", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-al-rajhi-06", "companyId": "al-rajhi", "sourceId": "ar-privacy25", "dimension": "governance", "claim": "Al Rajhi states its formal AI governance framework remains under development.", "url": "https://www.alrajhibank.com.sa/ir25/corporate_governance/data_privacy_and_information_management.html", "originalSourceUrl": "https://www.alrajhibank.com.sa/ir25/corporate_governance/data_privacy_and_information_management.html", "sourceTitle": "Al Rajhi Integrated Annual Report 2025 — Data privacy and information management", "corroboratingSourceIds": [], "publisher": "Al Rajhi Bank", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Opening section, paragraph beginning Both internal and external privacy policies", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Al Rajhi Bank; group context distinguished in claims. General privacy assessments do not establish AI-specific control operation. Application: internal-operating-practice.", "access": "Full page or document text retrieved", "rationale": "Specific AI framework in development; not deployed governance.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "67e420da18c7fad1daeba0f57884b1fbce63a4308841ec1560ca505ac2edd12c", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-al-rajhi-07", "companyId": "al-rajhi", "sourceId": "ar-shared25", "dimension": "ai-core", "claim": "AI model outputs were integrated into customer communication channels for Next Best Offer campaigns.", "url": "https://www.alrajhibank.com.sa/ir25/business_in_perspective/shared_services_group.html", "originalSourceUrl": "https://www.alrajhibank.com.sa/ir25/business_in_perspective/shared_services_group.html", "sourceTitle": "Al Rajhi Integrated Annual Report 2025 — Shared services group", "corroboratingSourceIds": [], "publisher": "Al Rajhi Bank", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Strategic initiative 04: Building a world-class data platform", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Al Rajhi Bank; group context distinguished in claims. No causal uplift, error rate or human override details. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Readable shared-services section explicitly integrates model outputs into named Next Best Offer customer-communication workflow.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "928b3043412bd09fbe7751e58d06d8ddaa7db912e1e6aa2b8e19903c0d7f1577", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 3, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": 1 }, "points": 10, "score": 37, "coverage": 4, "rank": 6, "sourceCount": 6, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "ar-innovation", "ar-risk", "ar-learning", "ar-learning25", "ar-privacy25", "ar-shared25" ], "secondPass": { "date": "2026-10-10", "sources": [ "ar-innovation", "ar-risk", "ar-learning", "ar-learning25", "ar-privacy25", "ar-shared25" ], "passages": [ "2025 innovation and product development (models, data ecosystem); risk management (secureAI strategy); learning (METAKKED); data privacy (AI framework in development); shared services IT initiatives 1–6 (GPU, NBO, general IT testing excluded).", "2025 talent attraction page checked; general650213training hours not coded as AI hours; 2024METAKKED retained historical comparison." ], "access": "Full official interactive annual sections read; did not read entire report page by page." } }, "audit": { "eligibleClaims": 6, "excludedClaims": 1, "eligibleSourceUrls": 5, "undatedEligibleClaims": 0, "latestSourceDate": "2026", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "bank-al-rajhi-01", "bank-al-rajhi-07" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "bank-al-rajhi-03", "bank-al-rajhi-05" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 3, "winningClaimIds": [ "bank-al-rajhi-02" ], "contribution": 11.1111 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 1, "winningClaimIds": [ "bank-al-rajhi-06" ], "contribution": 3.7037 } ], "singleSourceRemoval": [ { "url": "https://www.alrajhibank.com.sa/ir25/customer_engagement_and_responsible_banking/innovation_and_product_development.html", "scoreWithoutSource": 25.93 }, { "url": "https://www.alrajhibank.com.sa/ir25/corporate_governance/data_privacy_and_information_management.html", "scoreWithoutSource": 33.33 }, { "url": "https://www.alrajhibank.com.sa/ir24/cultivating_a_future_forward_workforce/learning_and_development_at_alrajhi_group.html", "scoreWithoutSource": 37.04 }, { "url": "https://www.alrajhibank.com.sa/ir25/business_in_perspective/shared_services_group.html", "scoreWithoutSource": 37.04 }, { "url": "https://www.alrajhibank.com.sa/ir25/cultivating_a_future_forward_workforce/learning_and_development.html", "scoreWithoutSource": 37.04 } ], "scenarioResults": [ { "scenario": "baseline", "score": 37.04, "rank": 6 }, { "scenario": "implemented-only", "score": 33.33, "rank": 6 }, { "scenario": "detail-cap", "score": 25.93, "rank": 7 }, { "scenario": "ai-core-0.5", "score": 33.33, "rank": 7 }, { "scenario": "ai-core-2", "score": 43.33, "rank": 5 }, { "scenario": "workforce-0.5", "score": 33.33, "rank": 6 }, { "scenario": "workforce-2", "score": 43.33, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 33.33, "rank": 7 }, { "scenario": "ecosystem-2", "score": 43.33, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 39.22, "rank": 5 }, { "scenario": "interfaces-2", "score": 33.33, "rank": 7 }, { "scenario": "lineage-0.5", "score": 39.22, "rank": 6 }, { "scenario": "lineage-2", "score": 33.33, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 39.22, "rank": 6 }, { "scenario": "experimentation-2", "score": 33.33, "rank": 7 }, { "scenario": "autonomy-0.5", "score": 39.22, "rank": 6 }, { "scenario": "autonomy-2", "score": 33.33, "rank": 6 }, { "scenario": "knowledge-0.5", "score": 39.22, "rank": 6 }, { "scenario": "knowledge-2", "score": 33.33, "rank": 6 }, { "scenario": "governance-0.5", "score": 37.25, "rank": 5 }, { "scenario": "governance-2", "score": 36.67, "rank": 7 } ], "scenarioRankRange": [ 2, 7 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "qnb", "name": "QNB", "country": "Qatar", "sector": "Banking", "scope": "QNB Group; excludes Egypt/Turkey-only announcements unless identified", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "qnb", "evidence": [ { "id": "bank-qnb-01", "companyId": "qnb", "sourceId": "qnb-gov", "dimension": "governance", "claim": "QNB identifies an AI Governance Committee and cross-functional Responsible AI Review Forum for material use cases.", "url": "https://www.qnb.com/sites/qnb/qnbqatar/document/en/DataAIGovernanceStatement-", "originalSourceUrl": "https://www.qnb.com/sites/qnb/qnbqatar/document/en/DataAIGovernanceStatement-", "sourceTitle": "QNB Group AI & Data Governance Statement", "corroboratingSourceIds": [], "publisher": "QNB", "sourceType": "company-policy", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.4 (printed p.3), Governance Approach", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: QNB Group; excludes Egypt/Turkey-only announcements unless identified. Statement proves disclosure of controls, not their operating effectiveness. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Readable five-page governance statement specifies AI committee and cross-functional material-use review forum with pre-implementation risk review.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "a71b5cf22409adf818ab7749938078e52764ef4e1c10635c63893e07ebbd5128", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-qnb-02", "companyId": "qnb", "sourceId": "qnb-ar25", "dimension": "experimentation", "claim": "QNB reports six AI use cases in progress under Falcon and a governance framework for scaling.", "url": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "originalSourceUrl": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "sourceTitle": "QNB Annual Report 2025", "corroboratingSourceIds": [], "publisher": "QNB", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.31 (zero-based30), Falcon", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: QNB Group; excludes Egypt/Turkey-only announcements unless identified. In progress does not mean deployed; no result for these cases. Application: internal-operating-practice.", "access": "Bank-authored annual report on Hong Kong Monetary Authority register", "rationale": "Named programme and case count; operational status unclear.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "2fb28cd153718cbdcf267891e95bede2be464b422733982d6ca0d56f3663364b", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-qnb-03", "companyId": "qnb", "sourceId": "qnb-ar25", "dimension": "governance", "claim": "QNB expanded model-risk policy to include artificial intelligence during 2025.", "url": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "originalSourceUrl": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "sourceTitle": "QNB Annual Report 2025", "corroboratingSourceIds": [], "publisher": "QNB", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.89 (zero-based88), model risk", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: QNB Group; excludes Egypt/Turkey-only announcements unless identified. Policy inclusion does not demonstrate AI adoption extent. Application: internal-operating-practice.", "access": "Bank-authored annual report on Hong Kong Monetary Authority register | Review: full-primary-page-or-document-read", "rationale": "Annual report PDF index88 says model-risk policy expanded to AI; that statement alone lacks concrete AI control mechanism. Other QNB governance claim can independently score3.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "41b7eeec87d94a969e932a127c502ff838a4c7b7c05456eb41d17ae15890dc1c", "review": { "decision": "downgrade", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-qnb-04", "companyId": "qnb", "sourceId": "qnb-q2", "dimension": "ecosystem", "claim": "QNB and CMU-Q established a business-innovation centre covering AI, analytics and practical learning.", "url": "https://www.qnb.com/sites/qnb/qnbglobal/en/ennew22072026news", "originalSourceUrl": "https://qnb.com/sites/qnb/qnbglobal/en/ennew22072026news", "sourceTitle": "QNB Group Maintains Disciplined Execution Across Key Strategic Priorities in Q2", "corroboratingSourceIds": [], "publisher": "QNB", "sourceType": "company-announcement", "publicationDate": "2026-07-22", "accessedDate": "2026-10-10", "locator": "QNB Center for Business Innovation", "status": "deployed", "level": 1, "eligible": true, "limitation": "Claim scope: QNB Group; excludes Egypt/Turkey-only announcements unless identified. Centre activity does not demonstrate internal bank AI workflow deployment. Application: internal-operating-practice.", "access": "Full indexed page text | Review: full-primary-page-or-document-read | Readable corroboration: https://www.qnb.com/sites/qnb/qnbglobal/en/ennew22072026news", "rationale": "Exact full passage says partnered to establish a center focused on AI and other subjects; no deployed internal AI practice. Specific commitment supports1, consistently with BBK center.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "2bf5bc75c0acc6b55a9100508a055be17967efe96fee05d65c149c5b9a82b0d0", "review": { "decision": "downgrade", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-qnb-05", "companyId": "qnb", "sourceId": "qnb-gov", "dimension": "lineage", "claim": "QNB reports a group Data Governance Council while roles and quality standards continue to be formalised.", "url": "https://www.qnb.com/sites/qnb/qnbqatar/document/en/DataAIGovernanceStatement-", "originalSourceUrl": "https://www.qnb.com/sites/qnb/qnbqatar/document/en/DataAIGovernanceStatement-", "sourceTitle": "QNB Group AI & Data Governance Statement", "corroboratingSourceIds": [], "publisher": "QNB", "sourceType": "company-policy", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.4 (printed p.3), Governance Approach", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: QNB Group; excludes Egypt/Turkey-only announcements unless identified. Retain context, do not credit traceability. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "Full page or document text retrieved", "rationale": "Data governance alone is insufficient evidence of AI decision/source lineage. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "d1b9b51c1ef91b4fcdd77cfe2d859d4946e6c1a402a27ede6b74de2398effbd0", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-qnb-06", "companyId": "qnb", "sourceId": "qnb-ar25", "dimension": "interfaces", "claim": "QNB's internal audit team began incorporating Microsoft 365 Copilot into audit workflows.", "url": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "originalSourceUrl": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "sourceTitle": "QNB Annual Report 2025", "corroboratingSourceIds": [], "publisher": "QNB", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.120 (zero-based119), Internal audit", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: QNB Group; excludes Egypt/Turkey-only announcements unless identified. Early use; extent and effect not quantified. Application: internal-operating-practice.", "access": "Bank-authored annual report on Hong Kong Monetary Authority register", "rationale": "Named tool and actual use in a specified workflow.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "77d560a73ff75a86e887781c251427eae514c44bd6362c401057fa79ef6c1374", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-qnb-07", "companyId": "qnb", "sourceId": "qnb-ar25", "dimension": "workforce", "claim": "Mandatory risk training refreshed in 2025 included AI ethics.", "url": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "originalSourceUrl": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "sourceTitle": "QNB Annual Report 2025", "corroboratingSourceIds": [], "publisher": "QNB", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF p.83 (zero-based82), Risk culture", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: QNB Group; excludes Egypt/Turkey-only announcements unless identified. No AI-specific participant completion or competency measure. Application: internal-operating-practice.", "access": "Bank-authored annual report on Hong Kong Monetary Authority register | Review: full-primary-page-or-document-read", "rationale": "Annual report PDF index82 specifies mandatory training refreshed annually with AI ethics focus in2025. Concrete recurrence mechanism, not completion or competence evidence.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "59edfb14289a6f062a249d2ccbbc0350a6e399fcfae3e3e06e4ae0c9afb88e8e", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": 3, "ecosystem": 1, "interfaces": 2, "lineage": null, "experimentation": 1, "autonomy": null, "knowledge": null, "governance": 3 }, "points": 10, "score": 37, "coverage": 5, "rank": 6, "sourceCount": 3, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "qnb-ar25", "qnb-gov", "qnb-q2" ], "secondPass": { "date": "2026-10-10", "sources": [ "qnb-ar25", "qnb-gov", "qnb-q2" ], "passages": [ "Annual2025 AI/artificial intelligence/Copilot matches: Falcon(PDF31), employee enablement(PDF46), risk ethics(PDF83), model-risk inclusion(PDF89), FCCinitiatives(PDF114), privacy(PDF116), internal auditCopilot(PDF120).", "Governance statement printedp3 committee/forum/council; Q2innovationcentre." ], "access": "Full annual PDF text from regulator repository. General risk authority frameworks not inferred as AI authority." } }, "audit": { "eligibleClaims": 6, "excludedClaims": 1, "eligibleSourceUrls": 3, "undatedEligibleClaims": 0, "latestSourceDate": "2026-07-22", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "bank-qnb-07" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "bank-qnb-04" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "bank-qnb-06" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "bank-qnb-02" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "bank-qnb-01" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf", "scoreWithoutSource": 14.81 }, { "url": "https://www.qnb.com/sites/qnb/qnbglobal/en/ennew22072026news", "scoreWithoutSource": 33.33 }, { "url": "https://www.qnb.com/sites/qnb/qnbqatar/document/en/DataAIGovernanceStatement-", "scoreWithoutSource": 33.33 } ], "scenarioResults": [ { "scenario": "baseline", "score": 37.04, "rank": 6 }, { "scenario": "implemented-only", "score": 29.63, "rank": 7 }, { "scenario": "detail-cap", "score": 29.63, "rank": 5 }, { "scenario": "ai-core-0.5", "score": 39.22, "rank": 5 }, { "scenario": "ai-core-2", "score": 33.33, "rank": 7 }, { "scenario": "workforce-0.5", "score": 33.33, "rank": 6 }, { "scenario": "workforce-2", "score": 43.33, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 37.25, "rank": 6 }, { "scenario": "ecosystem-2", "score": 36.67, "rank": 6 }, { "scenario": "interfaces-0.5", "score": 35.29, "rank": 7 }, { "scenario": "interfaces-2", "score": 40.0, "rank": 6 }, { "scenario": "lineage-0.5", "score": 39.22, "rank": 6 }, { "scenario": "lineage-2", "score": 33.33, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 37.25, "rank": 7 }, { "scenario": "experimentation-2", "score": 36.67, "rank": 5 }, { "scenario": "autonomy-0.5", "score": 39.22, "rank": 6 }, { "scenario": "autonomy-2", "score": 33.33, "rank": 6 }, { "scenario": "knowledge-0.5", "score": 39.22, "rank": 6 }, { "scenario": "knowledge-2", "score": 33.33, "rank": 6 }, { "scenario": "governance-0.5", "score": 33.33, "rank": 7 }, { "scenario": "governance-2", "score": 43.33, "rank": 5 } ], "scenarioRankRange": [ 5, 7 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "bbk", "name": "Bank of Bahrain and Kuwait", "country": "Bahrain", "sector": "Banking", "scope": "BBK; no automatic inference to subsidiaries", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "bbk", "evidence": [ { "id": "bank-bbk-01", "companyId": "bbk", "sourceId": "bbk-array", "dimension": "ecosystem", "claim": "BBK launched an ARRAY Innovation Center with 30 Bahraini engineers and plans to deliver AI digital solutions.", "url": "https://www.bbkonline.com/bbk-partners-with-array-to-launch-innovation-center-hiring-bahraini-software-engineers-to-accelerate-digital-transformation/", "originalSourceUrl": "https://www.bbkonline.com/bbk-partners-with-array-to-launch-innovation-center-hiring-bahraini-software-engineers-to-accelerate-digital-transformation/", "sourceTitle": "BBK partners with ARRAY to launch Innovation Center", "corroboratingSourceIds": [], "publisher": "Bank of Bahrain and Kuwait", "sourceType": "company-announcement", "publicationDate": "2025-11-05", "accessedDate": "2026-10-10", "locator": "Center launch; 30 engineers; planned AI solutions", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: BBK; no automatic inference to subsidiaries. Center launch is evidenced; planned solutions are not coded as deployed. Application: internal-enabling-capability.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Center is launched and staffed, but AI solutions explicitly remain planned in agreement. Specific commitment supports1; staffing general digital development does not establish implemented AI practice.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "1c797e07fbb87143f8e27e30effdcfb27d8f4808533e9249f23a8758f5a0978c", "review": { "decision": "downgrade", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-bbk-02", "companyId": "bbk", "sourceId": "bbk-ar25", "dimension": "interfaces", "claim": "BBK reports adding AI features to its board meeting and documentation system.", "url": "https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf", "originalSourceUrl": "https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf", "sourceTitle": "BBK Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Bank of Bahrain and Kuwait", "sourceType": "company-annual-report", "publicationDate": "2026-04", "accessedDate": "2026-10-10", "locator": "Printed p.29; PDF spread p.16 (zero-based15)", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: BBK; no automatic inference to subsidiaries. Feature design, use and impact unspecified. Application: internal-operating-practice.", "access": "Full page or document text retrieved", "rationale": "Explicit implemented internal board interface.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "08c67befb05881905485bfb267f161bcf8eb39c782723c8746940ee13cf22e60", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-bbk-03", "companyId": "bbk", "sourceId": "bbk-ar25", "dimension": "governance", "claim": "A quarterly management committee tracks data/AI adoption alongside digital-service usage and innovation measures.", "url": "https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf", "originalSourceUrl": "https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf", "sourceTitle": "BBK Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Bank of Bahrain and Kuwait", "sourceType": "company-annual-report", "publicationDate": "2026-04", "accessedDate": "2026-10-10", "locator": "Printed p.54; PDF spread p.29 (zero-based28)", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: BBK; no automatic inference to subsidiaries. AI-specific authority and control tests not disclosed. Application: internal-operating-practice.", "access": "Full page or document text retrieved | Review: full-primary-page-or-document-read", "rationale": "Annual report PDF index28 management-committee table specifies quarterly tracking of Data & AI adoption progress. Oversight cadence supported, effectiveness untested.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "075d9d8cab7f16cf22ecf30facab842580db6069e5aec61918b9d80a714dfea0", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-bbk-04", "companyId": "bbk", "sourceId": "bbk-ar25", "dimension": "workforce", "claim": "The annual report records board AI training on 12 December 2025.", "url": "https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf", "originalSourceUrl": "https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf", "sourceTitle": "BBK Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Bank of Bahrain and Kuwait", "sourceType": "company-annual-report", "publicationDate": "2026-04", "accessedDate": "2026-10-10", "locator": "Printed p.38; PDF spread p.21 (zero-based20)", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: BBK; no automatic inference to subsidiaries. No competence or behavioural results. Application: internal-operating-practice.", "access": "Full page or document text retrieved", "rationale": "Named training with exact event date.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "9434fb9be81f21540eddda1a4d7b91ebee20e8e6d82c82cbd559494d8d49e44a", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-bbk-05", "companyId": "bbk", "sourceId": "bbk-training", "dimension": "workforce", "claim": "BBK's historic management-trainee programme included AI in a two-phase fintech curriculum.", "url": "https://www.bbkonline.com/bbk-launches-its-digital-management-trainee-program-in-fintech/", "originalSourceUrl": "https://www.bbkonline.com/bbk-launches-its-digital-management-trainee-program-in-fintech/", "sourceTitle": "BBK launches its Digital Management Trainee Program in FinTech", "corroboratingSourceIds": [], "publisher": "Bank of Bahrain and Kuwait", "sourceType": "company-program-page", "publicationDate": "2022-07", "accessedDate": "2026-10-10", "locator": "Two-phase programme and AI curriculum", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: BBK; no automatic inference to subsidiaries. 2022 contextual source; too old for current release coding. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "Indexed article; historic context only", "rationale": "Specific training design. Exclusion: Explicit extraction-stage exclusion retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 1, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "af5303c870400e51f8f77e08f98626de504c0b0bf631543ea8c586983cfa218f", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": 2, "ecosystem": 1, "interfaces": 2, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": 3 }, "points": 8, "score": 30, "coverage": 4, "rank": 8, "sourceCount": 3, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "bbk-ar25", "bbk-array", "bbk-training" ], "secondPass": { "date": "2026-10-10", "sources": [ "bbk-ar25", "bbk-array", "bbk-training" ], "passages": [ "Annual2025 AI/artificial matches: innovationcentre printed16–17, boarddocumentsprinted29, trainingprinted38, managementcommitteeprinted54.", "Centrelaunch actualengineercohort vs futureAI solutions; historical2022training excluded currentcredit." ], "access": "Full official annual text and centre announcement. Genericautomation not credited." } }, "audit": { "eligibleClaims": 4, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 0, "latestSourceDate": "2026-04", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": 2, "winningClaimIds": [ "bank-bbk-04" ], "contribution": 7.4074 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "bank-bbk-01" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "bank-bbk-02" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "bank-bbk-03" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf", "scoreWithoutSource": 3.7 }, { "url": "https://www.bbkonline.com/bbk-partners-with-array-to-launch-innovation-center-hiring-bahraini-software-engineers-to-accelerate-digital-transformation/", "scoreWithoutSource": 25.93 } ], "scenarioResults": [ { "scenario": "baseline", "score": 29.63, "rank": 8 }, { "scenario": "implemented-only", "score": 25.93, "rank": 8 }, { "scenario": "detail-cap", "score": 25.93, "rank": 7 }, { "scenario": "ai-core-0.5", "score": 31.37, "rank": 8 }, { "scenario": "ai-core-2", "score": 26.67, "rank": 8 }, { "scenario": "workforce-0.5", "score": 27.45, "rank": 8 }, { "scenario": "workforce-2", "score": 33.33, "rank": 8 }, { "scenario": "ecosystem-0.5", "score": 29.41, "rank": 8 }, { "scenario": "ecosystem-2", "score": 30.0, "rank": 8 }, { "scenario": "interfaces-0.5", "score": 27.45, "rank": 8 }, { "scenario": "interfaces-2", "score": 33.33, "rank": 7 }, { "scenario": "lineage-0.5", "score": 31.37, "rank": 8 }, { "scenario": "lineage-2", "score": 26.67, "rank": 8 }, { "scenario": "experimentation-0.5", "score": 31.37, "rank": 8 }, { "scenario": "experimentation-2", "score": 26.67, "rank": 8 }, { "scenario": "autonomy-0.5", "score": 31.37, "rank": 8 }, { "scenario": "autonomy-2", "score": 26.67, "rank": 8 }, { "scenario": "knowledge-0.5", "score": 31.37, "rank": 8 }, { "scenario": "knowledge-2", "score": 26.67, "rank": 8 }, { "scenario": "governance-0.5", "score": 25.49, "rank": 8 }, { "scenario": "governance-2", "score": 36.67, "rank": 7 } ], "scenarioRankRange": [ 7, 8 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "snb", "name": "Saudi National Bank", "country": "KSA", "sector": "Banking", "scope": "Saudi National Bank; subsidiary evidence explicitly labelled", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "snb", "evidence": [ { "id": "bank-snb-01", "companyId": "snb", "sourceId": "snb-ar25", "dimension": "ai-core", "claim": "SNB reports using AI-derived retail customer insights for segmented cross-selling during 2025.", "url": "https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf", "originalSourceUrl": "https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf", "sourceTitle": "Saudi National Bank Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Saudi National Bank", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF spread p.19 (zero-based18), printed pp.36–37; Retail Banking", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Saudi National Bank; subsidiary evidence explicitly labelled. Issuer narrative; no controlled outcome or coverage denominator. Official PDF unavailable; mirrored report inspected. Application: internal-operating-practice.", "access": "Bank-authored report on Argaam mirror; issuer PDF timed out. Mirror provenance retained, not independently hash-matched. | Review: full-company-authored-mirror-read-not-hashmatched", "rationale": "Named AI retail segmentation workflow. Company report is readable only on Argaam mirror; preserve unverified mirror provenance; no audited outcome.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ac356001f5cf9c3a0356f9ebf42d2ae4876b0b85c133ccfc9b228a91eaf5ad25", "review": { "decision": "retain", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-snb-02", "companyId": "snb", "sourceId": "snb-tech", "dimension": "workforce", "claim": "SNB advertises a three-month technology programme with an AI track and two months of workplace training.", "url": "https://www.alahli.com/en/pages/about-us/careers/snb-technology-program", "originalSourceUrl": "https://www.alahli.com/en/pages/about-us/careers/snb-technology-program", "sourceTitle": "SNB Technology Program", "corroboratingSourceIds": [], "publisher": "Saudi National Bank", "sourceType": "company-program-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "About the Program; Program Duration", "status": "unclear", "level": 1, "eligible": true, "limitation": "Claim scope: Saudi National Bank; subsidiary evidence explicitly labelled. Undated; application closed; programme page does not establish participation or skills gained. Application: internal-operating-practice.", "access": "Full page or document text retrieved", "rationale": "Specific programme design; current cohort completion not evidenced.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "624597852c75e5296f686fffe95a8ed9421bb45fd6bcee51f6cbc5a8662e200b", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-snb-03", "companyId": "snb", "sourceId": "snb-ar25", "dimension": "ai-core", "claim": "SNB Capital describes AI use in buy-side research, multi-asset investing and analytics.", "url": "https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf", "originalSourceUrl": "https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf", "sourceTitle": "Saudi National Bank Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Saudi National Bank", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF spread p.21 (zero-based20), printed pp.38–39; Asset Management", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: SNB Capital asset-management business only. Subsidiary-only evidence; no bank-wide coverage inferred. Application: internal-operating-practice.", "access": "Bank-authored report on Argaam mirror; issuer PDF timed out. Mirror provenance retained, not independently hash-matched.", "rationale": "Explicit subsidiary deployment across named functions.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "a1e357d9e40ea23c33e2af5b66b49555fcf367621c566a9d6c58f25e4b3537e6", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-snb-04", "companyId": "snb", "sourceId": "snb-ar25", "dimension": "governance", "claim": "SNB's refreshed sustainability direction includes responsible AI readiness.", "url": "https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf", "originalSourceUrl": "https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf", "sourceTitle": "Saudi National Bank Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Saudi National Bank", "sourceType": "company-annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "PDF spread p.8 (zero-based7), Chairman statement", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Saudi National Bank; subsidiary evidence explicitly labelled. Context only; not qualifying implementation evidence. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "Bank-authored report on Argaam mirror; issuer PDF timed out. Mirror provenance retained, not independently hash-matched.", "rationale": "Broad strategic direction without a named operating AI control. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "c0409500c96882f4696426b9c5ab5bc7a172e50ad349cbe99b416d8562971ad5", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 1, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 4, "score": 15, "coverage": 2, "rank": 9, "sourceCount": 2, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "snb-ar25", "snb-tech" ], "secondPass": { "date": "2026-10-10", "sources": [ "snb-ar25", "snb-tech" ], "passages": [ "Annual2025 AI/artificial intelligence matches: Chairman responsible-AI strategy; retail printed pp.36–37; SNB Capital printed pp.38–39; Data Robot related-party contract (not deployment evidence).", "Technology graduate programme AI/data curriculum." ], "access": "Official annual PDF timed out twice; issuer-authored Argaam mirror read. No complete Arabic source review." } }, "audit": { "eligibleClaims": 3, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 1, "latestSourceDate": "2026", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "bank-snb-01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 1, "winningClaimIds": [ "bank-snb-02" ], "contribution": 3.7037 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf", "scoreWithoutSource": 3.7 }, { "url": "https://www.alahli.com/en/pages/about-us/careers/snb-technology-program", "scoreWithoutSource": 11.11 } ], "scenarioResults": [ { "scenario": "baseline", "score": 14.81, "rank": 9 }, { "scenario": "implemented-only", "score": 11.11, "rank": 9 }, { "scenario": "detail-cap", "score": 11.11, "rank": 9 }, { "scenario": "ai-core-0.5", "score": 9.8, "rank": 9 }, { "scenario": "ai-core-2", "score": 23.33, "rank": 9 }, { "scenario": "workforce-0.5", "score": 13.73, "rank": 9 }, { "scenario": "workforce-2", "score": 16.67, "rank": 9 }, { "scenario": "ecosystem-0.5", "score": 15.69, "rank": 9 }, { "scenario": "ecosystem-2", "score": 13.33, "rank": 9 }, { "scenario": "interfaces-0.5", "score": 15.69, "rank": 9 }, { "scenario": "interfaces-2", "score": 13.33, "rank": 9 }, { "scenario": "lineage-0.5", "score": 15.69, "rank": 9 }, { "scenario": "lineage-2", "score": 13.33, "rank": 9 }, { "scenario": "experimentation-0.5", "score": 15.69, "rank": 9 }, { "scenario": "experimentation-2", "score": 13.33, "rank": 9 }, { "scenario": "autonomy-0.5", "score": 15.69, "rank": 9 }, { "scenario": "autonomy-2", "score": 13.33, "rank": 9 }, { "scenario": "knowledge-0.5", "score": 15.69, "rank": 9 }, { "scenario": "knowledge-2", "score": 13.33, "rank": 9 }, { "scenario": "governance-0.5", "score": 15.69, "rank": 9 }, { "scenario": "governance-2", "score": 13.33, "rank": 9 } ], "scenarioRankRange": [ 9, 9 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "nbb", "name": "National Bank of Bahrain", "country": "Bahrain", "sector": "Banking", "scope": "NBB; NBB Group including BisB where source explicitly says group", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "nbb", "evidence": [ { "id": "bank-nbb-01", "companyId": "nbb", "sourceId": "nbb-array", "dimension": "ecosystem", "claim": "NBB's ARRAY agreement specifies assessment of its technology footprint and planned NLP/GenAI integration.", "url": "https://nbbonline.com/news/17533/", "originalSourceUrl": "https://nbbonline.com/news/17533/", "sourceTitle": "NBB Partners with ARRAY Innovation to Drive AI-Driven Efficiency and Customer Service Excellence", "corroboratingSourceIds": [], "publisher": "National Bank of Bahrain", "sourceType": "company-announcement", "publicationDate": "2024-11-03", "accessedDate": "2026-10-10", "locator": "Planned assessment and NLP/GenAI collaboration", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: NBB; NBB Group including BisB where source explicitly says group. No live AI workflow established by agreement announcement. Application: internal-operating-practice. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "access": "Full indexed article; direct retrieval 403 | Review: inaccessible-indexed-only", "rationale": "Original official URL inaccessible during review; indexed-only passage cannot score under release protocol. A separate directly readable annual report may support another claim. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 1, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "0968eb8b3e1ae026cc694ee021bc1c9f3b41b432ba96a362c30b170cb71d5b3d", "review": { "decision": "exclude", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbb-02", "companyId": "nbb", "sourceId": "nbb-academy", "dimension": "workforce", "claim": "NBB Group launched a seven-day AI and digital academy with four phases and a planned banking-project hackathon.", "url": "https://nbbonline.com/news/21947/", "originalSourceUrl": "https://nbbonline.com/news/21947/", "sourceTitle": "With Support from Tamkeen NBB Group Unveils New AI and Digital Banking Academy", "corroboratingSourceIds": [], "publisher": "National Bank of Bahrain", "sourceType": "company-announcement", "publicationDate": "2025-10-25", "accessedDate": "2026-10-10", "locator": "Seven-day, four-phase academy description", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: NBB Group including Bahrain Islamic Bank. Training delivery and final hackathon outcomes not independently evidenced. Application: internal-operating-practice. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "access": "Full indexed article; direct domain retrieval restricted | Review: inaccessible-indexed-only", "rationale": "Original official URL returns403/access failure; indexed-only academy passage cannot score under release protocol. Replace with independently readable exact claim if found. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 3, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "32f8ac3e939ac5e67433ea56731126576368f8b159baa3026c6187d51f18ca8a", "review": { "decision": "exclude", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbb-03", "companyId": "nbb", "sourceId": "nbb-board", "dimension": "workforce", "claim": "NBB and BisB directors and executives received an AI/GenAI training session.", "url": "https://nbbonline.com/news/14234/", "originalSourceUrl": "https://nbbonline.com/news/14234/", "sourceTitle": "NBB Group Hosts Gartner Training Session on AI for Group Board of Directors and Executive Management", "corroboratingSourceIds": [], "publisher": "National Bank of Bahrain", "sourceType": "company-announcement", "publicationDate": "2024-07-06", "accessedDate": "2026-10-10", "locator": "Board/executive training paragraph", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: NBB Group including Bahrain Islamic Bank. Historic 2024 event; no evidence of changed operating decisions. Application: internal-operating-practice. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "access": "Full indexed article | Review: inaccessible-indexed-only", "rationale": "Original official URL inaccessible; exclude from scoring absent readable alternate. This is an access gap, not absent training. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 2, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "64771b2e1c13766d20e086c669248a92bc23ae1b10bbd8b91ad631169463ef6a", "review": { "decision": "exclude", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbb-04", "companyId": "nbb", "sourceId": "nbb-ar25", "dimension": "workforce", "claim": "Two AI and Digital Banking cohorts reached 60 employees in June and November 2025, with a practical hackathon.", "url": "https://nbbonline.com/wp-content/uploads/2026/03/Annual-Financial-and-Sustainability-Report-2025-3.pdf", "originalSourceUrl": "https://nbbonline.com/wp-content/uploads/2026/03/Annual-Financial-and-Sustainability-Report-2025-3.pdf", "sourceTitle": "NBB Annual Financial and Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "National Bank of Bahrain", "sourceType": "company-annual-report", "publicationDate": "2026-03-03", "accessedDate": "2026-10-10", "locator": "Printed pp.68–69, NBB Group AI & Digital Banking Academy", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: NBB; NBB Group including BisB where source explicitly says group. Curriculum includes multiple technologies; 60 is not an AI competency certification count. Application: internal-operating-practice. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "access": "Official indexed PDF passages retrieved; direct full PDF failed. Report date corroborated by official investor index. | Review: inaccessible-indexed-only", "rationale": "Official annual PDF inaccessible in independent review, including www alternate. Indexed passages alone cannot establish scored claim under frozen protocol; no inference of absent academy. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 3, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "82cf19639fb231c2b6675199cb4cce2cef80b02074ea03d8f9d94eadf5e2601f", "review": { "decision": "exclude", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "bank-nbb-05", "companyId": "nbb", "sourceId": "nbb-results25", "dimension": "interfaces", "claim": "NBB's CEO reports introducing an AI-assisted chatbot to augment service.", "url": "https://nbbonline.com/news/27971/", "originalSourceUrl": "https://nbbonline.com/news/27971/", "sourceTitle": "NBB records a 4% growth in net profit attributable to shareholders for year ended 31 December 2025", "corroboratingSourceIds": [], "publisher": "National Bank of Bahrain", "sourceType": "company-results-announcement", "publicationDate": "2026-02-17", "accessedDate": "2026-10-10", "locator": "Group CEO commentary", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: NBB; NBB Group including BisB where source explicitly says group. No staff access, adoption, escalation or performance details; customer-facing service operation, not enterprise-wide internal interface. Application: customer-service-operating-interface. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "access": "Full official indexed release read; direct page fetch failed. | Review: inaccessible-indexed-only", "rationale": "Official results page inaccessible in independent review. Retain as unscored research lead unless exact claim is read in eligible alternate source. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 2, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "287f611cad05bbc9380f4ab3ff45f6a9c2609a5149eb7d8554b86d8bd3f3a23e", "review": { "decision": "exclude", "record": "research/method-banking-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 5, "searchBoundary": { "date": "2026-10-10", "languages": [ "English", "limited Arabic discovery" ], "method": "Targeted issuer-domain searches for AI/artificial intelligence + annual report 2025 + company; followed relevant original sources and linked reports", "reviewedSourceIds": [ "nbb-array", "nbb-academy", "nbb-board", "nbb-ar25", "nbb-results25" ], "secondPass": { "date": "2026-10-10", "sources": [ "nbb-ar25", "nbb-results25", "nbb-array", "nbb-academy", "nbb-board" ], "passages": [ "Annual2025 indexedHumanCapitalprinted68–69 actualcohorts/hackathon; indexedcorporategovernance passages genericAML excluded.", "ResultsFebruary17CEO chatbot; ARRAY specificplannedassessment; academyrelease and boardtraining." ], "access": "Direct NBB pages/PDF fail; full indexed official release text and annual section passages read. Full annual report could not be searched; unresolved disclosure gaps particularly lineage/autonomy/experimentation." } }, "audit": { "eligibleClaims": 0, "excludedClaims": 5, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "aramco", "name": "Aramco", "country": "Saudi Arabia", "sector": "Energy & Utilities", "scope": "Saudi Aramco group; each claim restricted to named site or function.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "aramco", "evidence": [ { "id": "ARAMCO01", "companyId": "aramco", "sourceId": "ENE-S01", "dimension": "ai-core", "claim": "Fadhili permanently adopted reinforcement-learning control for acid-gas removal after its October 2024 to April 2025 pilot.", "url": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "originalSourceUrl": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "sourceTitle": "AI and Big Data", "corroboratingSourceIds": [], "publisher": "Aramco", "sourceType": "corporate-operating-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Fadhili Gas Plant", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Saudi Aramco group; each claim restricted to named site or function.. Site-level; rollout elsewhere remained assessment. Undated live page. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Fadhili passage states permanent adoption after a dated pilot and identifies reinforcement-learning acid-gas control. Evaluation remains ongoing; no group-wide deployment inferred.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "107e44cd85a9c5638663709aa679e852e837981da6a5b219db0f3d55a1356de1", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ARAMCO02", "companyId": "aramco", "sourceId": "ENE-S01", "dimension": "autonomy", "claim": "At Abqaiq, AI/ML automatically adjusts the oil-stabilization process.", "url": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "originalSourceUrl": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "sourceTitle": "AI and Big Data", "corroboratingSourceIds": [], "publisher": "Aramco", "sourceType": "corporate-operating-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Abqaiq Plants", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Saudi Aramco group; each claim restricted to named site or function.. No override, fail-safe or decision-log details. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Abqaiq passage expressly attributes automatic process adjustment to AI/ML. Scope is that plant; safety authority and override controls are not demonstrated.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "2bec4fb2e26447a208ef7d19c21190e828cd46e1cc5f2e8b2b7519d4bd9d4e87", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ARAMCO03", "companyId": "aramco", "sourceId": "ENE-S01", "dimension": "interfaces", "claim": "Supply Chain Control Center uses AI alerts alongside live shipment tracking.", "url": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "originalSourceUrl": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "sourceTitle": "AI and Big Data", "corroboratingSourceIds": [], "publisher": "Aramco", "sourceType": "corporate-operating-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Supply Chain Control Center", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Saudi Aramco group; each claim restricted to named site or function.. No coverage denominator or attributed outcome. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Supply Chain Control Center passage specifies AI alerts with live logistics tracking. A described operating interface qualifies; effectiveness is not independently verified.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "f174833c6c7a5cc8a24fe5193a714573d39191958b2e72c8eb07fc606d927f19", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ARAMCO04", "companyId": "aramco", "sourceId": "ENE-S01", "dimension": "experimentation", "claim": "The 4IR Center uses operational data to develop AI solutions in a testing facility.", "url": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "originalSourceUrl": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "sourceTitle": "AI and Big Data", "corroboratingSourceIds": [], "publisher": "Aramco", "sourceType": "corporate-operating-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "4IR Center", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Saudi Aramco group; each claim restricted to named site or function.. No release-gate protocol disclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The named testing facility uses operating data to develop AI solutions. This supports implemented experimentation infrastructure, without comparative test results or decision thresholds.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "7c9cc5b9608ca787f8a56e67065c9f3a8e79a4102d0b8f3903bb2138b99bbb10", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ARAMCO05", "companyId": "aramco", "sourceId": "ENE-S01", "dimension": "ecosystem", "claim": "Yokogawa and Aramco jointly piloted the subsequently adopted Fadhili controller.", "url": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "originalSourceUrl": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "sourceTitle": "AI and Big Data", "corroboratingSourceIds": [], "publisher": "Aramco", "sourceType": "corporate-operating-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Fadhili Gas Plant", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Saudi Aramco group; each claim restricted to named site or function.. Not evidence all partners follow this model. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Named Yokogawa entities jointly delivered the dated Fadhili pilot that Aramco says became permanent. This is implemented collaboration within one case.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "1da705cee1220c2149380984f45fb2692651bfa4cc4230a3cd170e12c2685dca", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ARAMCO06", "companyId": "aramco", "sourceId": "ENE-S02", "dimension": "workforce", "claim": "Aramco reports training more than6,000 AI developers with universities, while domain specialists help train models.", "url": "https://europe.aramco.com/en/news-media/news/2025/aramco-deploys-ai", "originalSourceUrl": "https://europe.aramco.com/en/news-media/news/2025/aramco-deploys-ai", "sourceTitle": "How AI helps Aramco turn old data into new opportunity", "corroboratingSourceIds": [], "publisher": "Aramco", "sourceType": "corporate-article", "publicationDate": "2025-03-12", "accessedDate": "2026-10-10", "locator": "Final substantive paragraph", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Saudi Aramco group; each claim restricted to named site or function.. Training underway, not6,000 verified completions. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The final substantive paragraph describes an active training programme and specialist/model-development collaboration. More than 6,000 refers to people being trained, not verified completions.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "c7c9f85f631b337d7afc8d47ed614dfe63b9c2ae5fbbf33e4c33f59a14841155", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ARAMCO07", "companyId": "aramco", "sourceId": "ENE-S02", "dimension": "knowledge", "claim": "Aramco reports reprocessing legacy seismic data with AI to obtain new findings.", "url": "https://europe.aramco.com/en/news-media/news/2025/aramco-deploys-ai", "originalSourceUrl": "https://europe.aramco.com/en/news-media/news/2025/aramco-deploys-ai", "sourceTitle": "How AI helps Aramco turn old data into new opportunity", "corroboratingSourceIds": [], "publisher": "Aramco", "sourceType": "corporate-article", "publicationDate": "2025-03-12", "accessedDate": "2026-10-10", "locator": "Applied AI has a transformative impact", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Saudi Aramco group; each claim restricted to named site or function.. No maintained knowledge-base or retrieval governance details. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Explicit implemented reuse of institutional technical data.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "e36441899b1d817639d75163b64f645cedc05c445fc2f356d169fc8becf92bf7", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 3, "interfaces": 3, "lineage": null, "experimentation": 2, "autonomy": 3, "knowledge": 2, "governance": null }, "points": 19, "score": 70, "coverage": 7, "rank": 1, "sourceCount": 2, "searchBoundary": { "companyId": "aramco", "date": "2026-10-10", "queries": [ "site:aramco.com AI machine learning Fadhili operations", "site:aramco.com \"AI\" \"training\" \"2025\"" ], "passagesChecked": "Named facility, supply-chain, development-center and workforce passages; 2025 sustainability PDF lead not fully reviewed.", "languages": [ "English" ], "searchCompleteness": "Bounded non-exhaustive; nine-theme lens applied to retained passages, not nine independent searches.", "reviewedSourceIds": [ "ENE-S01", "ENE-S02" ] }, "audit": { "eligibleClaims": 7, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 5, "latestSourceDate": "2025-03-12", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "ARAMCO01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "ARAMCO06" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 3, "winningClaimIds": [ "ARAMCO05" ], "contribution": 11.1111 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "ARAMCO03" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 2, "winningClaimIds": [ "ARAMCO04" ], "contribution": 7.4074 }, { "dimension": "autonomy", "level": 3, "winningClaimIds": [ "ARAMCO02" ], "contribution": 11.1111 }, { "dimension": "knowledge", "level": 2, "winningClaimIds": [ "ARAMCO07" ], "contribution": 7.4074 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data", "scoreWithoutSource": 18.52 }, { "url": "https://europe.aramco.com/en/news-media/news/2025/aramco-deploys-ai", "scoreWithoutSource": 51.85 } ], "scenarioResults": [ { "scenario": "baseline", "score": 70.37, "rank": 1 }, { "scenario": "implemented-only", "score": 70.37, "rank": 1 }, { "scenario": "detail-cap", "score": 51.85, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 68.63, "rank": 1 }, { "scenario": "ai-core-2", "score": 73.33, "rank": 1 }, { "scenario": "workforce-0.5", "score": 68.63, "rank": 1 }, { "scenario": "workforce-2", "score": 73.33, "rank": 1 }, { "scenario": "ecosystem-0.5", "score": 68.63, "rank": 1 }, { "scenario": "ecosystem-2", "score": 73.33, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 68.63, "rank": 1 }, { "scenario": "interfaces-2", "score": 73.33, "rank": 1 }, { "scenario": "lineage-0.5", "score": 74.51, "rank": 1 }, { "scenario": "lineage-2", "score": 63.33, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 70.59, "rank": 1 }, { "scenario": "experimentation-2", "score": 70.0, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 68.63, "rank": 1 }, { "scenario": "autonomy-2", "score": 73.33, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 70.59, "rank": 1 }, { "scenario": "knowledge-2", "score": 70.0, "rank": 1 }, { "scenario": "governance-0.5", "score": 74.51, "rank": 1 }, { "scenario": "governance-2", "score": 63.33, "rank": 1 } ], "scenarioRankRange": [ 1, 1 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "dewa", "name": "Dubai Electricity and Water Authority", "country": "UAE", "sector": "Energy & Utilities", "scope": "DEWA utility operations and internal digital functions; external product sales excluded.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "dewa", "evidence": [ { "id": "DEWA01", "companyId": "dewa", "sourceId": "ENE-S06", "dimension": "interfaces", "claim": "Testing Centre AI agents generate test artifacts from specifications, execute tests and report results in the engineering pipeline.", "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "originalSourceUrl": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "sourceTitle": "DEWA deploys agentic AI across its digital platforms", "corroboratingSourceIds": [], "publisher": "DEWA", "sourceType": "corporate-release", "publicationDate": "2026-07-06", "accessedDate": "2026-10-10", "locator": "Boosting digital testing efficiency", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: DEWA utility operations and internal digital functions; external product sales excluded.. This tests digital services; it does not establish evaluation of AI models. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The Testing Centre paragraph specifies embedded test generation, execution and reporting in an engineering pipeline. It demonstrates software-testing automation, not controlled evaluation of AI impact.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "7b4b3f1c8ce8f68e4a3594dc11848808288043926d1546d035872470b7aaaf2f", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "DEWA02", "companyId": "dewa", "sourceId": "ENE-S06", "dimension": "workforce", "claim": "AI agents help design and engineering staff generate components and detect deviations from approved standards.", "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "originalSourceUrl": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "sourceTitle": "DEWA deploys agentic AI across its digital platforms", "corroboratingSourceIds": [], "publisher": "DEWA", "sourceType": "corporate-release", "publicationDate": "2026-07-06", "accessedDate": "2026-10-10", "locator": "Enhancing customer experience", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: DEWA utility operations and internal digital functions; external product sales excluded.. No staff adoption denominator or controlled productivity study. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Named design and engineering roles use AI-generated components and standards-deviation detection. This is workforce augmentation; training completion and competence are separate.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "61f67b8ef9d965da933e3a11080406f8c1c64b6b4e5efb87ab050a0a8d61d578", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "DEWA03", "companyId": "dewa", "sourceId": "ENE-S06", "dimension": "ecosystem", "claim": "DEWA's Agent Development Kit supports teams building and scaling agentic capabilities.", "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "originalSourceUrl": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "sourceTitle": "DEWA deploys agentic AI across its digital platforms", "corroboratingSourceIds": [], "publisher": "DEWA", "sourceType": "corporate-release", "publicationDate": "2026-07-06", "accessedDate": "2026-10-10", "locator": "A unified, secure platform", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: DEWA utility operations and internal digital functions; external product sales excluded.. No external interoperability or partner governance detail; level2 proposed conservatively. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Implemented common enablement platform.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "c0cb728a555453bec2b6c1505d6d54aac5331dfcb2e86a5aec687eea3a72fa12", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "DEWA04", "companyId": "dewa", "sourceId": "ENE-S06", "dimension": "governance", "claim": "The Agent Development Kit operates under governance, security and privacy frameworks.", "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "originalSourceUrl": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "sourceTitle": "DEWA deploys agentic AI across its digital platforms", "corroboratingSourceIds": [], "publisher": "DEWA", "sourceType": "corporate-release", "publicationDate": "2026-07-06", "accessedDate": "2026-10-10", "locator": "A unified, secure platform", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: DEWA utility operations and internal digital functions; external product sales excluded.. Control content, enforcement and escalation not described; cannot support3. Application: internal-operating-practice. Exclusion: New claim has no explicit accepted review decision. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Generic adherence to governance, security and privacy frameworks does not identify an AI-specific control, accountable role or concrete applied governance practice. Toolkit deployment alone cannot establish this theme. Exclusion: New claim has no explicit accepted review decision. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 2, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "005aa4fe45810762ead781abe495118f92f807337107eda0b5496094dc81ef0a", "review": { "decision": "exclude", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "DEWA05", "companyId": "dewa", "sourceId": "ENE-S07", "dimension": "knowledge", "claim": "Rammas draws responses directly from DEWA's website.", "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/1/dewas-virtual-employee-rammas-handled", "originalSourceUrl": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/1/dewas-virtual-employee-rammas-handled", "sourceTitle": "DEWA’s virtual employee Rammas handled over 1.6 million inquiries in 2025", "corroboratingSourceIds": [], "publisher": "DEWA", "sourceType": "corporate-release", "publicationDate": "2026-01-24", "accessedDate": "2026-10-10", "locator": "Openingparagraph", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: DEWA utility operations and internal digital functions; external product sales excluded.. No knowledge freshness, retrieval accuracy or source citation evaluation disclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The January release explicitly states website-grounded responses. Retrieval mechanism supports the knowledge theme; answer quality and provenance auditing are not established.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "20566b41a1ad20ed66f37f29d8d14603752606a64fe48cc0f77ad9246da4b3b0", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "DEWA06", "companyId": "dewa", "sourceId": "ENE-S07", "dimension": "ai-core", "claim": "Rammas handled over1.6million Arabic/English inquiries in2025 across DEWA channels.", "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/1/dewas-virtual-employee-rammas-handled", "originalSourceUrl": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/1/dewas-virtual-employee-rammas-handled", "sourceTitle": "DEWA’s virtual employee Rammas handled over 1.6 million inquiries in 2025", "corroboratingSourceIds": [], "publisher": "DEWA", "sourceType": "corporate-release", "publicationDate": "2026-01-24", "accessedDate": "2026-10-10", "locator": "Opening and channels paragraphs", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: DEWA utility operations and internal digital functions; external product sales excluded.. Customer-facing operation, not a separately sold AI product; inquiries are not unique users or resolved cases. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The January release gives annual inquiry coverage, languages and deployed service channels. Counts are company-reported interactions, not unique users or verified successful resolutions.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "23fefe3c81d198dec620a311668fb88c0316bdf246165286e589f53696d5bfe6", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 2, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": 3, "governance": null }, "points": 14, "score": 52, "coverage": 5, "rank": 2, "sourceCount": 2, "searchBoundary": { "companyId": "dewa", "date": "2026-10-10", "queries": [ "site:dewa.gov.ae AI Rammas2025 2026 agentic", "site:dewa.gov.ae AI plant intelligent controller Siemens2025", "site:dewa.gov.ae AI governance workforce annual report" ], "passagesChecked": "Full July2026 agentic and January2026 Rammas bodies read. October2025 controller body inaccessible; smartgrid/sustainability PDF leads not fully reviewed.", "languages": [ "English" ], "searchCompleteness": "Bounded non-exhaustive; nine-theme lens applied to retained passages, not nine independent searches.", "reviewedSourceIds": [ "ENE-S06", "ENE-S07" ] }, "audit": { "eligibleClaims": 5, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 0, "latestSourceDate": "2026-07-06", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "DEWA06" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "DEWA02" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 2, "winningClaimIds": [ "DEWA03" ], "contribution": 7.4074 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "DEWA01" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 3, "winningClaimIds": [ "DEWA05" ], "contribution": 11.1111 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms", "scoreWithoutSource": 22.22 }, { "url": "https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/1/dewas-virtual-employee-rammas-handled", "scoreWithoutSource": 29.63 } ], "scenarioResults": [ { "scenario": "baseline", "score": 51.85, "rank": 2 }, { "scenario": "implemented-only", "score": 51.85, "rank": 2 }, { "scenario": "detail-cap", "score": 37.04, "rank": 2 }, { "scenario": "ai-core-0.5", "score": 49.02, "rank": 2 }, { "scenario": "ai-core-2", "score": 56.67, "rank": 2 }, { "scenario": "workforce-0.5", "score": 49.02, "rank": 2 }, { "scenario": "workforce-2", "score": 56.67, "rank": 2 }, { "scenario": "ecosystem-0.5", "score": 50.98, "rank": 2 }, { "scenario": "ecosystem-2", "score": 53.33, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 49.02, "rank": 3 }, { "scenario": "interfaces-2", "score": 56.67, "rank": 2 }, { "scenario": "lineage-0.5", "score": 54.9, "rank": 2 }, { "scenario": "lineage-2", "score": 46.67, "rank": 2 }, { "scenario": "experimentation-0.5", "score": 54.9, "rank": 2 }, { "scenario": "experimentation-2", "score": 46.67, "rank": 3 }, { "scenario": "autonomy-0.5", "score": 54.9, "rank": 2 }, { "scenario": "autonomy-2", "score": 46.67, "rank": 2 }, { "scenario": "knowledge-0.5", "score": 49.02, "rank": 2 }, { "scenario": "knowledge-2", "score": 56.67, "rank": 2 }, { "scenario": "governance-0.5", "score": 54.9, "rank": 2 }, { "scenario": "governance-2", "score": 46.67, "rank": 2 } ], "scenarioRankRange": [ 2, 3 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "adnoc", "name": "ADNOC", "country": "UAE", "sector": "Energy & Utilities", "scope": "ADNOC group; asset-level claims confined to named operating subsidiaries.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "adnoc", "evidence": [ { "id": "ADNOC01", "companyId": "adnoc", "sourceId": "ENE-S04", "dimension": "ai-core", "claim": "Neuron5 monitors equipment at NEB and Taweelah using sensor readings to predict maintenance needs.", "url": "https://www.adnoc.ae/en/news-and-media/press-releases/2024/adnoc-deploys-pioneering-ai-enabled-process-optimization-technology", "originalSourceUrl": "https://www.adnoc.ae/en/news-and-media/press-releases/2024/adnoc-deploys-pioneering-ai-enabled-process-optimization-technology", "sourceTitle": "ADNOC Deploys Pioneering AI-Enabled Process Optimization Technology", "corroboratingSourceIds": [], "publisher": "ADNOC", "sourceType": "corporate-release", "publicationDate": "2024-08-27", "accessedDate": "2026-10-10", "locator": "Release paragraphs1–5", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: ADNOC group; asset-level claims confined to named operating subsidiaries.. Not group-wide deployment; prediction is not permission to actuate equipment. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The release identifies initial deployment at NEB and Taweelah, equipment scope and sensor-based prediction. Enterprise-wide rollout and projected maintenance benefits remain prospective.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "dfbfcccdbfdcc6db2834f38df5183bf1f69061a3a2d17d7a040eb162ad407906", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ADNOC02", "companyId": "adnoc", "sourceId": "ENE-S04", "dimension": "ecosystem", "claim": "Thamama Excellence Center, AIQ and AVEVA developed the deployed Neuron5 system.", "url": "https://www.adnoc.ae/en/news-and-media/press-releases/2024/adnoc-deploys-pioneering-ai-enabled-process-optimization-technology", "originalSourceUrl": "https://www.adnoc.ae/en/news-and-media/press-releases/2024/adnoc-deploys-pioneering-ai-enabled-process-optimization-technology", "sourceTitle": "ADNOC Deploys Pioneering AI-Enabled Process Optimization Technology", "corroboratingSourceIds": [], "publisher": "ADNOC", "sourceType": "corporate-release", "publicationDate": "2024-08-27", "accessedDate": "2026-10-10", "locator": "Final release paragraph", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: ADNOC group; asset-level claims confined to named operating subsidiaries.. Commercial partners are interested sources. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The release names three development partners for the initially deployed system. This supports a specific delivered collaboration, not independently evaluated partnership effectiveness.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ad217c1832a6d3ba22fd1c166b07edca1bcfe8d329cb8cb61ff7b6865845033b", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ADNOC03", "companyId": "adnoc", "sourceId": "ENE-S05", "dimension": "workforce", "claim": "ADNOC reports40,000 colleagues equipped with AI skills in2025.", "url": "https://reports.adnoc.ae/e/2025-a-year-in-review", "originalSourceUrl": "https://reports.adnoc.ae/e/2025-a-year-in-review", "sourceTitle": "2025: A Year in Review", "corroboratingSourceIds": [], "publisher": "ADNOC", "sourceType": "annual-review", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printedslide9", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: ADNOC group; asset-level claims confined to named operating subsidiaries.. No skill assessment, denominator or proficiency measure given. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The full annual-review page reports 40K colleagues equipped with AI skills in the 2025 section. Coverage count supports specificity; proficiency and denominator are unestablished.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "3fb388a6ebdafcad99bff369df369eae716c39f27ae971d41f183b6962d896e4", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ADNOC04", "companyId": "adnoc", "sourceId": "ENE-S05", "dimension": "experimentation", "claim": "ADNOC reports its AI Lab developed15 proofs of concept in2025.", "url": "https://reports.adnoc.ae/e/2025-a-year-in-review", "originalSourceUrl": "https://reports.adnoc.ae/e/2025-a-year-in-review", "sourceTitle": "2025: A Year in Review", "corroboratingSourceIds": [], "publisher": "ADNOC", "sourceType": "annual-review", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printedslide9", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: ADNOC group; asset-level claims confined to named operating subsidiaries.. No test design, acceptance gates or failure reporting. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The annual-review page attributes 15 developed proofs of concept to the AI Lab. Completed experimentation activity is reported; evaluation quality is unspecified.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "52cd3952d1bfdfbbd0b0a45016b0d76c154b341af516ad6b234d49851d1af970", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "ADNOC05", "companyId": "adnoc", "sourceId": "ENE-S05", "dimension": "knowledge", "claim": "ENERGYai began deployment in2025 using decades of ADNOC data and workflows.", "url": "https://reports.adnoc.ae/e/2025-a-year-in-review", "originalSourceUrl": "https://reports.adnoc.ae/e/2025-a-year-in-review", "sourceTitle": "2025: A Year in Review", "corroboratingSourceIds": [], "publisher": "ADNOC", "sourceType": "annual-review", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printedslide10", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: ADNOC group; asset-level claims confined to named operating subsidiaries.. No source revision, access control or grounding-evaluation mechanism disclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The annual-review page says deployment of ENERGYai began and describes training on company data and workflows. Knowledge use is reported, with no quality measure.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "1a5dc48a79894379eecc53526fc19333818229db0c596b1128ebc069a61cf09d", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 3, "interfaces": null, "lineage": null, "experimentation": 2, "autonomy": null, "knowledge": 2, "governance": null }, "points": 13, "score": 48, "coverage": 5, "rank": 3, "sourceCount": 2, "searchBoundary": { "companyId": "adnoc", "date": "2026-10-10", "queries": [ "site:adnoc.ae AI Neuron5 RoboWell2024 2025", "site:adnoc.ae artificial intelligence training ENERGYai governance" ], "passagesChecked": "Exact Neuron5 release and 2025 year-review AI slides read. Sustainability2024 PDF failed; other academy/operations leads not fully read.", "languages": [ "English" ], "searchCompleteness": "Bounded non-exhaustive; nine-theme lens applied to retained passages, not nine independent searches.", "reviewedSourceIds": [ "ENE-S04", "ENE-S05" ] }, "audit": { "eligibleClaims": 5, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 3, "latestSourceDate": "2024-08-27", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "ADNOC01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "ADNOC03" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 3, "winningClaimIds": [ "ADNOC02" ], "contribution": 11.1111 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 2, "winningClaimIds": [ "ADNOC04" ], "contribution": 7.4074 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 2, "winningClaimIds": [ "ADNOC05" ], "contribution": 7.4074 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://reports.adnoc.ae/e/2025-a-year-in-review", "scoreWithoutSource": 22.22 }, { "url": "https://www.adnoc.ae/en/news-and-media/press-releases/2024/adnoc-deploys-pioneering-ai-enabled-process-optimization-technology", "scoreWithoutSource": 25.93 } ], "scenarioResults": [ { "scenario": "baseline", "score": 48.15, "rank": 3 }, { "scenario": "implemented-only", "score": 48.15, "rank": 3 }, { "scenario": "detail-cap", "score": 37.04, "rank": 2 }, { "scenario": "ai-core-0.5", "score": 45.1, "rank": 3 }, { "scenario": "ai-core-2", "score": 53.33, "rank": 3 }, { "scenario": "workforce-0.5", "score": 45.1, "rank": 3 }, { "scenario": "workforce-2", "score": 53.33, "rank": 3 }, { "scenario": "ecosystem-0.5", "score": 45.1, "rank": 3 }, { "scenario": "ecosystem-2", "score": 53.33, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 50.98, "rank": 2 }, { "scenario": "interfaces-2", "score": 43.33, "rank": 3 }, { "scenario": "lineage-0.5", "score": 50.98, "rank": 3 }, { "scenario": "lineage-2", "score": 43.33, "rank": 3 }, { "scenario": "experimentation-0.5", "score": 47.06, "rank": 3 }, { "scenario": "experimentation-2", "score": 50.0, "rank": 2 }, { "scenario": "autonomy-0.5", "score": 50.98, "rank": 3 }, { "scenario": "autonomy-2", "score": 43.33, "rank": 3 }, { "scenario": "knowledge-0.5", "score": 47.06, "rank": 3 }, { "scenario": "knowledge-2", "score": 50.0, "rank": 3 }, { "scenario": "governance-0.5", "score": 50.98, "rank": 3 }, { "scenario": "governance-2", "score": 43.33, "rank": 3 } ], "scenarioRankRange": [ 2, 3 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "koc", "name": "Kuwait Oil Company", "country": "Kuwait", "sector": "Energy & Utilities", "scope": "KOC operating company, a KPC subsidiary; no group-wide extrapolation.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "koc", "evidence": [ { "id": "KOC01", "companyId": "koc", "sourceId": "ENE-S10", "dimension": "workforce", "claim": "KOC technicians completed a12-week SLB AI academy with hands-on production, optimization and planning work.", "url": "https://www.kockw.com/sites/EN/EMagazine/Pages/Events/1082.aspx", "originalSourceUrl": "https://www.kockw.com/sites/EN/EMagazine/Pages/Events/1082.aspx", "sourceTitle": "First batch of Certified Technicians Graduate from AI Academy in the UK", "corroboratingSourceIds": [], "publisher": "Kuwait Oil Company", "sourceType": "corporate-release", "publicationDate": "2025-11-03", "accessedDate": "2026-10-10", "locator": "Bodyparagraphs1,4,6", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: KOC operating company, a KPC subsidiary; no group-wide extrapolation.. Participant count and measured proficiency not stated. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The full company page describes a graduated cohort, a 12-week programme and applied curriculum with SLB. Participant count and competence outcomes are absent.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "99c1d1b4e3d65dd9693e6403e58552f61f1256e324f2d254362f048ed2b3e0dd", "review": { "decision": "retain", "record": "expansion/energy/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "KOC02", "companyId": "koc", "sourceId": "ENE-S11", "dimension": "experimentation", "claim": "KOC inaugurated its AI Innovation Center with Microsoft.", "url": "https://www.kockw.com/sites/EN/EMagazine/Pages/Events/1000.aspx", "originalSourceUrl": "https://www.kockw.com/sites/EN/EMagazine/Pages/Events/1000.aspx", "sourceTitle": "AI Innovation Center inauguration", "corroboratingSourceIds": [], "publisher": "Kuwait Oil Company", "sourceType": "corporate-release", "publicationDate": "2025-08-07", "accessedDate": "2026-10-10", "locator": "Bodyparagraphs1,4", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: KOC operating company, a KPC subsidiary; no group-wide extrapolation.. Inauguration does not establish an operating experimentation workflow. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named center and accountable adoption initiative.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "960953a7c793f31a18591352e92d0522414f94aaf2f1db9a02b8bef89cc5dd41", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "KOC03", "companyId": "koc", "sourceId": "ENE-S12", "dimension": "ai-core", "claim": "KOC and SLB developed ML-based mud-loss analysis and forecasting for planned wells.", "url": "https://investorcenter.slb.com/news-releases/news-release-details/slb-announces-fourth-quarter-and-full-year-2023-results/", "originalSourceUrl": "https://investorcenter.slb.com/news-releases/news-release-details/slb-announces-fourth-quarter-and-full-year-2023-results/", "sourceTitle": "SLB Announces Fourth-Quarter and Full-Year 2023 Results", "corroboratingSourceIds": [], "publisher": "SLB", "sourceType": "partner-results-release", "publicationDate": "2024-01-19", "accessedDate": "2026-10-10", "locator": "Quarterly Highlights KOC paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: KOC operating company, a KPC subsidiary; no group-wide extrapolation.. Production coverage and deployment acceptance not detailed; partner claim. 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No evidence of sustained portfolio governance or repeatability. 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Name-change issuer disclosure found; current legal identity should be reconfirmed in release.. Agreement does not establish delivery or completion. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named partner and specific contracted learning activity.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "325c45db4ef1a724b591e405284f64b8687e4e372c030cc003b913daf112ceaa", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "SEC02", "companyId": "sec", "sourceId": "ENE-S03", "dimension": "governance", "claim": "The Technical Committee's stated remit includes supervising AI integration into core activities.", "url": "https://www.se.com.sa/-/media/sec/Investors/Saudi-Electricity-Company-Annual-Report-2024_Eng_V21.ashx", "originalSourceUrl": "https://www.se.com.sa/-/media/sec/Investors/Saudi-Electricity-Company-Annual-Report-2024_Eng_V21.ashx", "sourceTitle": "Annual Report 2024", "corroboratingSourceIds": [], "publisher": "Saudi Electricity Company", "sourceType": "annual-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "PDF127; printed252–253", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Saudi Energy group; 2024 report uses former SEC name. Name-change issuer disclosure found; current legal identity should be reconfirmed in release.. Meetings are reported but no AI-specific agenda, risk review or approval decision is disclosed. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "AI is explicitly within an operating committee's remit.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "23b7f2e6e0e04038b96e4eb20987cac6de539056b7b1f75e0307f88a37f02f34", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "SEC03", "companyId": "sec", "sourceId": "ENE-S03", "dimension": "interfaces", "claim": "The report labels contracting RPA as artificial intelligence.", "url": "https://www.se.com.sa/-/media/sec/Investors/Saudi-Electricity-Company-Annual-Report-2024_Eng_V21.ashx", "originalSourceUrl": "https://www.se.com.sa/-/media/sec/Investors/Saudi-Electricity-Company-Annual-Report-2024_Eng_V21.ashx", "sourceTitle": "Annual Report 2024", "corroboratingSourceIds": [], "publisher": "Saudi Electricity Company", "sourceType": "annual-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "PDF79; printed156–157", "status": "unclear", "level": null, "eligible": false, "limitation": "Claim scope: Saudi Energy group; 2024 report uses former SEC name. Name-change issuer disclosure found; current legal identity should be reconfirmed in release.. Excluded: implementation appears digital automation but AI mechanism not specified. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "full-source-read", "rationale": "Retain as ambiguous lead; RPA alone does not establish AI inference or learning. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "da745cf2167287bc21789d562c12de98dbc2054d7696bb7ea2e5c6e13c1b985a", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": 1, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": 2 }, "points": 3, "score": 11, "coverage": 2, "rank": 6, "sourceCount": 1, "searchBoundary": { "companyId": "sec", "date": "2026-10-10", "queries": [ "site:se.com.sa \"artificial intelligence\" \"2025\" maintenance", "site:se.com.sa \"Saudi Energy\" \"name\"" ], "passagesChecked": "Complete 148-page 2024 PDF keyword screen; all AI matches read physical pages20,62,79,127. Current archive shell only; 2024 sustainability and ARCI2025 paper inaccessible through web.", "languages": [ "English" ], "searchCompleteness": "Bounded non-exhaustive; nine-theme lens applied to retained passages, not nine independent searches.", "reviewedSourceIds": [ "ENE-S03" ] }, "audit": { "eligibleClaims": 2, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2025", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": 1, "winningClaimIds": [ "SEC01" ], "contribution": 3.7037 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 2, "winningClaimIds": [ "SEC02" ], "contribution": 7.4074 } ], "singleSourceRemoval": [ { "url": "https://www.se.com.sa/-/media/sec/Investors/Saudi-Electricity-Company-Annual-Report-2024_Eng_V21.ashx", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 11.11, "rank": 6 }, { "scenario": "implemented-only", "score": 7.41, "rank": 6 }, { "scenario": "detail-cap", "score": 11.11, "rank": 6 }, { "scenario": "ai-core-0.5", "score": 11.76, "rank": 5 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 7 }, { "scenario": "workforce-0.5", "score": 9.8, "rank": 7 }, { "scenario": "workforce-2", "score": 13.33, "rank": 6 }, { "scenario": "ecosystem-0.5", "score": 11.76, "rank": 6 }, { "scenario": "ecosystem-2", "score": 10.0, "rank": 7 }, { "scenario": "interfaces-0.5", "score": 11.76, "rank": 6 }, { "scenario": "interfaces-2", "score": 10.0, "rank": 7 }, { "scenario": "lineage-0.5", "score": 11.76, "rank": 6 }, { "scenario": "lineage-2", "score": 10.0, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 11.76, "rank": 6 }, { "scenario": "experimentation-2", "score": 10.0, "rank": 6 }, { "scenario": "autonomy-0.5", "score": 11.76, "rank": 6 }, { "scenario": "autonomy-2", "score": 10.0, "rank": 6 }, { "scenario": "knowledge-0.5", "score": 11.76, "rank": 6 }, { "scenario": "knowledge-2", "score": 10.0, "rank": 6 }, { "scenario": "governance-0.5", "score": 7.84, "rank": 7 }, { "scenario": "governance-2", "score": 16.67, "rank": 5 } ], "scenarioRankRange": [ 5, 7 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "kahramaa", "name": "KAHRAMAA", "country": "Qatar", "sector": "Energy & Utilities", "scope": "Qatar General Electricity and Water Corporation; public utility corporation, not a listed company.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "kahramaa", "evidence": [ { "id": "KAHRAMAA01", "companyId": "kahramaa", "sourceId": "ENE-S09", "dimension": "ai-core", "claim": "KAHRAMAA launched an AI/cloud platform with planned consumption-pattern analysis for operational planning.", "url": "https://km.qa/MediaCenter/Pages/NewsDetails.aspx?ItemID=431", "originalSourceUrl": "https://km.qa/MediaCenter/Pages/NewsDetails.aspx?ItemID=431", "sourceTitle": "KAHRAMAA Launches AI Platform, in Collaboration with Microsoft and KPMG", "corroboratingSourceIds": [], "publisher": "KAHRAMAA", "sourceType": "corporate-release", "publicationDate": "2023-03-08", "accessedDate": "2026-10-10", "locator": "Body paragraphs1 and final technical paragraph", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Qatar General Electricity and Water Corporation; public utility corporation, not a listed company.. Source predominantly future-tense about use; production workload not demonstrated;2023 historical. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named launched platform and specific intended utility application.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "67a822d42ce44ff0eba7a8f84db8183f55051506912d558456f38050686558cf", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "KAHRAMAA02", "companyId": "kahramaa", "sourceId": "ENE-S19", "dimension": "knowledge", "claim": "Indexed2025 Google Cloud initiative mentions Gemini and NotebookLM for enterprise knowledge.", "url": "https://www.km.qa/MediaCenter/Pages/NewsDetails.aspx?ItemID=503", "originalSourceUrl": "https://www.km.qa/MediaCenter/Pages/NewsDetails.aspx?ItemID=503", "sourceTitle": "KAHRAMAA Google Cloud initiatives", "corroboratingSourceIds": [], "publisher": "KAHRAMAA", "sourceType": "corporate-release", "publicationDate": "2025-12-11", "accessedDate": "2026-10-10", "locator": "Indexed lead only", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Qatar General Electricity and Water Corporation; public utility corporation, not a listed company.. Full source unavailable; programme announcement also insufficient for deployed status. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "snippet-only", "rationale": "Unscored lead only. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "c377ce19268e4be083d1cc125428481871f81e40bd5d39b3371ff868f0efa956", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 1, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 1, "score": 4, "coverage": 1, "rank": 8, "sourceCount": 2, "searchBoundary": { "companyId": "kahramaa", "date": "2026-10-10", "queries": [ "site:km.qa artificial intelligence platform Microsoft Google", "site:km.qa \"AI\" \"governance\"", "\"KAHRAMAA\" \"Gemini\" site:cloud.google.com" ], "passagesChecked": "2023 platform launch body read. 2025 Google article full-access failed. Renewable forecasting tender located as procurement lead, not verified operating practice.", "languages": [ "English" ], "searchCompleteness": "Bounded non-exhaustive; nine-theme lens applied to retained passages, not nine independent searches.", "reviewedSourceIds": [ "ENE-S09" ] }, "audit": { "eligibleClaims": 1, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2023-03-08", "lineage": [ { "dimension": "ai-core", "level": 1, "winningClaimIds": [ "KAHRAMAA01" ], "contribution": 3.7037 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://km.qa/MediaCenter/Pages/NewsDetails.aspx?ItemID=431", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 3.7, "rank": 8 }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 3.7, "rank": 8 }, { "scenario": "ai-core-0.5", "score": 1.96, "rank": 8 }, { "scenario": "ai-core-2", "score": 6.67, "rank": 8 }, { "scenario": "workforce-0.5", "score": 3.92, "rank": 8 }, { "scenario": "workforce-2", "score": 3.33, "rank": 8 }, { "scenario": "ecosystem-0.5", "score": 3.92, "rank": 8 }, { "scenario": "ecosystem-2", "score": 3.33, "rank": 8 }, { "scenario": "interfaces-0.5", "score": 3.92, "rank": 8 }, { "scenario": "interfaces-2", "score": 3.33, "rank": 8 }, { "scenario": "lineage-0.5", "score": 3.92, "rank": 8 }, { "scenario": "lineage-2", "score": 3.33, "rank": 8 }, { "scenario": "experimentation-0.5", "score": 3.92, "rank": 8 }, { "scenario": "experimentation-2", "score": 3.33, "rank": 8 }, { "scenario": "autonomy-0.5", "score": 3.92, "rank": 8 }, { "scenario": "autonomy-2", "score": 3.33, "rank": 8 }, { "scenario": "knowledge-0.5", "score": 3.92, "rank": 8 }, { "scenario": "knowledge-2", "score": 3.33, "rank": 8 }, { "scenario": "governance-0.5", "score": 3.92, "rank": 8 }, { "scenario": "governance-2", "score": 3.33, "rank": 8 } ], "scenarioRankRange": [ 8, 8 ], "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "bapco-refining", "name": "Bapco Refining", "country": "Bahrain", "sector": "Energy & Utilities", "scope": "Refining subsidiary of Bapco Energies; not upstream sibling or whole group.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "bapco-refining", "evidence": [ { "id": "BAPCOR01", "companyId": "bapco-refining", "sourceId": "ENE-S17", "dimension": "ai-core", "claim": "Indexed annual-report passage describes intended AI optimization of the Bapco Modernization Project.", "url": "https://bapco-phase2.objects.frb.io/Media-Centre/Reports/Annual-Report-2024.pdf", "originalSourceUrl": "https://bapco-phase2.objects.frb.io/Media-Centre/Reports/Annual-Report-2024.pdf", "sourceTitle": "Bapco Energies Annual Report 2024", "corroboratingSourceIds": [], "publisher": "Bapco Energies", "sourceType": "annual-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "Printed28–29 indexed lead", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Refining subsidiary of Bapco Energies; not upstream sibling or whole group.. Public report download truncated; only indexed passage read. Future language is not deployment. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "inaccessible", "rationale": "Excluded pending full-document access. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "e755a4da35f61e26e4be463fa508c578e0e1ed5fa4ee53aa0e3c6fd8bbf487f8", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "BAPCOR02", "companyId": "bapco-refining", "sourceId": "ENE-S21", "dimension": "ecosystem", "claim": "Indexed tender requests engineering services covering APC, RTO and AI applications.", "url": "https://www.tenderboard.gov.bh/tenderdetails/?id=745%2F2025%2FBTB+%28T88046+%2861%29%29", "originalSourceUrl": "https://www.tenderboard.gov.bh/tenderdetails/?id=745%2F2025%2FBTB+%28T88046+%2861%29%29", "sourceTitle": "Tender745/2025/BTB T88046(61): APC RTO and AI engineering", "corroboratingSourceIds": [], "publisher": "Bahrain Tender Board", "sourceType": "official-tender", "publicationDate": "2025-12-08", "accessedDate": "2026-10-10", "locator": "Indexed tender description", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Refining subsidiary of Bapco Energies; not upstream sibling or whole group.. Tender is not award or deployment; fetched page inaccessible. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "snippet-only", "rationale": "Excluded pending full-source read. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "8c13d62f8b7641b9ab36f08b07265ee02e640b0b4c772b79f7de04b3f72f7507", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 2, "searchBoundary": { "companyId": "bapco-refining", "date": "2026-10-10", "queries": [ "site:bapco.net \"artificial intelligence\"", "site:bapcoenergies.com AI refining2025 2024", "Bapco Refining AI APC RTO tender2025" ], "passagesChecked": "Group annual report web exceeded20MB; local transfer truncated and failed parsing. Tender inaccessible. Indexed descriptions excluded; refining archive only a discovery source.", "languages": [ "English" ], "searchCompleteness": "Bounded non-exhaustive; nine-theme lens applied to retained passages, not nine independent searches.", "reviewedSourceIds": [] }, "audit": { "eligibleClaims": 0, "excludedClaims": 2, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "knpc", "name": "Kuwait National Petroleum Company", "country": "Kuwait", "sector": "Energy & Utilities", "scope": "KNPC operating company, a KPC subsidiary and sibling of KOC.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "knpc", "evidence": [ { "id": "KNPC01", "companyId": "knpc", "sourceId": "ENE-S14", "dimension": "ai-core", "claim": "KNPC magazine describes planned AI predictive maintenance through a Maximo7.6 upgrade.", "url": "https://www.knpc.com/knpc/media/pdf/tech-magazine-1_1.pdf", "originalSourceUrl": "https://www.knpc.com/knpc/media/pdf/tech-magazine-1_1.pdf", "sourceTitle": "KNPC Technology Magazine, issue1", "corroboratingSourceIds": [], "publisher": "KNPC", "sourceType": "company-magazine", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF8; lines324–329", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: KNPC operating company, a KPC subsidiary and sibling of KOC.. Publication date unclear; nearby charts reference2018. Current AI implementation unproven; do not treat deployed Maximo as deployed AI. Application: internal-operating-practice. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "access": "full-source-read", "rationale": "Historical lead retained without scoring. Exclusion: Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "20b0d95ff13a66c283897728c880d55b8a11134d9da5033a44cbeae819287968", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": { "companyId": "knpc", "date": "2026-10-10", "queries": [ "site:knpc.com \"artificial intelligence\" deployed OR implemented OR training", "site:knpc.com AI artificial intelligence digital2025 2024", "\"Kuwait National Petroleum\" \"AI\" site:microsoft.com" ], "passagesChecked": "Magazine mobile-rounds/Maximo passages read; nearby2018 charts suggest historical context. Annual2024–25 card located but no download in retrieved page; HTML retrieval connection refused.", "languages": [ "English" ], "searchCompleteness": "Bounded non-exhaustive; nine-theme lens applied to retained passages, not nine independent searches.", "reviewedSourceIds": [ "ENE-S14", "ENE-S18" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-purehealth", "name": "PureHealth", "country": "UAE", "sector": "Healthcare", "scope": "PureHealth Holding PJSC consolidated group; includes Daman insurance and overseas HHG, not a UAE hospital-only measure.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-purehealth", "evidence": [ { "id": "HLT-016", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "ai-core", "claim": "PureHealth’s AI Factory became operational inQ3 2025 and supports document, radiology, pharmacy and claims workflows.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF40–41/printed76–79, AI Factory", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: PureHealth Holding PJSC consolidated group; includes Daman insurance and overseas HHG, not a UAE hospital-only measure.. Group architecture assertion does not imply uniform adoption across all acquired entities. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Operational timing, initial platforms and cross-function deployment are specified. Uniform group-wide adoption is not inferred.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "e8626696da3dc4169377b04da7800e9969834a448a97c1ec4ad9bdf5d145ee78", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-017", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "interfaces", "claim": "Daman deployed in-house AI classification, validation and extraction for documents.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF39/printed74–75, Digital Transformation", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: PureHealth subsidiary Daman health-insurance operations, UAE.. Insurance subsidiary scope; annual60million figure is issuer-reported and not an external performance audit. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Daman document classification, validation and extraction are deployed with reported annual processing scope; insurance subsidiary only.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "cadfc8be4fa6e6cd9a447bd3be1ef43a7ed0d307a3e134b36027fca40d7bc601", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-018", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "ai-core", "claim": "SEHA Clinics rolled out AI retinal screening after a pilot and introduced AI radiology.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF31/printed58–59, Digital Transformation", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: PureHealth SEHA Clinics, UAE; retinal and screening workflows only.. 1.4million annual visa screenings is programme denominator, not proven count processed by AI. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Explicit transition from pilot to clinical rollout.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "d773f8ef130654cb5409883507f026e373f7c4fe438e332c72d7ca7d6457f926", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-019", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "workforce", "claim": "HHG uses machine-learning-based nurse scheduling.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF36/printed68–69, HHG digital adoption", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: PureHealth international HHG hospital operations in Greece/Cyprus; not UAE hospitals.. No participation, staffing-outcome or fairness evaluation disclosed; overseas scope. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Implemented AI changes staff scheduling.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "04e6a2894672343c71c390d0068f8e067786cfdc6a85ab95f93bfc8b3497ed93", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-020", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "knowledge", "claim": "HHG executive dashboards include AI-generated insights.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF36/printed68–69", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: PureHealth HHG international operations; not group-wide dashboard adoption.. No retrieval provenance, evaluation or decision-impact evidence; overseas scope. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Explicit internal management information practice.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "17b1fcc4f4f81ffe94de3b833e16ce88c98d40a419d0170e3f5f6bd81dc63f28", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-021", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "experimentation", "claim": "SEHA piloted AI-assisted physician documentation.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF29/printed54–55, Digital Transformation", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: PureHealth Holding PJSC consolidated group; includes Daman insurance and overseas HHG, not a UAE hospital-only measure.. A pilot remains1; group AI Factory deployment cannot upgrade this specific workflow. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Explicit bounded clinical pilot.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "fa9aef61a92b5a14360e32a41b591d79f2e0b4f8148312d82711fcc257a29044", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-022", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "governance", "claim": "PureCS names a dedicated AI Office as a2026 priority.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF41/printed78–79, Outlook for2026", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: PureHealth Holding PJSC consolidated group; includes Daman insurance and overseas HHG, not a UAE hospital-only measure.. Publication plans do not establish that the office was operating at the cutoff. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Dedicated AI Office is stated in 2026 priorities, not established operating governance.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "da1daf63460d5a60411f46caa59220dc0e51d6e0100506f4621341621ce8e53d", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-023", "companyId": "health-purehealth", "sourceId": "HLT-S05", "dimension": "lineage", "claim": "PureCS describes AI infrastructure decisions reflecting data residency and regulatory requirements.", "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "originalSourceUrl": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "sourceTitle": "Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "PureHealth", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF40/printed76–77, AI Factory", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: PureHealth Holding PJSC consolidated group; includes Daman insurance and overseas HHG, not a UAE hospital-only measure.. Excluded: sovereign infrastructure does not establish traceable training sources, model versions or AI-output lineage. Application: excluded-or-scope-unresolved. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Infrastructure residency rationale does not establish data lineage or traceability. Preserve raw exclusion. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 1, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "39cd5d611f3cc21ea082111885699f5a35fb3bd44fc9dcad181bc69811eeb12d", "review": { "decision": "exclude", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 2, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": 1, "autonomy": null, "knowledge": 2, "governance": 1 }, "points": 12, "score": 44, "coverage": 6, "rank": 1, "sourceCount": 1, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 7, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 7, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "HLT-016" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 2, "winningClaimIds": [ "HLT-019" ], "contribution": 7.4074 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "HLT-017" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "HLT-021" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 2, "winningClaimIds": [ "HLT-020" ], "contribution": 7.4074 }, { "dimension": "governance", "level": 1, "winningClaimIds": [ "HLT-022" ], "contribution": 3.7037 } ], "singleSourceRemoval": [ { "url": "https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 44.44, "rank": 1 }, { "scenario": "implemented-only", "score": 37.04, "rank": 1 }, { "scenario": "detail-cap", "score": 37.04, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 41.18, "rank": 1 }, { "scenario": "ai-core-2", "score": 50.0, "rank": 1 }, { "scenario": "workforce-0.5", "score": 43.14, "rank": 1 }, { "scenario": "workforce-2", "score": 46.67, "rank": 1 }, { "scenario": "ecosystem-0.5", "score": 47.06, "rank": 1 }, { "scenario": "ecosystem-2", "score": 40.0, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 41.18, "rank": 1 }, { "scenario": "interfaces-2", "score": 50.0, "rank": 1 }, { "scenario": "lineage-0.5", "score": 47.06, "rank": 1 }, { "scenario": "lineage-2", "score": 40.0, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 45.1, "rank": 1 }, { "scenario": "experimentation-2", "score": 43.33, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 47.06, "rank": 1 }, { "scenario": "autonomy-2", "score": 40.0, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 43.14, "rank": 1 }, { "scenario": "knowledge-2", "score": 46.67, "rank": 1 }, { "scenario": "governance-0.5", "score": 45.1, "rank": 1 }, { "scenario": "governance-2", "score": 43.33, "rank": 1 } ], "scenarioRankRange": [ 1, 2 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-dallah", "name": "Dallah Healthcare", "country": "Saudi Arabia", "sector": "Healthcare", "scope": "Listed Dallah Healthcare Company and hospital operations in Saudi Arabia.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-dallah", "evidence": [ { "id": "HLT-006", "companyId": "health-dallah", "sourceId": "HLT-S02", "dimension": "interfaces", "claim": "Dallah reports Sahl ambient AI documenting patient encounters for more than175 clinicians.", "url": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "originalSourceUrl": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Dallah Healthcare", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF24/printed46–47, Investing in responsible AI", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed Dallah Healthcare Company and hospital operations in Saudi Arabia.. No denominator of eligible clinicians; provider-reported scale, not independently verified. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Ambient documentation is explicitly in use by a stated clinician population. Reported time savings are not independently verified.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "834c471049cc7ed1387a938cfe52dd636a152bdff686767d01c403e448735c0a", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-007", "companyId": "health-dallah", "sourceId": "HLT-S02", "dimension": "ai-core", "claim": "Dallah deployed Thakaa Med machine learning to detect oral pathologies in radiographs.", "url": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "originalSourceUrl": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Dallah Healthcare", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF24/printed46–47", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed Dallah Healthcare Company and hospital operations in Saudi Arabia.. Sensitivity, validation dataset, override rates and deployment-site count not reported here. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Deployment and machine-learning analysis of dental radiographs are explicit. No clinical-outcome inference.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "51201eea889d27fc7e1bff4a5b5a134e298be8bf806976f7db217da3bdc52f17", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-008", "companyId": "health-dallah", "sourceId": "HLT-S02", "dimension": "governance", "claim": "Dallah applies a Responsible AI framework to design, validate and operationalise clinical and administrative AI.", "url": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "originalSourceUrl": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Dallah Healthcare", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF24/printed46–47", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Listed Dallah Healthcare Company and hospital operations in Saudi Arabia.. No approval gates, named accountable owner, monitoring thresholds or incident record disclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Applied AI-specific framework is explicit, while concrete authority or control procedures remain unspecified.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "6b0aafad7c9f1d414aabfc3586a01f15e5b35a6f0ea1634105a348f68d46599b", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-009", "companyId": "health-dallah", "sourceId": "HLT-S02", "dimension": "workforce", "claim": "Dallah describes training and certification in AI, analytics and digital operations across hospitals and corporate functions.", "url": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "originalSourceUrl": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Dallah Healthcare", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF26/printed50–51, skills paragraph", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Listed Dallah Healthcare Company and hospital operations in Saudi Arabia.. No AI-specific participation denominator, completion assessment or role coverage. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "AI training appears under Looking forward, without a completed programme or unambiguous current operation. Raw deployed status overstates the commitment.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "7d37fc7b4f75e169fd7ee83b7d85e3958759d844691097453908a7a70041a85c", "review": { "decision": "downgrade", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-010", "companyId": "health-dallah", "sourceId": "HLT-S02", "dimension": "ecosystem", "claim": "Dallah identifies Sahl AI and Thakaa Med as clinical AI implementation partners.", "url": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "originalSourceUrl": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Dallah Healthcare", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF26/printed50–51, strategic partnerships", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Listed Dallah Healthcare Company and hospital operations in Saudi Arabia.. Counts only partnership evidence; no independent ecosystem quality metric. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Named partners linked to implemented hospital workflows.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "67eeb917f0b6a59c9f1090bd2ff393b49c908a79a7b00616cc6645203726f2f1", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-011", "companyId": "health-dallah", "sourceId": "HLT-S14", "dimension": "ai-core", "claim": "Dallah’s2024 dentistry account reports successful AI implementation in the dental department.", "url": "https://www.dallah-hospital.com/english/media-center/news--events/artificial-intelligence-in-dentistry-enhancing-patient-care-and-service-quality", "originalSourceUrl": "https://www.dallah-hospital.com/english/media-center/news--events/artificial-intelligence-in-dentistry-enhancing-patient-care-and-service-quality", "sourceTitle": "Artificial Intelligence in Dentistry: Enhancing Patient Care and Service Quality", "corroboratingSourceIds": [], "publisher": "Dallah Hospital", "sourceType": "official-implementation-account", "publicationDate": "2024-12-30", "accessedDate": "2026-10-10", "locator": "Successful implementation opening paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Listed Dallah Healthcare Company and hospital operations in Saudi Arabia.. Subsequent general descriptions of what AI can do are not treated as verified local functionality. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Explicit deployment statement.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "126660f5285d138365d8be31e532ca3369793559b86b32723962e659bec8a2fe", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 1, "ecosystem": 2, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": 2 }, "points": 11, "score": 41, "coverage": 5, "rank": 2, "sourceCount": 2, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 6, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 5, "latestSourceDate": "2024-12-30", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "HLT-007" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 1, "winningClaimIds": [ "HLT-009" ], "contribution": 3.7037 }, { "dimension": "ecosystem", "level": 2, "winningClaimIds": [ "HLT-010" ], "contribution": 7.4074 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "HLT-006" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 2, "winningClaimIds": [ "HLT-008" ], "contribution": 7.4074 } ], "singleSourceRemoval": [ { "url": "https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf", "scoreWithoutSource": 7.41 }, { "url": "https://www.dallah-hospital.com/english/media-center/news--events/artificial-intelligence-in-dentistry-enhancing-patient-care-and-service-quality", "scoreWithoutSource": 40.74 } ], "scenarioResults": [ { "scenario": "baseline", "score": 40.74, "rank": 2 }, { "scenario": "implemented-only", "score": 37.04, "rank": 1 }, { "scenario": "detail-cap", "score": 33.33, "rank": 2 }, { "scenario": "ai-core-0.5", "score": 37.25, "rank": 2 }, { "scenario": "ai-core-2", "score": 46.67, "rank": 2 }, { "scenario": "workforce-0.5", "score": 41.18, "rank": 2 }, { "scenario": "workforce-2", "score": 40.0, "rank": 2 }, { "scenario": "ecosystem-0.5", "score": 39.22, "rank": 2 }, { "scenario": "ecosystem-2", "score": 43.33, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 37.25, "rank": 2 }, { "scenario": "interfaces-2", "score": 46.67, "rank": 2 }, { "scenario": "lineage-0.5", "score": 43.14, "rank": 2 }, { "scenario": "lineage-2", "score": 36.67, "rank": 2 }, { "scenario": "experimentation-0.5", "score": 43.14, "rank": 2 }, { "scenario": "experimentation-2", "score": 36.67, "rank": 2 }, { "scenario": "autonomy-0.5", "score": 43.14, "rank": 2 }, { "scenario": "autonomy-2", "score": 36.67, "rank": 2 }, { "scenario": "knowledge-0.5", "score": 43.14, "rank": 1 }, { "scenario": "knowledge-2", "score": 36.67, "rank": 2 }, { "scenario": "governance-0.5", "score": 39.22, "rank": 2 }, { "scenario": "governance-2", "score": 43.33, "rank": 1 } ], "scenarioRankRange": [ 1, 2 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-hmg", "name": "Dr. Sulaiman Al Habib Medical Services Group", "country": "Saudi Arabia", "sector": "Healthcare", "scope": "Listed healthcare group; HMG hospital workflows distinguished from Cloud Solutions sales to third parties.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-hmg", "evidence": [ { "id": "HLT-001", "companyId": "health-hmg", "sourceId": "HLT-S01", "dimension": "ai-core", "claim": "HMG reports integrated cardiac-imaging AI for automated analysis and plaque characterisation.", "url": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "originalSourceUrl": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "HMG", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF51/printed99–100, Radiology Services", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed healthcare group; HMG hospital workflows distinguished from Cloud Solutions sales to third parties.. No model accuracy audit or hospital-by-hospital coverage; AI is clinician support. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Cardiac imaging software is integrated and its analysis mechanism specified. Clinical support scope only.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "23892d347bcad06a5c0599f11d28df65a19fbacb0308d950df8c1de353df7020", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-002", "companyId": "health-hmg", "sourceId": "HLT-S01", "dimension": "governance", "claim": "HMG reports a Responsible AI Framework with clinician final authority, security requirements and fairness monitoring.", "url": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "originalSourceUrl": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "HMG", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF39/printed75–76, Applying Responsible AI", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed healthcare group; HMG hospital workflows distinguished from Cloud Solutions sales to third parties.. Framework asserted by issuer; thresholds, incidents and independent compliance results unavailable. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The applied AI framework explicitly retains clinician final authority and describes monitoring. This is issuer disclosure, not control testing.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "fe3a607b47447f90adb1e7b0087a034943a85930ddd366fa16dd9b6544320cdd", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-003", "companyId": "health-hmg", "sourceId": "HLT-S01", "dimension": "interfaces", "claim": "HMG says clinical note-documentation tools launched in2025 support record management.", "url": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "originalSourceUrl": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "HMG", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF39/printed75–76", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Listed healthcare group; HMG hospital workflows distinguished from Cloud Solutions sales to third parties.. Do not assume every hospital or clinician uses the tool. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The 2025 launch of documentation tools is stated in the AI implementation section; detailed uptake is not given.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "915f647819df0897d0d8c1d27756f7f0ab59bca12567f90620f131a69ad324cb", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-004", "companyId": "health-hmg", "sourceId": "HLT-S01", "dimension": "ecosystem", "claim": "HMG established an AI-company partnership for development, clinical research and scientific publication.", "url": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "originalSourceUrl": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "sourceTitle": "Annual Report 2025", "corroboratingSourceIds": [], "publisher": "HMG", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF51/printed99–100, Partnerships", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Listed healthcare group; HMG hospital workflows distinguished from Cloud Solutions sales to third parties.. Partner is unnamed; no programme coverage or evaluation detail sufficient for3. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "An AI partnership is established for intended development and research; completed joint implementation is not demonstrated. Raw deployed status overstates this commitment.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "c83ff0610e2862bf38bcc8436739c2b12b0bdd3539c3092e8b25fdc02fba5197", "review": { "decision": "downgrade", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-005", "companyId": "health-hmg", "sourceId": "HLT-S13", "dimension": "ai-core", "claim": "HMG’s2024 sustainability account identifies internal AI use in HR, legal, administration and radiology.", "url": "https://hmg.com/ir/ESGReports/2024/maintain-sound-business-practices.html", "originalSourceUrl": "https://hmg.com/ir/ESGReports/2024/maintain-sound-business-practices.html", "sourceTitle": "Sustainability2024: Maintain sound business practices", "corroboratingSourceIds": [], "publisher": "HMG", "sourceType": "sustainability-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Innovation and technology, AI applications paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Listed healthcare group; HMG hospital workflows distinguished from Cloud Solutions sales to third parties.. Corroborating earlier disclosure; functions alone do not establish workforce skills or governance. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Explicit internal implementation, but broad disclosure.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "31d1669d5e6f88290045e3b51661b2491a1f3a9728bb246480c72b77ed3497ae", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 1, "interfaces": 2, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": 3 }, "points": 9, "score": 33, "coverage": 4, "rank": 3, "sourceCount": 2, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 5, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 5, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "HLT-001" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "HLT-004" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "HLT-003" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "HLT-002" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf", "scoreWithoutSource": 7.41 }, { "url": "https://hmg.com/ir/ESGReports/2024/maintain-sound-business-practices.html", "scoreWithoutSource": 33.33 } ], "scenarioResults": [ { "scenario": "baseline", "score": 33.33, "rank": 3 }, { "scenario": "implemented-only", "score": 29.63, "rank": 3 }, { "scenario": "detail-cap", "score": 25.93, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 29.41, "rank": 3 }, { "scenario": "ai-core-2", "score": 40.0, "rank": 3 }, { "scenario": "workforce-0.5", "score": 35.29, "rank": 3 }, { "scenario": "workforce-2", "score": 30.0, "rank": 4 }, { "scenario": "ecosystem-0.5", "score": 33.33, "rank": 3 }, { "scenario": "ecosystem-2", "score": 33.33, "rank": 3 }, { "scenario": "interfaces-0.5", "score": 31.37, "rank": 3 }, { "scenario": "interfaces-2", "score": 36.67, "rank": 3 }, { "scenario": "lineage-0.5", "score": 35.29, "rank": 3 }, { "scenario": "lineage-2", "score": 30.0, "rank": 3 }, { "scenario": "experimentation-0.5", "score": 35.29, "rank": 3 }, { "scenario": "experimentation-2", "score": 30.0, "rank": 4 }, { "scenario": "autonomy-0.5", "score": 35.29, "rank": 3 }, { "scenario": "autonomy-2", "score": 30.0, "rank": 3 }, { "scenario": "knowledge-0.5", "score": 35.29, "rank": 3 }, { "scenario": "knowledge-2", "score": 30.0, "rank": 3 }, { "scenario": "governance-0.5", "score": 29.41, "rank": 3 }, { "scenario": "governance-2", "score": 40.0, "rank": 3 } ], "scenarioRankRange": [ 3, 4 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-hmc", "name": "Hamad Medical Corporation", "country": "Qatar", "sector": "Healthcare", "scope": "Public healthcare provider; clinical imaging departmental evidence, not all Qatar healthcare.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-hmc", "evidence": [ { "id": "HLT-024", "companyId": "health-hmc", "sourceId": "HLT-S06", "dimension": "governance", "claim": "HMC’s clinical-imaging AI committee sets review strategy, monitors implementation and assesses controls.", "url": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "originalSourceUrl": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "sourceTitle": "Precision Medicine Annual Report 2021–2022", "corroboratingSourceIds": [], "publisher": "Hamad Medical Corporation", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF39/printed37, CIPAC", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Public healthcare provider; clinical imaging departmental evidence, not all Qatar healthcare.. Historical2021–22 department-level evidence; continuation to2026 unverified, not current enterprise-wide assurance. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The formed clinical-imaging committee has review and monitoring duties. Evidence refers to 2021–22 and does not establish current operation.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "603efd3a0ea3a48c2043540469a7bb07eb36feac0bcb789efd25aa47bb681928", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-025", "companyId": "health-hmc", "sourceId": "HLT-S06", "dimension": "workforce", "claim": "Clinical imaging arranged repeated AI educational sessions and demonstrations.", "url": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "originalSourceUrl": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "sourceTitle": "Precision Medicine Annual Report 2021–2022", "corroboratingSourceIds": [], "publisher": "Hamad Medical Corporation", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF39/printed37, Educational sessions and Demos", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Public healthcare provider; clinical imaging departmental evidence, not all Qatar healthcare.. Historical departmental scope; no assessed competence or participant count. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Repeated AI education is described for the historical clinical-imaging department; no current continuation inferred.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "20f384937fd5a712cd0208e5718ed20eb9f28e56a491b958c68424a50539d930", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-026", "companyId": "health-hmc", "sourceId": "HLT-S06", "dimension": "experimentation", "claim": "HMC trialled Siemens AI-Rad and Koios breast imaging, integrating tools with imaging systems to obtain radiologist feedback.", "url": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "originalSourceUrl": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "sourceTitle": "Precision Medicine Annual Report 2021–2022", "corroboratingSourceIds": [], "publisher": "Hamad Medical Corporation", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF39/printed37, Trial based AI integrated solution", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Public healthcare provider; clinical imaging departmental evidence, not all Qatar healthcare.. Technical integration does not prove routine deployment or current2026 use. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Both imaging integrations are expressly trial-based, including the tool still in use. Pilot anchor remains appropriate.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "d7c871eb25630fbba9b36786e03838bcb0cf2700038c7e8bbcaa5728a3380ce5", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-027", "companyId": "health-hmc", "sourceId": "HLT-S06", "dimension": "ai-core", "claim": "HMC describes AI-embedded cardiac-image post-processing at Heart Hospital.", "url": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "originalSourceUrl": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "sourceTitle": "Precision Medicine Annual Report 2021–2022", "corroboratingSourceIds": [], "publisher": "Hamad Medical Corporation", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF57/printed55, Cardiac Imaging Post Processing", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: HMC Heart Hospital clinical imaging, as described in2021–22 report.. Historical account; no scale, operating evaluation or2026 recency confirmation. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Existing clinical imaging practice explicitly labelled AI.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "6ef29a274068888e9b4f6f373cc27301c8bfa35c11ec1dff5a530088c3c687b9", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 2, "workforce": 2, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": 1, "autonomy": null, "knowledge": null, "governance": 3 }, "points": 8, "score": 30, "coverage": 4, "rank": 4, "sourceCount": 1, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 4, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 4, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": 2, "winningClaimIds": [ "HLT-027" ], "contribution": 7.4074 }, { "dimension": "workforce", "level": 2, "winningClaimIds": [ "HLT-025" ], "contribution": 7.4074 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "HLT-026" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "HLT-024" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 29.63, "rank": 4 }, { "scenario": "implemented-only", "score": 25.93, "rank": 4 }, { "scenario": "detail-cap", "score": 25.93, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 27.45, "rank": 4 }, { "scenario": "ai-core-2", "score": 33.33, "rank": 4 }, { "scenario": "workforce-0.5", "score": 27.45, "rank": 4 }, { "scenario": "workforce-2", "score": 33.33, "rank": 3 }, { "scenario": "ecosystem-0.5", "score": 31.37, "rank": 4 }, { "scenario": "ecosystem-2", "score": 26.67, "rank": 4 }, { "scenario": "interfaces-0.5", "score": 31.37, "rank": 3 }, { "scenario": "interfaces-2", "score": 26.67, "rank": 4 }, { "scenario": "lineage-0.5", "score": 31.37, "rank": 4 }, { "scenario": "lineage-2", "score": 26.67, "rank": 4 }, { "scenario": "experimentation-0.5", "score": 29.41, "rank": 4 }, { "scenario": "experimentation-2", "score": 30.0, "rank": 4 }, { "scenario": "autonomy-0.5", "score": 31.37, "rank": 4 }, { "scenario": "autonomy-2", "score": 26.67, "rank": 4 }, { "scenario": "knowledge-0.5", "score": 31.37, "rank": 4 }, { "scenario": "knowledge-2", "score": 26.67, "rank": 4 }, { "scenario": "governance-0.5", "score": 25.49, "rank": 5 }, { "scenario": "governance-2", "score": 36.67, "rank": 4 } ], "scenarioRankRange": [ 3, 5 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-m42", "name": "M42", "country": "UAE", "sector": "Healthcare", "scope": "Abu Dhabi-headquartered international healthcare group; cases identify Capital Health Screening Centre or other operating subsidiaries.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-m42", "evidence": [ { "id": "HLT-012", "companyId": "health-m42", "sourceId": "HLT-S03", "dimension": "ai-core", "claim": "M42 reports AIRIS-TB conducting2,000 AI chest X-ray examinations daily.", "url": "https://m42.ae/media-resources/news/m42-and-ge-healthcare-join-forces-to-advance-ai-enabled-patient-care/", "originalSourceUrl": "https://m42.ae/media-resources/news/m42-and-ge-healthcare-join-forces-to-advance-ai-enabled-patient-care/", "sourceTitle": "M42 and GE HealthCare join forces to advance AI-Enabled Patient Care", "corroboratingSourceIds": [], "publisher": "M42", "sourceType": "official-implementation-account", "publicationDate": "2025-07-08", "accessedDate": "2026-10-10", "locator": "Final AIRIS-TB paragraph, line69", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: M42 AIRIS-TB screening operation; Capital Health Screening Centre context from separate study account.. Screening programme scope; not all M42 services. Conventional comparator and period not fully described. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "AIRIS-TB is reported operating with daily screening coverage. This does not establish the new GE partnership has deployed.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "3d6a7504753ae6e82ddae451a257bf95e211313e4f69fd040c8b9be2d6508910", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-013", "companyId": "health-m42", "sourceId": "HLT-S04", "dimension": "experimentation", "claim": "M42 reports AIRIS-TB validation using more than1million screening radiographs and demographic performance checks.", "url": "https://m42.ae/media-resources/news/m42-announces-breakthrough-results-for-its-ai-powered-tuberculosis-screening/", "originalSourceUrl": "https://m42.ae/media-resources/news/m42-announces-breakthrough-results-for-its-ai-powered-tuberculosis-screening/", "sourceTitle": "M42 announces breakthrough results for its AI-powered tuberculosis screening", "corroboratingSourceIds": [], "publisher": "M42", "sourceType": "official-implementation-account", "publicationDate": "2025-08-06", "accessedDate": "2026-10-10", "locator": "Study design, results and demographic consistency paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Abu Dhabi-headquartered international healthcare group; cases identify Capital Health Screening Centre or other operating subsidiaries.. Company describes research results; benchmark performance is not a clinical outcome or causal productivity result. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Completed evaluation scale and demographic checks are described for the named model. This review verifies the issuer account, not clinical findings.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "2ec2c377dd65986fbe6fa0fda7a9f532f8fec78bb84dc640de3dc63d453c1d27", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-014", "companyId": "health-m42", "sourceId": "HLT-S03", "dimension": "ecosystem", "claim": "M42 and GE HealthCare announced co-development of AI-enabled diagnostic and operational solutions.", "url": "https://m42.ae/media-resources/news/m42-and-ge-healthcare-join-forces-to-advance-ai-enabled-patient-care/", "originalSourceUrl": "https://m42.ae/media-resources/news/m42-and-ge-healthcare-join-forces-to-advance-ai-enabled-patient-care/", "sourceTitle": "M42 and GE HealthCare join forces to advance AI-Enabled Patient Care", "corroboratingSourceIds": [], "publisher": "M42", "sourceType": "official-implementation-account", "publicationDate": "2025-07-08", "accessedDate": "2026-10-10", "locator": "Collaboration paragraphs59–67", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Abu Dhabi-headquartered international healthcare group; cases identify Capital Health Screening Centre or other operating subsidiaries.. Existing AIRIS-TB does not prove the new GE collaboration has deployed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Co-development is announced in future language; retain commitment anchor only.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "34629017da52fc42e42fd3bb1ad2772de8c20b6d05655f28c80c0f527a4ae9c1", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-015", "companyId": "health-m42", "sourceId": "HLT-S04", "dimension": "autonomy", "claim": "The AIRIS-TB study estimates potential automation of80% of screening work.", "url": "https://m42.ae/media-resources/news/m42-announces-breakthrough-results-for-its-ai-powered-tuberculosis-screening/", "originalSourceUrl": "https://m42.ae/media-resources/news/m42-announces-breakthrough-results-for-its-ai-powered-tuberculosis-screening/", "sourceTitle": "M42 announces breakthrough results for its AI-powered tuberculosis screening", "corroboratingSourceIds": [], "publisher": "M42", "sourceType": "official-implementation-account", "publicationDate": "2025-08-06", "accessedDate": "2026-10-10", "locator": "Automation potential paragraph", "status": "planned", "level": null, "eligible": false, "limitation": "Claim scope: Abu Dhabi-headquartered international healthcare group; cases identify Capital Health Screening Centre or other operating subsidiaries.. Excluded: potential workload automation does not demonstrate an operating autonomy mechanism. Application: excluded-or-scope-unresolved. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Potential workload automation does not establish implemented decision authority. Preserve raw exclusion. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 1, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "f76c11719a4cf46c705bdaed183d42bdfc7d7d8e9fb96239cc482525b1ba13e7", "review": { "decision": "exclude", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 1, "interfaces": null, "lineage": null, "experimentation": 3, "autonomy": null, "knowledge": null, "governance": null }, "points": 7, "score": 26, "coverage": 3, "rank": 5, "sourceCount": 2, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 3, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 0, "latestSourceDate": "2025-08-06", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "HLT-012" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "HLT-014" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 3, "winningClaimIds": [ "HLT-013" ], "contribution": 11.1111 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://m42.ae/media-resources/news/m42-and-ge-healthcare-join-forces-to-advance-ai-enabled-patient-care/", "scoreWithoutSource": 11.11 }, { "url": "https://m42.ae/media-resources/news/m42-announces-breakthrough-results-for-its-ai-powered-tuberculosis-screening/", "scoreWithoutSource": 14.81 } ], "scenarioResults": [ { "scenario": "baseline", "score": 25.93, "rank": 5 }, { "scenario": "implemented-only", "score": 22.22, "rank": 5 }, { "scenario": "detail-cap", "score": 18.52, "rank": 5 }, { "scenario": "ai-core-0.5", "score": 21.57, "rank": 5 }, { "scenario": "ai-core-2", "score": 33.33, "rank": 4 }, { "scenario": "workforce-0.5", "score": 27.45, "rank": 4 }, { "scenario": "workforce-2", "score": 23.33, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 25.49, "rank": 5 }, { "scenario": "ecosystem-2", "score": 26.67, "rank": 4 }, { "scenario": "interfaces-0.5", "score": 27.45, "rank": 5 }, { "scenario": "interfaces-2", "score": 23.33, "rank": 5 }, { "scenario": "lineage-0.5", "score": 27.45, "rank": 5 }, { "scenario": "lineage-2", "score": 23.33, "rank": 5 }, { "scenario": "experimentation-0.5", "score": 21.57, "rank": 5 }, { "scenario": "experimentation-2", "score": 33.33, "rank": 3 }, { "scenario": "autonomy-0.5", "score": 27.45, "rank": 5 }, { "scenario": "autonomy-2", "score": 23.33, "rank": 5 }, { "scenario": "knowledge-0.5", "score": 27.45, "rank": 5 }, { "scenario": "knowledge-2", "score": 23.33, "rank": 5 }, { "scenario": "governance-0.5", "score": 27.45, "rank": 4 }, { "scenario": "governance-2", "score": 23.33, "rank": 5 } ], "scenarioRankRange": [ 3, 5 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-bsh", "name": "Bahrain Specialist Hospital", "country": "Bahrain", "sector": "Healthcare", "scope": "Bahrain Specialist Hospital appointment-support workflow; supplier testimonial, not supplier-wide performance.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-bsh", "evidence": [ { "id": "HLT-031", "companyId": "health-bsh", "sourceId": "HLT-S11", "dimension": "interfaces", "claim": "A named Bahrain Specialist Hospital IT director says Voiceoc conversational AI handles close to20% of its traffic after several months.", "url": "https://customerexperience.voiceoc.com/", "originalSourceUrl": "https://customerexperience.voiceoc.com/", "sourceTitle": "Voiceoc patient interaction: Bahrain Specialist Hospital testimonial", "corroboratingSourceIds": [], "publisher": "Voiceoc", "sourceType": "supplier-case-study", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Nadim Navde testimonial, lines222–232", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Bahrain Specialist Hospital appointment-support workflow; supplier testimonial, not supplier-wide performance.. Undated supplier-hosted statement; traffic denominator and evaluation period unspecified. Supplier-wide90–95% claims are not attributed to BSH. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Named hospital director reports operating conversational AI and traffic coverage. Undated supplier-hosted testimonial, not independent corroboration.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ea76e621230014b07207173cd23478a4f0e7d1d61050dda0e0dbafc95c871283", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 3, "score": 11, "coverage": 1, "rank": 6, "sourceCount": 1, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 1, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 1, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "HLT-031" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://customerexperience.voiceoc.com/", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 11.11, "rank": 6 }, { "scenario": "implemented-only", "score": 11.11, "rank": 6 }, { "scenario": "detail-cap", "score": 7.41, "rank": 6 }, { "scenario": "ai-core-0.5", "score": 11.76, "rank": 6 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 7 }, { "scenario": "workforce-0.5", "score": 11.76, "rank": 6 }, { "scenario": "workforce-2", "score": 10.0, "rank": 6 }, { "scenario": "ecosystem-0.5", "score": 11.76, "rank": 6 }, { "scenario": "ecosystem-2", "score": 10.0, "rank": 6 }, { "scenario": "interfaces-0.5", "score": 5.88, "rank": 7 }, { "scenario": "interfaces-2", "score": 20.0, "rank": 6 }, { "scenario": "lineage-0.5", "score": 11.76, "rank": 6 }, { "scenario": "lineage-2", "score": 10.0, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 11.76, "rank": 6 }, { "scenario": "experimentation-2", "score": 10.0, "rank": 6 }, { "scenario": "autonomy-0.5", "score": 11.76, "rank": 6 }, { "scenario": "autonomy-2", "score": 10.0, "rank": 6 }, { "scenario": "knowledge-0.5", "score": 11.76, "rank": 6 }, { "scenario": "knowledge-2", "score": 10.0, "rank": 6 }, { "scenario": "governance-0.5", "score": 11.76, "rank": 6 }, { "scenario": "governance-2", "score": 10.0, "rank": 6 } ], "scenarioRankRange": [ 6, 7 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-phcc", "name": "Primary Health Care Corporation", "country": "Qatar", "sector": "Healthcare", "scope": "Qatar public primary-care provider, separate from HMC; screening programme evidence.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-phcc", "evidence": [ { "id": "HLT-028", "companyId": "health-phcc", "sourceId": "HLT-S07", "dimension": "ai-core", "claim": "PHCC says its2024 screening programme integrated AI to help radiologists interpret images.", "url": "https://www.phcc.gov.qa/media/news/2025/03/10/we-are-working-on-introducing-mandatory-annual-oral-screening-for-students---dr.-hamad-al-mudhahka", "originalSourceUrl": "https://www.phcc.gov.qa/media/news/2025/03/10/we-are-working-on-introducing-mandatory-annual-oral-screening-for-students---dr.-hamad-al-mudhahka", "sourceTitle": "We are working on introducing mandatory annual oral screening for students", "corroboratingSourceIds": [], "publisher": "PHCC", "sourceType": "official-implementation-account", "publicationDate": "2025-03-06", "accessedDate": "2026-10-10", "locator": "Breast and bowel screening paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Qatar public primary-care provider, separate from HMC; screening programme evidence.. No vendor, exact modality, AI coverage denominator, evaluation or current performance disclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Screening-programme integration helping radiologists is stated, without a detailed deployment mechanism.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "11f7d6b801ed711a78bfca13daed74642dd98b3b57a13d4bae94aeddb14440f0", "review": { "decision": "retain", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "HLT-029", "companyId": "health-phcc", "sourceId": "HLT-S08", "dimension": "workforce", "claim": "PHCC’s annual report describes a conference exploring AI and precision medicine.", "url": "https://www.phcc.gov.qa/docs/default-source/default-document-library/phcc%27s-annual-report-2024-eng-final.pdf", "originalSourceUrl": "https://www.phcc.gov.qa/docs/default-source/default-document-library/phcc%27s-annual-report-2024-eng-final.pdf", "sourceTitle": "Annual Report 2024", "corroboratingSourceIds": [], "publisher": "PHCC", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF23, conference paragraph", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Qatar public primary-care provider, separate from HMC; screening programme evidence.. Excluded: conference participation is not evidence of an internal AI workforce programme. Application: excluded-or-scope-unresolved. Exclusion: Explicit extraction-stage exclusion retained.", "access": "full-source-read", "rationale": "Educational event is visible but broad attendee scope. Exclusion: Explicit extraction-stage exclusion retained.", "proposedLevel": 1, "exclusionReasons": [ "Explicit extraction-stage exclusion retained." ], "claimSha256": "1afd42e37925f6c46d67bf0e844490d3bcdc8007cc69f56e73e38bcc1502483a", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 2, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 2, "score": 7, "coverage": 1, "rank": 7, "sourceCount": 2, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 1, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2025-03-06", "lineage": [ { "dimension": "ai-core", "level": 2, "winningClaimIds": [ "HLT-028" ], "contribution": 7.4074 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.phcc.gov.qa/media/news/2025/03/10/we-are-working-on-introducing-mandatory-annual-oral-screening-for-students---dr.-hamad-al-mudhahka", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 7.41, "rank": 7 }, { "scenario": "implemented-only", "score": 7.41, "rank": 7 }, { "scenario": "detail-cap", "score": 7.41, "rank": 6 }, { "scenario": "ai-core-0.5", "score": 3.92, "rank": 7 }, { "scenario": "ai-core-2", "score": 13.33, "rank": 6 }, { "scenario": "workforce-0.5", "score": 7.84, "rank": 7 }, { "scenario": "workforce-2", "score": 6.67, "rank": 7 }, { "scenario": "ecosystem-0.5", "score": 7.84, "rank": 7 }, { "scenario": "ecosystem-2", "score": 6.67, "rank": 7 }, { "scenario": "interfaces-0.5", "score": 7.84, "rank": 6 }, { "scenario": "interfaces-2", "score": 6.67, "rank": 7 }, { "scenario": "lineage-0.5", "score": 7.84, "rank": 7 }, { "scenario": "lineage-2", "score": 6.67, "rank": 7 }, { "scenario": "experimentation-0.5", "score": 7.84, "rank": 7 }, { "scenario": "experimentation-2", "score": 6.67, "rank": 7 }, { "scenario": "autonomy-0.5", "score": 7.84, "rank": 7 }, { "scenario": "autonomy-2", "score": 6.67, "rank": 7 }, { "scenario": "knowledge-0.5", "score": 7.84, "rank": 7 }, { "scenario": "knowledge-2", "score": 6.67, "rank": 7 }, { "scenario": "governance-0.5", "score": 7.84, "rank": 7 }, { "scenario": "governance-2", "score": 6.67, "rank": 7 } ], "scenarioRankRange": [ 6, 7 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-kuwait-hospital", "name": "Kuwait Hospital", "country": "Kuwait", "sector": "Healthcare", "scope": "Private Kuwait Hospital, Sabah Al-Salem, kuwaithospital.com.kw; excludes UAE hospitals with the same name.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-kuwait-hospital", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-rbh", "name": "Royal Bahrain Hospital", "country": "Bahrain", "sector": "Healthcare", "scope": "Royal Bahrain Hospital in Bahrain; generic KIMSHEALTH group statements are not automatically attributed to this hospital.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-rbh", "evidence": [ { "id": "HLT-032", "companyId": "health-rbh", "sourceId": "HLT-S12", "dimension": "ai-core", "claim": "Royal Bahrain’s MRI page includes a KIMSHEALTH-wide FAQ mentioning AI-assisted diagnostics.", "url": "https://www.royalbahrainhospital.com/procedures/magnetic-resonance-imaging-mri", "originalSourceUrl": "https://www.royalbahrainhospital.com/procedures/magnetic-resonance-imaging-mri", "sourceTitle": "MRI Scan page: KIMSHEALTH FAQs", "corroboratingSourceIds": [], "publisher": "Royal Bahrain Hospital / KIMSHEALTH", "sourceType": "official-implementation-account", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "FAQ: What advanced technologies are available at KIMSHEALTH?", "status": "unclear", "level": null, "eligible": false, "limitation": "Claim scope: Royal Bahrain Hospital in Bahrain; generic KIMSHEALTH group statements are not automatically attributed to this hospital.. Excluded: group template does not identify RBH tool, workflow or implementation scope. Application: excluded-or-scope-unresolved. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Generic KIMSHEALTH group FAQ does not establish deployment at the scoped Bahrain hospital. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 1, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "57102384179f400d9ea334ee71ee248fefb0ff9e657373915d70deec8c4ad8e7", "review": { "decision": "exclude", "record": "expansion/healthcare/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "health-taiba", "name": "Taiba Hospital", "country": "Kuwait", "sector": "Healthcare", "scope": "Private Taiba Hospital in Sabah Al-Salem; overseas physician demonstrations are not evidence of local routine use.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "health-taiba", "evidence": [ { "id": "HLT-030", "companyId": "health-taiba", "sourceId": "HLT-S09", "dimension": "ai-core", "claim": "Indexed Taiba news describes its doctor demonstrating AI-assisted endoscopy at an overseas conference.", "url": "https://www.taibahospital.com/taiba-hospital-proudly-recognizes-dr-asmaa-al-kandaris-achievement-in-a-global-gastrointestinal-endoscopy-conference/", "originalSourceUrl": "https://www.taibahospital.com/taiba-hospital-proudly-recognizes-dr-asmaa-al-kandaris-achievement-in-a-global-gastrointestinal-endoscopy-conference/", "sourceTitle": "Taiba Hospital proudly recognizes Dr Asmaa Al Kandari’s achievement", "corroboratingSourceIds": [], "publisher": "Taiba Hospital", "sourceType": "official-implementation-account", "publicationDate": "2025-03-05", "accessedDate": "2026-10-10", "locator": "Indexed conference passage; full source unavailable", "status": "pilot", "level": null, "eligible": false, "limitation": "Claim scope: Private Taiba Hospital in Sabah Al-Salem; overseas physician demonstrations are not evidence of local routine use.. Snippet-only, direct fetch failed; excluded even if index wording appears specific. Application: excluded-or-scope-unresolved. Exclusion: Explicit extraction-stage exclusion retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "snippet-only", "rationale": "No eligible anchor: inaccessible passage and overseas demonstration cannot establish local routine use. Exclusion: Explicit extraction-stage exclusion retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 1, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "a29f9aa219c11dec29e44101c414ff4105e021a9df37b9a25e596574b9a4f0db", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": "Official annual/sustainability/clinical sources plus issuer and named implementation-partner searches; English and targeted Arabic discovery; cutoff10October2026. See search log.", "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "alba", "name": "Aluminium Bahrain (Alba)", "country": "Bahrain", "sector": "Manufacturing & Materials", "scope": "Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "alba", "evidence": [ { "id": "MAN-ALBA01", "companyId": "alba", "sourceId": "MAN-S11", "dimension": "experimentation", "claim": "Alba and NAIRDC completed a green-anode density prediction model and parameter-entry dashboard.", "url": "https://www.albasmelter.com/en/article/alba-joins-forces-with-nasser-artificial-intelligence-research-and-development-centre-to-advance-in-ai", "originalSourceUrl": "https://www.albasmelter.com/en/article/alba-joins-forces-with-nasser-artificial-intelligence-research-and-development-centre-to-advance-in-ai", "sourceTitle": "Alba and NAIRDC artificial intelligence collaboration", "corroboratingSourceIds": [], "publisher": "Alba", "sourceType": "corporate-operating-announcement", "publicationDate": "2023-07-12", "accessedDate": "2026-10-10", "locator": "Body paragraphs on completed Green Anode Density Prediction project", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.. Completion is not proof of sustained production use or predictive accuracy. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific completed development project; routine operational deployment not established.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "aec89a747da079caab134b687b25b26862144eff230968d32a65b259ce01b23e", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-ALBA02", "companyId": "alba", "sourceId": "MAN-S12", "dimension": "workforce", "claim": "Alba reports a three-day AI course underway for 35 employees, covering generative AI and prompting.", "url": "https://www.albasmelter.com/en/article/alba-partners-with-nvtcs-randd-arm-to-upskill-employees-with-artificial-intelligence-knowledge", "originalSourceUrl": "https://www.albasmelter.com/en/article/alba-partners-with-nvtcs-randd-arm-to-upskill-employees-with-artificial-intelligence-knowledge", "sourceTitle": "Alba partners with NVTC R&D arm to upskill employees", "corroboratingSourceIds": [], "publisher": "Alba", "sourceType": "corporate-operating-announcement", "publicationDate": "2024-08-21", "accessedDate": "2026-10-10", "locator": "Body paragraphs on participants, duration and course topics", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.. 35 participants are not 35 certified completions; no productivity effect established. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "A CEO visit to students confirms the course was underway; duration, participation and topics are specified. Completion and proficiency are not established.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ff805d01138e54128a14df3236ea446a49138c1e674ccafcae48e2cde8a382ac", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-ALBA03", "companyId": "alba", "sourceId": "MAN-S13", "dimension": "ecosystem", "claim": "Alba signed a Seeq agreement for future AI-powered platform integration.", "url": "https://www.albasmelter.com/en/article/alba-sets-regional-benchmark-as-first-middle-east-smelter-to-adopt-ai-powered-seeq-platform", "originalSourceUrl": "https://www.albasmelter.com/en/article/alba-sets-regional-benchmark-as-first-middle-east-smelter-to-adopt-ai-powered-seeq-platform", "sourceTitle": "Alba adopts AI-powered Seeq platform", "corroboratingSourceIds": [], "publisher": "Alba", "sourceType": "corporate-operating-announcement", "publicationDate": "2025-04-27", "accessedDate": "2026-10-10", "locator": "Body paragraphs on agreement and future integration", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.. Headline adoption does not override future-tense implementation text. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Signing and future integration language control the reading despite the adoption headline; completed operational use is not established.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "cb512831dbf15430e1b47e6be85ec33c3a975e8ddbc6b9688dbe9cd2f0c7f6f4", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-ALBA04", "companyId": "alba", "sourceId": "MAN-S14", "dimension": "ai-core", "claim": "Alba describes deployed AI using incident patterns and risk drivers to identify high-risk operating areas.", "url": "https://www.albasmelter.com/uploads/albayan2026_q1.pdf", "originalSourceUrl": "https://www.albasmelter.com/uploads/albayan2026_q1.pdf", "sourceTitle": "Al Bayan Q1 2026", "corroboratingSourceIds": [], "publisher": "Alba", "sourceType": "company-magazine", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical24, printed46–47, AI safety article", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.. No verified predictive accuracy or causal safety improvement. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Issuer newsletter explicitly describes deployment using incident patterns to prioritise smelter risk areas; accuracy and safety effects remain unverified.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "05cea341e50896f575427351bbb2a71c5b6f4fd1bb0ddc56805da3656f146fa3", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-ALBA05", "companyId": "alba", "sourceId": "MAN-S14", "dimension": "interfaces", "claim": "Alba describes an AI Scope of Work assistant that helps prepare structured contract drafts for the contractor-management approval process.", "url": "https://www.albasmelter.com/uploads/albayan2026_q1.pdf", "originalSourceUrl": "https://www.albasmelter.com/uploads/albayan2026_q1.pdf", "sourceTitle": "Al Bayan Q1 2026", "corroboratingSourceIds": [], "publisher": "Alba", "sourceType": "company-magazine", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical25, printed48–49, Scope of Work Chatbot subsection", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.. No autonomous contract award; user-reviewed drafting function. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The company describes a current structured drafting function and its contractor-approval interface. It does not award contracts autonomously.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "58344d788207bf9f38e3118b5ce3c9d8165b5c23e51a5fcb33263560c91254ba", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-ALBA06", "companyId": "alba", "sourceId": "MAN-S14", "dimension": "knowledge", "claim": "Alba describes a Code of Conduct chatbot designed to guide employees on conduct-policy questions.", "url": "https://www.albasmelter.com/uploads/albayan2026_q1.pdf", "originalSourceUrl": "https://www.albasmelter.com/uploads/albayan2026_q1.pdf", "sourceTitle": "Al Bayan Q1 2026", "corroboratingSourceIds": [], "publisher": "Alba", "sourceType": "company-magazine", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical25, printed48–49, Code of Conduct Chatbot subsection", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.. No maintained retrieval mechanism, answer evaluation or live usage established. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Specific conduct-policy chatbot is described as designed for guidance; ongoing operational use is not unambiguously evidenced.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "14b4e1435f9c6c5f582aaa22703595be63fc96df5f1186c0e2136da94a588549", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-ALBA07", "companyId": "alba", "sourceId": "MAN-S15", "dimension": "governance", "claim": "Alba launched an updated Code of Conduct with responsible-AI guidance.", "url": "https://www.albasmelter.com/uploads/Disclosure_of_MI_Alba_Reinforces_Ethical_Leadership_Through_Updated_Code_of_Conduct_23.09.2026_1.pdf", "originalSourceUrl": "https://www.albasmelter.com/uploads/Disclosure_of_MI_Alba_Reinforces_Ethical_Leadership_Through_Updated_Code_of_Conduct_23.09.2026_1.pdf", "sourceTitle": "Updated Code of Conduct disclosure", "corroboratingSourceIds": [], "publisher": "Alba", "sourceType": "regulatory-disclosure", "publicationDate": "2026-09-23", "accessedDate": "2026-10-10", "locator": "Physical2, launch and new guidance paragraphs", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Aluminium Bahrain B.S.C.; retained evidence concerns Bahrain smelter and named central functions, not all newly acquired operations. Maaden shareholding creates overlap.. No public detail on applied testing, enforcement or model controls. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Issued policy explicitly addresses AI.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "93fcac714c555f6fd5eefbae12d370bc197838b08a68389ea5552c36ec39a736", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 1, "interfaces": 3, "lineage": null, "experimentation": 1, "autonomy": null, "knowledge": 1, "governance": 2 }, "points": 14, "score": 52, "coverage": 7, "rank": 1, "sourceCount": 5, "searchBoundary": { "companyId": "alba", "date": "2026-10-10", "queries": [ "site:albasmelter.com artificial intelligence chatbot training" ], "passagesChecked": "2023 model completion; 2024 course; 2025 Seeq future integration; bilingual Q12026 safety/chatbot pages24–25; Sep2026 code pages2–3 read. Array potline article inaccessible and not retained.", "languages": [ "English", "Arabic" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "MAN-S11", "MAN-S12", "MAN-S13", "MAN-S14", "MAN-S15" ] }, "audit": { "eligibleClaims": 7, "excludedClaims": 0, "eligibleSourceUrls": 5, "undatedEligibleClaims": 0, "latestSourceDate": "2026-09-23", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "MAN-ALBA04" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "MAN-ALBA02" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "MAN-ALBA03" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "MAN-ALBA05" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "MAN-ALBA01" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 1, "winningClaimIds": [ "MAN-ALBA06" ], "contribution": 3.7037 }, { "dimension": "governance", "level": 2, "winningClaimIds": [ "MAN-ALBA07" ], "contribution": 7.4074 } ], "singleSourceRemoval": [ { "url": "https://www.albasmelter.com/uploads/albayan2026_q1.pdf", "scoreWithoutSource": 25.93 }, { "url": "https://www.albasmelter.com/en/article/alba-partners-with-nvtcs-randd-arm-to-upskill-employees-with-artificial-intelligence-knowledge", "scoreWithoutSource": 40.74 }, { "url": "https://www.albasmelter.com/uploads/Disclosure_of_MI_Alba_Reinforces_Ethical_Leadership_Through_Updated_Code_of_Conduct_23.09.2026_1.pdf", "scoreWithoutSource": 44.44 }, { "url": "https://www.albasmelter.com/en/article/alba-joins-forces-with-nasser-artificial-intelligence-research-and-development-centre-to-advance-in-ai", "scoreWithoutSource": 48.15 }, { "url": "https://www.albasmelter.com/en/article/alba-sets-regional-benchmark-as-first-middle-east-smelter-to-adopt-ai-powered-seeq-platform", "scoreWithoutSource": 48.15 } ], "scenarioResults": [ { "scenario": "baseline", "score": 51.85, "rank": 1 }, { "scenario": "implemented-only", "score": 40.74, "rank": 1 }, { "scenario": "detail-cap", "score": 40.74, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 49.02, "rank": 1 }, { "scenario": "ai-core-2", "score": 56.67, "rank": 1 }, { "scenario": "workforce-0.5", "score": 49.02, "rank": 1 }, { "scenario": "workforce-2", "score": 56.67, "rank": 1 }, { "scenario": "ecosystem-0.5", "score": 52.94, "rank": 1 }, { "scenario": "ecosystem-2", "score": 50.0, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 49.02, "rank": 1 }, { "scenario": "interfaces-2", "score": 56.67, "rank": 1 }, { "scenario": "lineage-0.5", "score": 54.9, "rank": 1 }, { "scenario": "lineage-2", "score": 46.67, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 52.94, "rank": 1 }, { "scenario": "experimentation-2", "score": 50.0, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 54.9, "rank": 1 }, { "scenario": "autonomy-2", "score": 46.67, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 52.94, "rank": 1 }, { "scenario": "knowledge-2", "score": 50.0, "rank": 1 }, { "scenario": "governance-0.5", "score": 50.98, "rank": 1 }, { "scenario": "governance-2", "score": 53.33, "rank": 1 } ], "scenarioRankRange": [ 1, 1 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "sabic", "name": "SABIC", "country": "Saudi Arabia", "sector": "Manufacturing & Materials", "scope": "SABIC group global manufacturing; claim scope restricted to named functions. Aramco ownership creates cross-sector overlap.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "sabic", "evidence": [ { "id": "MAN-SABIC01", "companyId": "sabic", "sourceId": "MAN-S01", "dimension": "ai-core", "claim": "AI process and predictive analytics tools are deployed at a majority of global manufacturing plants.", "url": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "originalSourceUrl": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "sourceTitle": "SABIC Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "SABIC", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Digital Transformation, printed60 / physical60, deployment paragraph", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: SABIC group global manufacturing; claim scope restricted to named functions. Aramco ownership creates cross-sector overlap.. Mixed predictive analytics/AI tool portfolio; not every tool is AI or every plant covered. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Current deployment and majority-of-plants coverage are explicit for a mixed AI/predictive-analytics portfolio; no all-plants or AI-only coverage inferred.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "14fb3542ea3a0b066d1b08d579fd1179fddee7af3fed2cbf5d5b2bc9e141091a", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-SABIC02", "companyId": "sabic", "sourceId": "MAN-S01", "dimension": "experimentation", "claim": "SABIC piloted AI demand forecasting, pricing and production-planning applications.", "url": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "originalSourceUrl": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "sourceTitle": "SABIC Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "SABIC", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Digital Transformation, printed60 / physical60, AI pilot paragraph", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: SABIC group global manufacturing; claim scope restricted to named functions. Aramco ownership creates cross-sector overlap.. No completed rollout or validated outcome inferred. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "The source expressly labels these commercial/planning applications as pilots.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "f2acac58dfc31627aeba263c76d8c905f5e799e9206b8ce529305415c6b1cc01", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-SABIC03", "companyId": "sabic", "sourceId": "MAN-S01", "dimension": "knowledge", "claim": "GenAI platforms and AI infrastructure connected previously isolated laboratory systems.", "url": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "originalSourceUrl": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "sourceTitle": "SABIC Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "SABIC", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Digital Transformation, printed60 / physical60, R&D paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: SABIC group global manufacturing; claim scope restricted to named functions. Aramco ownership creates cross-sector overlap.. No documented knowledge maintenance, retrieval evaluation or lineage controls. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "GenAI platforms and infrastructure are said to have connected laboratory systems; knowledge maintenance or traceability is not established.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "21ea235a06532262a7af99c20e6db7b488c68adb3953ae946b57aa92502ef230", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-SABIC04", "companyId": "sabic", "sourceId": "MAN-S01", "dimension": "workforce", "claim": "AI and data-driven workforce adoption programmes continued to scale.", "url": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "originalSourceUrl": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "sourceTitle": "SABIC Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "SABIC", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Digital Transformation, printed60 / physical60, workforce paragraph", "status": "scaled", "level": 2, "eligible": true, "limitation": "Claim scope: SABIC group global manufacturing; claim scope restricted to named functions. Aramco ownership creates cross-sector overlap.. No AI-specific completion denominator or demonstrated proficiency. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "AI and data adoption programmes are explicitly continuing to scale; no AI-only completion denominator.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "bebfcd7cafa2092af3b8a216b78b457d0e820ab4e66ee7d3dab60e899f81d02b", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-SABIC05", "companyId": "sabic", "sourceId": "MAN-S01", "dimension": "governance", "claim": "Risk assessments covered emerging technologies including AI.", "url": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "originalSourceUrl": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "sourceTitle": "SABIC Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "SABIC", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Cybersecurity, printed62 / physical62, assessments paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: SABIC group global manufacturing; claim scope restricted to named functions. Aramco ownership creates cross-sector overlap.. General cybersecurity committees and metrics do not prove AI-specific control operation. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "AI explicitly included in performed risk assessments.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "e7396661f2f2723841fcd28d1eb3b4d5b35924b12808da8f89783bddd4444dd0", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 2, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": 1, "autonomy": null, "knowledge": 2, "governance": 2 }, "points": 10, "score": 37, "coverage": 5, "rank": 2, "sourceCount": 1, "searchBoundary": { "companyId": "sabic", "date": "2026-10-10", "queries": [ "site:sabic.com artificial intelligence annual report 2025" ], "passagesChecked": "Digital chapter printed60–62; production coverage, pilots, R&D, workforce and cyber-risk passages checked. Indexed Q22025 call transcript could not be fully read; no count used.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "MAN-S01" ] }, "audit": { "eligibleClaims": 5, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2026", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "MAN-SABIC01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 2, "winningClaimIds": [ "MAN-SABIC04" ], "contribution": 7.4074 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "MAN-SABIC02" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": 2, "winningClaimIds": [ "MAN-SABIC03" ], "contribution": 7.4074 }, { "dimension": "governance", "level": 2, "winningClaimIds": [ "MAN-SABIC05" ], "contribution": 7.4074 } ], "singleSourceRemoval": [ { "url": "https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 37.04, "rank": 2 }, { "scenario": "implemented-only", "score": 33.33, "rank": 2 }, { "scenario": "detail-cap", "score": 33.33, "rank": 2 }, { "scenario": "ai-core-0.5", "score": 33.33, "rank": 2 }, { "scenario": "ai-core-2", "score": 43.33, "rank": 2 }, { "scenario": "workforce-0.5", "score": 35.29, "rank": 2 }, { "scenario": "workforce-2", "score": 40.0, "rank": 2 }, { "scenario": "ecosystem-0.5", "score": 39.22, "rank": 2 }, { "scenario": "ecosystem-2", "score": 33.33, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 39.22, "rank": 2 }, { "scenario": "interfaces-2", "score": 33.33, "rank": 2 }, { "scenario": "lineage-0.5", "score": 39.22, "rank": 2 }, { "scenario": "lineage-2", "score": 33.33, "rank": 2 }, { "scenario": "experimentation-0.5", "score": 37.25, "rank": 2 }, { "scenario": "experimentation-2", "score": 36.67, "rank": 2 }, { "scenario": "autonomy-0.5", "score": 39.22, "rank": 2 }, { "scenario": "autonomy-2", "score": 33.33, "rank": 2 }, { "scenario": "knowledge-0.5", "score": 35.29, "rank": 2 }, { "scenario": "knowledge-2", "score": 40.0, "rank": 2 }, { "scenario": "governance-0.5", "score": 35.29, "rank": 2 }, { "scenario": "governance-2", "score": 40.0, "rank": 2 } ], "scenarioRankRange": [ 2, 2 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "ega", "name": "Emirates Global Aluminium", "country": "United Arab Emirates", "sector": "Manufacturing & Materials", "scope": "EGA group, with retained operating claims confined to Al Taweelah or explicitly described EGA processes.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "ega", "evidence": [ { "id": "MAN-EGA01", "companyId": "ega", "sourceId": "MAN-S04", "dimension": "ai-core", "claim": "EGA reports implemented AI vision checking carbon-anode quality in real time.", "url": "https://media.ega.ae/ega-designated-industry-40-global-lighthouse-by-world-economic-forum-in-uae-and-world-aluminium-industry-first/", "originalSourceUrl": "https://media.ega.ae/ega-designated-industry-40-global-lighthouse-by-world-economic-forum-in-uae-and-world-aluminium-industry-first/", "sourceTitle": "EGA designated Industry 4.0 Global Lighthouse", "corroboratingSourceIds": [], "publisher": "EGA", "sourceType": "corporate-operating-announcement", "publicationDate": "2025-01-15", "accessedDate": "2026-10-10", "locator": "Body paragraph describing real-time carbon-anode quality assessment", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: EGA group, with retained operating claims confined to Al Taweelah or explicitly described EGA processes.. Al Taweelah/site context; no groupwide autonomous production inference. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Implemented AI vision performs real-time carbon-anode checks. Broader Industry 4.0 financial totals are not attributable to this AI workflow.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "62beb5ae46a6f442bc2b450586580b7be36793de53c7e0691c72582b1b7fed82", "review": { "decision": "retain", "record": "expansion/manufacturing/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "MAN-EGA02", "companyId": "ega", "sourceId": "MAN-S05", "dimension": "experimentation", "claim": "EGA began building an EX pilot intended to test AI-enabled autonomous potline capabilities.", "url": "https://media.ega.ae/egas-ex-technology-pilot-to-be-used-to-prove-most-advanced-industry-40-capabilities-in-aluminium-industry/", "originalSourceUrl": "https://media.ega.ae/egas-ex-technology-pilot-to-be-used-to-prove-most-advanced-industry-40-capabilities-in-aluminium-industry/", "sourceTitle": "EX technology pilot and Industry 4.0 capabilities", "corroboratingSourceIds": [], "publisher": "EGA", "sourceType": "corporate-operating-announcement", "publicationDate": "2024-12-09", "accessedDate": "2026-10-10", "locator": "Body paragraphs on ten pilot reduction cells and autonomous-control goal", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: EGA group, with retained operating claims confined to Al Taweelah or explicitly described EGA processes.. Construction and ambition are not deployed autonomous control; later hot-metal milestones do not themselves validate AI. 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Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "MAN-S16" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "industries-qatar", "name": "Industries Qatar", "country": "Qatar", "sector": "Manufacturing & Materials", "scope": "Industries Qatar Q.P.S.C.; investment group exposure to QAPCO is not an independent AI implementation.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "industries-qatar", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": { "companyId": "industries-qatar", "date": "2026-10-10", "queries": [ "site:iq.com.qa artificial intelligence annual report2025" ], "passagesChecked": "2025 annual macro/market passages physical14–15 checked; AI as growth backdrop is not a group deployment.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "MAN-S07" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "pic", "name": "Petrochemical Industries Company", "country": "Kuwait", "sector": "Manufacturing & Materials", "scope": "PIC, KPC petrochemical subsidiary; do not attribute KPC or EQUATE deployment to PIC without direct evidence.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "pic", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": { "companyId": "pic", "date": "2026-10-10", "queries": [ "site:pic.com.kw artificial intelligence" ], "passagesChecked": "Corporate identity read; KPC and EQUATE practices not inherited. Arabic query الذكاء الاصطناعي returned emotional-intelligence training, excluded.", "languages": [ "English", "Arabic" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "MAN-S10" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "qapco", "name": "QAPCO", "country": "Qatar", "sector": "Manufacturing & Materials", "scope": "Qatar Petrochemical Company Q.P.J.S.C.; separate operating entity from Industries Qatar.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "qapco", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": { "companyId": "qapco", "date": "2026-10-10", "queries": [ "site:qapco.com artificial intelligence predictive maintenance" ], "passagesChecked": "Report archive returned No Reports Found. 2022 PDF and 2023 PDF returned404; indexed AVEVA/Microsoft predictive-maintenance plan remains unscored. Arabic site query الذكاء الاصطناعي yielded no retained operating source.", "languages": [ "English", "Arabic" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "MAN-S08" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-aldar", "name": "Aldar Properties", "country": "UAE", "sector": "Real Estate & Construction", "scope": "Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-aldar", "evidence": [ { "id": "REA-008", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "ai-core", "claim": "Aldar reports deployed district AI with Siemens and AI parking through Aldar Estates/Parkonic.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF10, Partnership Highlights", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Parking200,000+ is design capacity, not confirmed active spaces; no causal energy outcome established. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Original innovation report page10 explicitly describes deployed Siemens district system and Parkonic parking operation. 200,000+ spaces is design capacity; no achieved energy reduction inferred.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "6c81a74b97f82ef24b21cef6cbc296f36c411678131fb1a0264be8c7994b3d1e", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-009", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "workforce", "claim": "Aldar embedded AI and geospatial specialists in its Innovation and Strategy team for several months.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF14, Market Intelligence Project", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. No enterprise-wide workforce competence or training claim; specialist integration only. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Page14 describes AI and geospatial specialists embedded for several months in a named team. Retain for operating workforce arrangement, not workforce-wide proficiency.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "43b853ca51462c224365ad3180c81ae360c0b84319102343b2da9dd2dd2fa3a8", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-010", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "knowledge", "claim": "Aldar’s market-intelligence team used PerigonAI mobility and transaction analysis to inform asset decisions.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF14, technology and operating description", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Reported datasets are provider-described inputs, not independently audited or a lineage-control record. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Page14 identifies implemented mobility, transaction and utilisation analytics used in internal market intelligence. Dataset quantities do not demonstrate lineage controls or causal revenue impact.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "f0e64f7b0c055ba1a59d856c3c05ac794ed36d4153e0b1950c678530c8109ea6", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-011", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "autonomy", "claim": "Aldar integrated AI agents into daily security assessments, investigations and triage.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF16, AI-enabled security operating model", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. No exact action authority, approval gates, rollback or escalation mechanism; cannot establish3. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Page16 describes daily AI security assessments, triage and investigations. No explicit permission boundaries or rollback mechanism; retain2 without autonomous enterprise-control inference.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "5d59f48ea064fde9382d69ce8eae5fced5512a716a1cd39a5605aee0da089e99", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-012", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "ecosystem", "claim": "Contilio says Aldar feedback shapes construction-AI functionality and system scale.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF18, How does working with Aldar impact your technical roadmap?", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Supplier statement within issuer report; not independent performance assurance. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Page18 named Contilio executive describes Aldar feedback shaping features and system capacity. This is a specific ongoing collaboration mechanism; vendor testimony is not independent validation.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "61d74a35075339c5689b23bbf0cdb77e46bac68e749456eb3b6572ac19c815a8", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-013", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "interfaces", "claim": "Aldar says it launched AI-agent voice payment of service charges with Visa.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF10, Voice-Enabled Agentic Payment Experience", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. User authorisation described only at high level; not permission for autonomous financial decisions. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Page10 explicitly says launched voice-enabled AI-agent service-charge payment with Visa and lists NLP/integration/encryption. No transaction count, unbounded authority or demonstrated safety inferred.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "365e025da3304b774182ec376bfea51cd050ea5f19fb26d9b560edbecf71c3b1", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-014", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "experimentation", "claim": "Aldar’s Towards0 includes an AI ESG insights chatbot explicitly labelled beta.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF29, RA Copilot(beta)", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Platform’s200assets/513metrics are not evidence of beta chatbot adoption or validated answers. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Page29 specifically labels RA Copilot beta. Surrounding implemented ESG platform does not establish production adoption of this AI component.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "01b0cd1ee1a686f075fd3387e56c5761ac9902943acdcdbef85db94012a74b80", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-015", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "governance", "claim": "Towards0 strengthens ESG data governance through an AI-first platform.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF29, governance and data collection", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Excluded: ESG data governance is not oversight of AI models, decisions or risk. Application: excluded-or-scope-unresolved. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Page29 concerns ESG data governance and general reporting architecture, not oversight of AI models or decisions. Uphold raw exclusion. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 1, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "9622bd15fe9810131baef539e6cd91f0ee3fbd8f864312a645859fd21df50d46", "review": { "decision": "exclude", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-016", "companyId": "real-aldar", "sourceId": "REA-S05", "dimension": "autonomy", "claim": "Aldar describes future autonomous asset-lifecycle agents with minimal human oversight.", "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "originalSourceUrl": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "sourceTitle": "Aldar Innovation Report2025", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "innovation-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF37, What’s Next", "status": "planned", "level": null, "eligible": false, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Excluded as broad future vision; no named implementation or bounded authority. Application: excluded-or-scope-unresolved. Exclusion: Explicit extraction-stage exclusion retained.", "access": "full-source-read | Review: full-source-read", "rationale": "Page37 is future-facing What Is Next, describing autonomous lifecycle agents. It does not establish present deployment or authority. Exclusion: Explicit extraction-stage exclusion retained.", "proposedLevel": 1, "exclusionReasons": [ "Explicit extraction-stage exclusion retained." ], "claimSha256": "60d1db70a9b6e557c9abf0a75eb3616a871d1478f6c92366eb7a129f3dd4ef9f", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-017", "companyId": "real-aldar", "sourceId": "REA-S06", "dimension": "interfaces", "claim": "Aldar reports215,000 AI customer conversations via Hello Aldar in2025.", "url": "https://financialfilings.com/filings/aldar-properties-pjsc/annual-report/2026/48374610/", "originalSourceUrl": "https://financialfilings.com/filings/aldar-properties-pjsc/annual-report/2026/48374610/", "sourceTitle": "Annual Report2025 — full issuer document mirrored by FinancialFilings", "corroboratingSourceIds": [], "publisher": "Aldar Properties; mirror FinancialFilings", "sourceType": "annual-report", "publicationDate": "2026-03-18", "accessedDate": "2026-10-10", "locator": "Mirror lines881–883; Live Aldar section", "status": "scaled", "level": 3, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Full mirror provenance disclosed; conversation count is not resolution, satisfaction or safety. Application: internal-operating-practice.", "access": "full-mirror-read | Review: full-mirror-read", "rationale": "Full issuer-report text on mirror reports 215,000 AI customer conversations in the digital-transformation section. Retain specificity/coverage anchor; issuer bytes not hash-matched and no unique-user, resolution or causality inference.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "354990fb256f457d14685146d7191f8364d1e2617b8d1dcdcace060b7ae7636d", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-018", "companyId": "real-aldar", "sourceId": "REA-S06", "dimension": "ai-core", "claim": "Aldar’s Digital Spine uses AI preconcept design and cost benchmarking.", "url": "https://financialfilings.com/filings/aldar-properties-pjsc/annual-report/2026/48374610/", "originalSourceUrl": "https://financialfilings.com/filings/aldar-properties-pjsc/annual-report/2026/48374610/", "sourceTitle": "Annual Report2025 — full issuer document mirrored by FinancialFilings", "corroboratingSourceIds": [], "publisher": "Aldar Properties; mirror FinancialFilings", "sourceType": "annual-report", "publicationDate": "2026-03-18", "accessedDate": "2026-10-10", "locator": "Mirror lines830–849, Digital Spine", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Selected-project efficiencies not all-project causal AI effects;50+projects is DigitalSpine coverage. Application: internal-operating-practice.", "access": "full-mirror-read | Review: full-mirror-read", "rationale": "Full issuer-report mirror describes AI preconcept generation, design and cost benchmarking integrated into Digital Spine. Reported performance effects are not independently causal or scored outcomes.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "a3d425c4afc84890b058cee8a3df6208cb267737dcb9fae77ab794ea0b9b5063", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-019", "companyId": "real-aldar", "sourceId": "REA-S07", "dimension": "ai-core", "claim": "Aldar’s FY2025 results identify targeted AI use within Digital Spine.", "url": "https://www.aldar.com/en/news-and-media/aldar-q4-fy-2025-financial-results", "originalSourceUrl": "https://www.aldar.com/en/news-and-media/aldar-q4-fy-2025-financial-results", "sourceTitle": "Q4/FY2025 financial results", "corroboratingSourceIds": [], "publisher": "Aldar Properties", "sourceType": "official-implementation-account", "publicationDate": "2026-02-09", "accessedDate": "2026-10-10", "locator": "Group & Corporate Updates, DigitalSpine bullet", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Aldar Properties PJSC group; cases distinguish corporate teams, Aldar Estates, construction and district operations.. Digital programme scale does not isolate AI coverage or gains; no additive scoring for duplicate accounts. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Official issuer corroborates implementation.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "ef1c0b5652722d3122aa9d677e812af7cd367baabad68345d0a309de43845e27", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 3, "interfaces": 3, "lineage": null, "experimentation": 1, "autonomy": 2, "knowledge": 3, "governance": null }, "points": 18, "score": 67, "coverage": 7, "rank": 1, "sourceCount": 3, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 10, "excludedClaims": 2, "eligibleSourceUrls": 3, "undatedEligibleClaims": 7, "latestSourceDate": "2026-03-18", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "REA-008", "REA-018" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "REA-009" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": 3, "winningClaimIds": [ "REA-012" ], "contribution": 11.1111 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "REA-013", "REA-017" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "REA-014" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": 2, "winningClaimIds": [ "REA-011" ], "contribution": 7.4074 }, { "dimension": "knowledge", "level": 3, "winningClaimIds": [ "REA-010" ], "contribution": 11.1111 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf", "scoreWithoutSource": 22.22 }, { "url": "https://financialfilings.com/filings/aldar-properties-pjsc/annual-report/2026/48374610/", "scoreWithoutSource": 66.67 }, { "url": "https://www.aldar.com/en/news-and-media/aldar-q4-fy-2025-financial-results", "scoreWithoutSource": 66.67 } ], "scenarioResults": [ { "scenario": "baseline", "score": 66.67, "rank": 1 }, { "scenario": "implemented-only", "score": 62.96, "rank": 1 }, { "scenario": "detail-cap", "score": 48.15, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 64.71, "rank": 1 }, { "scenario": "ai-core-2", "score": 70.0, "rank": 1 }, { "scenario": "workforce-0.5", "score": 64.71, "rank": 1 }, { "scenario": "workforce-2", "score": 70.0, "rank": 1 }, { "scenario": "ecosystem-0.5", "score": 64.71, "rank": 1 }, { "scenario": "ecosystem-2", "score": 70.0, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 64.71, "rank": 1 }, { "scenario": "interfaces-2", "score": 70.0, "rank": 1 }, { "scenario": "lineage-0.5", "score": 70.59, "rank": 1 }, { "scenario": "lineage-2", "score": 60.0, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 68.63, "rank": 1 }, { "scenario": "experimentation-2", "score": 63.33, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 66.67, "rank": 1 }, { "scenario": "autonomy-2", "score": 66.67, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 64.71, "rank": 1 }, { "scenario": "knowledge-2", "score": 70.0, "rank": 1 }, { "scenario": "governance-0.5", "score": 70.59, "rank": 1 }, { "scenario": "governance-2", "score": 60.0, "rank": 1 } ], "scenarioRankRange": [ 1, 1 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-qatari-diar", "name": "Qatari Diar", "country": "Qatar", "sector": "Real Estate & Construction", "scope": "Qatari Diar Real Estate Investment Company; Lusail operated through Lusail Real Estate Development Company. No inference to global portfolio.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-qatari-diar", "evidence": [ { "id": "REA-020", "companyId": "real-qatari-diar", "sourceId": "REA-S08", "dimension": "ai-core", "claim": "ST Engineering describes AI city operations deployed across80 Lusail sites, connecting video and operational systems.", "url": "https://www.stengg.com/en/innovation/artificial-intelligence/how-ai-is-powering-the-next-generation-of-smart-cities/", "originalSourceUrl": "https://www.stengg.com/en/innovation/artificial-intelligence/how-ai-is-powering-the-next-generation-of-smart-cities/", "sourceTitle": "How AI is powering the next generation of smart cities", "corroboratingSourceIds": [], "publisher": "ST Engineering", "sourceType": "supplier-case-study", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Lusail case, lines902–909 and934", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Qatari Diar through LREDC/Lusail city operations, not global Qatari Diar portfolio.. Undated page observed at cutoff; contract runs to2027, so do not claim all contractual work complete. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Supplier full account explicitly connects deployed Lusail operations to video, sensor and operational systems across80 sites. Vendor assertion, not independent operating audit.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "6fb90617f7f48b1cdfbb41745ea931d9085cfa509701251d0f0170da34af480c", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-021", "companyId": "real-qatari-diar", "sourceId": "REA-S08", "dimension": "interfaces", "claim": "ST Engineering describes Lusail’s CityAI Assistant supporting operator questions and workflow commands.", "url": "https://www.stengg.com/en/innovation/artificial-intelligence/how-ai-is-powering-the-next-generation-of-smart-cities/", "originalSourceUrl": "https://www.stengg.com/en/innovation/artificial-intelligence/how-ai-is-powering-the-next-generation-of-smart-cities/", "sourceTitle": "How AI is powering the next generation of smart cities", "corroboratingSourceIds": [], "publisher": "ST Engineering", "sourceType": "supplier-case-study", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "CityAI Assistant and deployment summary, lines924,929–934", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Qatari Diar Real Estate Investment Company; Lusail operated through Lusail Real Estate Development Company. No inference to global portfolio.. No independently observed use, response accuracy or authorisation details; underlying supplier account. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Supplier explicitly includes CityAI Assistant in the Lusail deployment, with operator natural-language queries and workflow commands. No inference that all described emergency actions operate without human approval.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "16f4758a1fbfbe2ca8348bffb59aa9c4311823bf4e42a5e194b91bd46b6d9702", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-022", "companyId": "real-qatari-diar", "sourceId": "REA-S09", "dimension": "ecosystem", "claim": "LREDC contracted ST Engineering to build and operate Lusail’s AI city platform.", "url": "https://www.stengg.com/en/newsroom/news-releases/st-engineering-drive-ai-powered-smart-city-transformation", "originalSourceUrl": "https://www.stengg.com/en/newsroom/news-releases/st-engineering-drive-ai-powered-smart-city-transformation", "sourceTitle": "ST Engineering wins AI smart-city project in Lusail", "corroboratingSourceIds": [], "publisher": "ST Engineering", "sourceType": "supplier-contract-announcement", "publicationDate": "2024-10-14", "accessedDate": "2026-10-10", "locator": "Contract opening;2024–2027schedule", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Qatari Diar Real Estate Investment Company; Lusail operated through Lusail Real Estate Development Company. No inference to global portfolio.. Contract value and future features are not achieved operational maturity. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific2024commitment; deployment evidence belongs to a separate later account.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "bb3495a8ab46233092a7d4935c5a000e4c13f417a324718a9c8dce7086f8de72", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 1, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 7, "score": 26, "coverage": 3, "rank": 2, "sourceCount": 2, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 3, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 2, "latestSourceDate": "2024-10-14", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "REA-020" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "REA-022" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "REA-021" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.stengg.com/en/innovation/artificial-intelligence/how-ai-is-powering-the-next-generation-of-smart-cities/", "scoreWithoutSource": 3.7 }, { "url": "https://www.stengg.com/en/newsroom/news-releases/st-engineering-drive-ai-powered-smart-city-transformation", "scoreWithoutSource": 22.22 } ], "scenarioResults": [ { "scenario": "baseline", "score": 25.93, "rank": 2 }, { "scenario": "implemented-only", "score": 22.22, "rank": 2 }, { "scenario": "detail-cap", "score": 18.52, "rank": 2 }, { "scenario": "ai-core-0.5", "score": 21.57, "rank": 2 }, { "scenario": "ai-core-2", "score": 33.33, "rank": 2 }, { "scenario": "workforce-0.5", "score": 27.45, "rank": 2 }, { "scenario": "workforce-2", "score": 23.33, "rank": 2 }, { "scenario": "ecosystem-0.5", "score": 25.49, "rank": 2 }, { "scenario": "ecosystem-2", "score": 26.67, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 21.57, "rank": 3 }, { "scenario": "interfaces-2", "score": 33.33, "rank": 2 }, { "scenario": "lineage-0.5", "score": 27.45, "rank": 2 }, { "scenario": "lineage-2", "score": 23.33, "rank": 2 }, { "scenario": "experimentation-0.5", "score": 27.45, "rank": 2 }, { "scenario": "experimentation-2", "score": 23.33, "rank": 3 }, { "scenario": "autonomy-0.5", "score": 27.45, "rank": 2 }, { "scenario": "autonomy-2", "score": 23.33, "rank": 2 }, { "scenario": "knowledge-0.5", "score": 27.45, "rank": 2 }, { "scenario": "knowledge-2", "score": 23.33, "rank": 2 }, { "scenario": "governance-0.5", "score": 27.45, "rank": 2 }, { "scenario": "governance-2", "score": 23.33, "rank": 2 } ], "scenarioRankRange": [ 2, 3 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-rsg", "name": "Red Sea Global", "country": "Saudi Arabia", "sector": "Real Estate & Construction", "scope": "Vertically integrated destination developer; marine environmental monitoring is its own development/conservation operation.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-rsg", "evidence": [ { "id": "REA-003", "companyId": "real-rsg", "sourceId": "REA-S02", "dimension": "ai-core", "claim": "RSG uses CoralNet machine learning to analyse recurring coral-survey imagery.", "url": "https://www.redseaglobal.com/en/media-center/blog/innovation-meets-preservation-technology-s-impact-on-red-sea-biodiversity-conservation/", "originalSourceUrl": "https://www.redseaglobal.com/en/media-center/blog/innovation-meets-preservation-technology-s-impact-on-red-sea-biodiversity-conservation/", "sourceTitle": "Innovation meets preservation: technology’s impact on Red Sea biodiversity conservation", "corroboratingSourceIds": [], "publisher": "Red Sea Global", "sourceType": "official-implementation-account", "publicationDate": "2024-05-22", "accessedDate": "2026-10-10", "locator": "CoralNet paragraphs, lines392–393", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Vertically integrated destination developer; marine environmental monitoring is its own development/conservation operation.. Marine-monitoring scope, not enterprise-wide AI or quantified ecological improvement. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Issuer describes CoralNet used for annual coral-image analysis. Marine monitoring qualifies within destination-development operations; not group-wide coverage.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "710dff500e4aac4abedc1e9eaf0fbefd1eefb93bec0298ada1959cabd236e636", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-004", "companyId": "real-rsg", "sourceId": "REA-S02", "dimension": "experimentation", "claim": "RSG trained CoralNet with1,500 human-labelled images and checked performance against skilled analysts.", "url": "https://www.redseaglobal.com/en/media-center/blog/innovation-meets-preservation-technology-s-impact-on-red-sea-biodiversity-conservation/", "originalSourceUrl": "https://www.redseaglobal.com/en/media-center/blog/innovation-meets-preservation-technology-s-impact-on-red-sea-biodiversity-conservation/", "sourceTitle": "Innovation meets preservation: technology’s impact on Red Sea biodiversity conservation", "corroboratingSourceIds": [], "publisher": "Red Sea Global", "sourceType": "official-implementation-account", "publicationDate": "2024-05-22", "accessedDate": "2026-10-10", "locator": "CoralNet validation paragraph", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Vertically integrated destination developer; marine environmental monitoring is its own development/conservation operation.. No published metric, uncertainty interval, test split or external validation details in this account. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Issuer explicitly describes human-labelled training images and comparison with skilled analysts for the implemented CoralNet workflow. No test split or quantitative validation supplied.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "0d17f7282d5e5bc28b37fa4130dad40cc8050fbaaab3a743b4ec14bda7e9cae8", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": 3, "autonomy": null, "knowledge": null, "governance": null }, "points": 6, "score": 22, "coverage": 2, "rank": 3, "sourceCount": 1, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 2, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2024-05-22", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "REA-003" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 3, "winningClaimIds": [ "REA-004" ], "contribution": 11.1111 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.redseaglobal.com/en/media-center/blog/innovation-meets-preservation-technology-s-impact-on-red-sea-biodiversity-conservation/", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 22.22, "rank": 3 }, { "scenario": "implemented-only", "score": 22.22, "rank": 2 }, { "scenario": "detail-cap", "score": 14.81, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 17.65, "rank": 3 }, { "scenario": "ai-core-2", "score": 30.0, "rank": 3 }, { "scenario": "workforce-0.5", "score": 23.53, "rank": 3 }, { "scenario": "workforce-2", "score": 20.0, "rank": 3 }, { "scenario": "ecosystem-0.5", "score": 23.53, "rank": 3 }, { "scenario": "ecosystem-2", "score": 20.0, "rank": 4 }, { "scenario": "interfaces-0.5", "score": 23.53, "rank": 2 }, { "scenario": "interfaces-2", "score": 20.0, "rank": 3 }, { "scenario": "lineage-0.5", "score": 23.53, "rank": 3 }, { "scenario": "lineage-2", "score": 20.0, "rank": 3 }, { "scenario": "experimentation-0.5", "score": 17.65, "rank": 4 }, { "scenario": "experimentation-2", "score": 30.0, "rank": 2 }, { "scenario": "autonomy-0.5", "score": 23.53, "rank": 3 }, { "scenario": "autonomy-2", "score": 20.0, "rank": 3 }, { "scenario": "knowledge-0.5", "score": 23.53, "rank": 3 }, { "scenario": "knowledge-2", "score": 20.0, "rank": 3 }, { "scenario": "governance-0.5", "score": 23.53, "rank": 3 }, { "scenario": "governance-2", "score": 20.0, "rank": 3 } ], "scenarioRankRange": [ 2, 4 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-emaar", "name": "Emaar Properties", "country": "UAE", "sector": "Real Estate & Construction", "scope": "Emaar Properties group; named Emaar Hospitality kitchen case is scoped to subsidiary operations, not all development activities.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-emaar", "evidence": [ { "id": "REA-005", "companyId": "real-emaar", "sourceId": "REA-S03", "dimension": "ai-core", "claim": "Winnow describes installed AI food-waste recognition at Emaar Hospitality properties after a2019 proof of concept.", "url": "https://www.winnowsolutions.com/resources/news/emaar-hospitality-group-first-to-bring-cutting-edge-technology-into-uae-kitchen", "originalSourceUrl": "https://www.winnowsolutions.com/resources/news/emaar-hospitality-group-first-to-bring-cutting-edge-technology-into-uae-kitchen", "sourceTitle": "Emaar Hospitality Group first to bring AI technology into UAE kitchens", "corroboratingSourceIds": [], "publisher": "Winnow", "sourceType": "supplier-case-study", "publicationDate": "2019-05-14", "accessedDate": "2026-10-10", "locator": "Deployment and camera/scales/ML paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emaar Hospitality commercial-kitchen operations; historic2019 case, not entire Emaar Properties.. Historical supplier account:12properties cited for food-waste technology overall; not all12 proven VisionAI installations. Current2026 continuation unverified. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Supplier account explicitly reports launch after proof of concept and installed food-waste technology, with AI recognition mechanism. Historical 2019 hospitality scope; twelve properties cannot safely be treated as twelve Vision AI installations or continuing 2026 use.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "49254f2d4e60c5719b01573657608e1aa33112ef0d7354431c12500c860e189e", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-006", "companyId": "real-emaar", "sourceId": "REA-S03", "dimension": "ecosystem", "claim": "Emaar Hospitality worked with Winnow and UAE MOCCAE to launch AI kitchen-waste technology.", "url": "https://www.winnowsolutions.com/resources/news/emaar-hospitality-group-first-to-bring-cutting-edge-technology-into-uae-kitchen", "originalSourceUrl": "https://www.winnowsolutions.com/resources/news/emaar-hospitality-group-first-to-bring-cutting-edge-technology-into-uae-kitchen", "sourceTitle": "Emaar Hospitality Group first to bring AI technology into UAE kitchens", "corroboratingSourceIds": [], "publisher": "Winnow", "sourceType": "supplier-case-study", "publicationDate": "2019-05-14", "accessedDate": "2026-10-10", "locator": "Launch and named Emaar COO paragraphs", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Emaar Properties group; named Emaar Hospitality kitchen case is scoped to subsidiary operations, not all development activities.. Historical vendor-hosted account; no inference that current partnership terms or coverage are unchanged. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Named Emaar executive and supplier describe implemented kitchen collaboration. Historical supplier evidence, not independent assurance.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "0d94e178f8ce83450994265e2bbba81dd122184eca676cb3cb26867fcf6b04a5", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-007", "companyId": "real-emaar", "sourceId": "REA-S04", "dimension": "ai-core", "claim": "Indexed2024 annual-report text mentions Winnow AI in five Dubai hotels.", "url": "https://properties.emaar.com/wp-content/uploads/2025/03/Emaar-Properties-IR-2024_English_F.pdf", "originalSourceUrl": "https://properties.emaar.com/wp-content/uploads/2025/03/Emaar-Properties-IR-2024_English_F.pdf", "sourceTitle": "Emaar Properties Integrated Annual Report2024 — inaccessible current issuer URL", "corroboratingSourceIds": [], "publisher": "Emaar Properties", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Indexed printed45", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Emaar Properties group; named Emaar Hospitality kitchen case is scoped to subsidiary operations, not all development activities.. Snippet-only; issuer PDF URL now redirects to homepage. Not used to establish refreshed adoption. Application: excluded-or-scope-unresolved. Exclusion: Explicit extraction-stage exclusion retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "snippet-only", "rationale": "Not eligible: full current source did not resolve. Exclusion: Explicit extraction-stage exclusion retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 2, "exclusionReasons": [ "Explicit extraction-stage exclusion retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "4b485bc9f3bc261cd5af774dd801508443eaf3e2d9ebd2729b5a51c454e45510", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 2, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 5, "score": 19, "coverage": 2, "rank": 4, "sourceCount": 2, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 2, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2019-05-14", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "REA-005" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 2, "winningClaimIds": [ "REA-006" ], "contribution": 7.4074 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.winnowsolutions.com/resources/news/emaar-hospitality-group-first-to-bring-cutting-edge-technology-into-uae-kitchen", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 18.52, "rank": 4 }, { "scenario": "implemented-only", "score": 18.52, "rank": 4 }, { "scenario": "detail-cap", "score": 14.81, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 13.73, "rank": 4 }, { "scenario": "ai-core-2", "score": 26.67, "rank": 4 }, { "scenario": "workforce-0.5", "score": 19.61, "rank": 4 }, { "scenario": "workforce-2", "score": 16.67, "rank": 4 }, { "scenario": "ecosystem-0.5", "score": 15.69, "rank": 4 }, { "scenario": "ecosystem-2", "score": 23.33, "rank": 3 }, { "scenario": "interfaces-0.5", "score": 19.61, "rank": 4 }, { "scenario": "interfaces-2", "score": 16.67, "rank": 5 }, { "scenario": "lineage-0.5", "score": 19.61, "rank": 4 }, { "scenario": "lineage-2", "score": 16.67, "rank": 4 }, { "scenario": "experimentation-0.5", "score": 19.61, "rank": 3 }, { "scenario": "experimentation-2", "score": 16.67, "rank": 4 }, { "scenario": "autonomy-0.5", "score": 19.61, "rank": 4 }, { "scenario": "autonomy-2", "score": 16.67, "rank": 4 }, { "scenario": "knowledge-0.5", "score": 19.61, "rank": 4 }, { "scenario": "knowledge-2", "score": 16.67, "rank": 4 }, { "scenario": "governance-0.5", "score": 19.61, "rank": 4 }, { "scenario": "governance-2", "score": 16.67, "rank": 4 } ], "scenarioRankRange": [ 3, 5 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-udc", "name": "United Development Company", "country": "Qatar", "sector": "Real Estate & Construction", "scope": "UDC QPSC; Pearl/Gewan services. Qatar Cool separately identified as51%-owned investee; report labels Major Associates.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-udc", "evidence": [ { "id": "REA-023", "companyId": "real-udc", "sourceId": "REA-S10", "dimension": "interfaces", "claim": "UDC says its facilities ecosystem and Pearl Island Super App use an omnichannel AI assistant.", "url": "https://udcqatar.com/Media-Room/Press-Releases/Middle-East-Enterprise-AI", "originalSourceUrl": "https://udcqatar.com/Media-Room/Press-Releases/Middle-East-Enterprise-AI", "sourceTitle": "United Development Company wins three AI & Analytics Summit awards", "corroboratingSourceIds": [], "publisher": "United Development Company", "sourceType": "official-implementation-account", "publicationDate": "2025-11-17", "accessedDate": "2026-10-10", "locator": "CAFM ecosystem and Super App paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: UDC QPSC; Pearl/Gewan services. Qatar Cool separately identified as51%-owned investee; report labels Major Associates.. Awards are not independent validation; no AI resolution or user-volume denominator. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Official article body describes implemented CAFM/contact-centre ecosystem and Super App supported by omnichannel AI assistant. Specific service linkage qualifies; awards do not independently validate effectiveness.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "9b428dae00aa356f053bed6470696f1b6de9c1fd0e023959cec860a3867c9d4c", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-024", "companyId": "real-udc", "sourceId": "REA-S11", "dimension": "ai-core", "claim": "UDC reports Qatar Cool implementing AI initiatives across multiple plants in2025.", "url": "https://udcqatar.com/-/media/28448e61b207417e9756f8027195f1a5.ashx", "originalSourceUrl": "https://udcqatar.com/-/media/28448e61b207417e9756f8027195f1a5.ashx", "sourceTitle": "Annual Report2025", "corroboratingSourceIds": [], "publisher": "United Development Company", "sourceType": "annual-report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF34/printed66–67, QatarCool2025highlights", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Qatar Cool, UDC51%-owned district-cooling investee; plant operations in Qatar.. QatarCool is51%-owned; report section labels Major Associates. Separate investee case, not group-wide use or proof that availability gains resulted from AI. Application: internal-operating-practice. Exclusion: New claim has no explicit accepted review decision. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Full annual report places Qatar Cool under Major Associates, separately from Major Subsidiaries. A51% interest alone does not resolve operating-control/consolidation scope. Preserve investee case, but do not transfer its practice into UDC points without scope evidence. Exclusion: New claim has no explicit accepted review decision. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 2, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "e37e10b5e91b96547ea8395460e29bc9b111661a40c784dbf5e6b2369859100f", "review": { "decision": "exclude", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 3, "score": 11, "coverage": 1, "rank": 5, "sourceCount": 2, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 1, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2025-11-17", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "REA-023" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://udcqatar.com/Media-Room/Press-Releases/Middle-East-Enterprise-AI", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 11.11, "rank": 5 }, { "scenario": "implemented-only", "score": 11.11, "rank": 5 }, { "scenario": "detail-cap", "score": 7.41, "rank": 5 }, { "scenario": "ai-core-0.5", "score": 11.76, "rank": 5 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 5 }, { "scenario": "workforce-0.5", "score": 11.76, "rank": 5 }, { "scenario": "workforce-2", "score": 10.0, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 11.76, "rank": 5 }, { "scenario": "ecosystem-2", "score": 10.0, "rank": 5 }, { "scenario": "interfaces-0.5", "score": 5.88, "rank": 6 }, { "scenario": "interfaces-2", "score": 20.0, "rank": 3 }, { "scenario": "lineage-0.5", "score": 11.76, "rank": 5 }, { "scenario": "lineage-2", "score": 10.0, "rank": 5 }, { "scenario": "experimentation-0.5", "score": 11.76, "rank": 5 }, { "scenario": "experimentation-2", "score": 10.0, "rank": 5 }, { "scenario": "autonomy-0.5", "score": 11.76, "rank": 5 }, { "scenario": "autonomy-2", "score": 10.0, "rank": 5 }, { "scenario": "knowledge-0.5", "score": 11.76, "rank": 5 }, { "scenario": "knowledge-2", "score": 10.0, "rank": 5 }, { "scenario": "governance-0.5", "score": 11.76, "rank": 5 }, { "scenario": "governance-2", "score": 10.0, "rank": 5 } ], "scenarioRankRange": [ 3, 6 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-roshn", "name": "ROSHN Group", "country": "Saudi Arabia", "sector": "Real Estate & Construction", "scope": "PIF-owned multi-asset real-estate developer; proposed innovation programmes distinguished from deployed operations.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-roshn", "evidence": [ { "id": "REA-001", "companyId": "real-roshn", "sourceId": "REA-S01", "dimension": "experimentation", "claim": "ROSHN’s2025 hackathon includes an AI & Automation track and opportunities to develop pilots for its operations.", "url": "https://www.roshn.sa/news-and-events/press-releases/article-78", "originalSourceUrl": "https://www.roshn.sa/news-and-events/press-releases/article-78", "sourceTitle": "ROSHN Group launches registration for ROSHN Hackathon2025", "corroboratingSourceIds": [], "publisher": "ROSHN", "sourceType": "official-implementation-account", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "AI track and pilot-opportunity paragraphs", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: PIF-owned multi-asset real-estate developer; proposed innovation programmes distinguished from deployed operations.. Registration announcement; prizes and submissions are not adoption or employee-skill measures. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Registration for a named AI track and possible pilots is a specific programme commitment, not deployed solutions.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "faf160a10090751ed92b9acb7876d411a602844aebddfedccb115970c9778734", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "REA-002", "companyId": "real-roshn", "sourceId": "REA-S01", "dimension": "ecosystem", "claim": "ROSHN names Tuwaiq Academy, The Garage, Microsoft and Google Cloud in its hackathon ecosystem.", "url": "https://www.roshn.sa/news-and-events/press-releases/article-78", "originalSourceUrl": "https://www.roshn.sa/news-and-events/press-releases/article-78", "sourceTitle": "ROSHN Group launches registration for ROSHN Hackathon2025", "corroboratingSourceIds": [], "publisher": "ROSHN", "sourceType": "official-implementation-account", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Partner and mentorship paragraphs", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: PIF-owned multi-asset real-estate developer; proposed innovation programmes distinguished from deployed operations.. Not evidence that proposed solutions were adopted or a live AI partner operating mechanism. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Named partners support an AI-inclusive planned hackathon; no implemented AI collaboration outcome established.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "0031dc7f61246c8750d8f34658cc74c27cc6ad8ea30d3d937c28fea843498609", "review": { "decision": "retain", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": 1, "interfaces": null, "lineage": null, "experimentation": 1, "autonomy": null, "knowledge": null, "governance": null }, "points": 2, "score": 7, "coverage": 2, "rank": 6, "sourceCount": 1, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 2, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 2, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "REA-002" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "REA-001" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.roshn.sa/news-and-events/press-releases/article-78", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 7.41, "rank": 6 }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 7.41, "rank": 5 }, { "scenario": "ai-core-0.5", "score": 7.84, "rank": 6 }, { "scenario": "ai-core-2", "score": 6.67, "rank": 6 }, { "scenario": "workforce-0.5", "score": 7.84, "rank": 6 }, { "scenario": "workforce-2", "score": 6.67, "rank": 6 }, { "scenario": "ecosystem-0.5", "score": 5.88, "rank": 6 }, { "scenario": "ecosystem-2", "score": 10.0, "rank": 5 }, { "scenario": "interfaces-0.5", "score": 7.84, "rank": 5 }, { "scenario": "interfaces-2", "score": 6.67, "rank": 6 }, { "scenario": "lineage-0.5", "score": 7.84, "rank": 6 }, { "scenario": "lineage-2", "score": 6.67, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 5.88, "rank": 6 }, { "scenario": "experimentation-2", "score": 10.0, "rank": 5 }, { "scenario": "autonomy-0.5", "score": 7.84, "rank": 6 }, { "scenario": "autonomy-2", "score": 6.67, "rank": 6 }, { "scenario": "knowledge-0.5", "score": 7.84, "rank": 6 }, { "scenario": "knowledge-2", "score": 6.67, "rank": 6 }, { "scenario": "governance-0.5", "score": 7.84, "rank": 6 }, { "scenario": "governance-2", "score": 6.67, "rank": 6 } ], "scenarioRankRange": [ 5, 6 ], "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-diyar", "name": "Diyar Al Muharraq", "country": "Bahrain", "sector": "Real Estate & Construction", "scope": "Bahrain integrated master developer Diyar Al Muharraq; excludes unrelated similarly named developers.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-diyar", "evidence": [ { "id": "REA-026", "companyId": "real-diyar", "sourceId": "REA-S15", "dimension": "ecosystem", "claim": "Diyar sponsored a smart-city summit whose agenda included AI.", "url": "https://www.diyar.bh/en/press/97/diyar-al-muharraq-official-platinum-sponsor-for-the-bahrain-smart-cities-summit-2024", "originalSourceUrl": "https://www.diyar.bh/en/press/97/diyar-al-muharraq-official-platinum-sponsor-for-the-bahrain-smart-cities-summit-2024", "sourceTitle": "Diyar sponsors Bahrain Smart Cities Summit2024", "corroboratingSourceIds": [], "publisher": "Diyar Al Muharraq", "sourceType": "official-implementation-account", "publicationDate": "2024-02-11", "accessedDate": "2026-10-10", "locator": "Conference agenda and sponsorship paragraphs", "status": "planned", "level": null, "eligible": false, "limitation": "Claim scope: Bahrain integrated master developer Diyar Al Muharraq; excludes unrelated similarly named developers.. Excluded: smart meters, LEDs and solar trees are not themselves AI deployments. Application: excluded-or-scope-unresolved. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Full release establishes conference sponsorship and AI as an agenda topic. It does not establish a named internal AI operating practice or implementation partnership. Uphold exclusion. Exclusion: New claim has no explicit accepted review decision. Explicit extraction-stage exclusion retained. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 1, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "Explicit extraction-stage exclusion retained.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "be491caa2eb31e1ec49421b042108aa70a6ea8c2492f31997c99dbe730749388", "review": { "decision": "exclude", "record": "expansion/realestate/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-mabanee", "name": "Mabanee", "country": "Kuwait", "sector": "Real Estate & Construction", "scope": "Mabanee Company KPSC consolidated real-estate developer/retail asset group; headquarters is not country scope of all assets.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-mabanee", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-naseej", "name": "Naseej", "country": "Bahrain", "sector": "Real Estate & Construction", "scope": "Naseej BSC(c), Bahrain real-estate developer; excludes Saudi Naseej technology and textile companies.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-naseej", "evidence": [ { "id": "REA-027", "companyId": "real-naseej", "sourceId": "REA-S16", "dimension": "ai-core", "claim": "Naseej’s2025–2027strategy emphasises growth and innovation.", "url": "https://www.naseej.bh/our-news/naseej-holds-annual-general-meeting-and-announces-new-strategy", "originalSourceUrl": "https://www.naseej.bh/our-news/naseej-holds-annual-general-meeting-and-announces-new-strategy", "sourceTitle": "Naseej AGM and new strategy", "corroboratingSourceIds": [], "publisher": "Naseej", "sourceType": "official-implementation-account", "publicationDate": "2025-06-24", "accessedDate": "2026-10-10", "locator": "AGM strategy paragraph", "status": "planned", "level": null, "eligible": false, "limitation": "Claim scope: Naseej BSC(c), Bahrain real-estate developer; excludes Saudi Naseej technology and textile companies.. Excluded: generic innovation is not AI implementation; unrelated SaudiNaseej AI-model news is not this company. Application: excluded-or-scope-unresolved. Exclusion: Explicit extraction-stage exclusion retained.", "access": "full-source-read", "rationale": "No qualifying specific AI commitment. Exclusion: Explicit extraction-stage exclusion retained.", "proposedLevel": 1, "exclusionReasons": [ "Explicit extraction-stage exclusion retained." ], "claimSha256": "f21793d7441640e222998fa1c948d1b6c54cdd04408cd6ed1a9f6f544f63c191", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "real-tamdeen", "name": "Tamdeen Group", "country": "Kuwait", "sector": "Real Estate & Construction", "scope": "Tamdeen Group umbrella, not identical to listed Tamdeen Real Estate Company; entertainment subsidiary case identified and excluded.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "real-tamdeen", "evidence": [ { "id": "REA-025", "companyId": "real-tamdeen", "sourceId": "REA-S14", "dimension": "ai-core", "claim": "Tamdeen’s Planet Laser announcement describes AI-enhanced gameplay at a new entertainment venue.", "url": "https://www.tamdeen.com/news/tamdeen-entertainment-unveils-planet-laser-by-fun-tiki-at-al-khiran-mall", "originalSourceUrl": "https://www.tamdeen.com/news/tamdeen-entertainment-unveils-planet-laser-by-fun-tiki-at-al-khiran-mall", "sourceTitle": "Tamdeen Entertainment unveils Planet Laser", "corroboratingSourceIds": [], "publisher": "Tamdeen Group", "sourceType": "official-implementation-account", "publicationDate": "2025-06-29", "accessedDate": "2026-10-10", "locator": "AI gameplay paragraph and future launch sentence", "status": "planned", "level": null, "eligible": false, "limitation": "Claim scope: Tamdeen Group umbrella, not identical to listed Tamdeen Real Estate Company; entertainment subsidiary case identified and excluded.. Excluded: customer entertainment feature without evidenced internal workflow mechanism; page mixes opening language with upcoming launch. Application: excluded-or-scope-unresolved. Exclusion: Explicit extraction-stage exclusion retained.", "access": "full-source-read", "rationale": "Specific product feature, not internal developer operating practice. Exclusion: Explicit extraction-stage exclusion retained.", "proposedLevel": 1, "exclusionReasons": [ "Explicit extraction-stage exclusion retained." ], "claimSha256": "1ce7b784a5356e130a6795faf811ca4d860f94127e94b2ebc77e19362cf6b837", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": "Annual/sustainability/innovation reports, official operating accounts and named suppliers; English plus targeted Arabic discovery. See access/search log.", "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "lulu-retail", "name": "LuLu Retail Holdings PLC", "country": "United Arab Emirates", "sector": "Retail & Consumer", "scope": "Listed GCC retail group; historical LuLu brand claims limited to GCC operations, excluding non-GCC group businesses.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "lulu-retail", "evidence": [ { "id": "RET-LULU01", "companyId": "lulu-retail", "sourceId": "RET-S06", "dimension": "ai-core", "claim": "LuLu reports AI forecasting and replenishment operational in the UAE using sales, seasonality, promotion and store-pattern inputs.", "url": "https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf", "originalSourceUrl": "https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf", "sourceTitle": "LuLu Retail Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "LuLu Retail", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical105, printed104, AI adoption section", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed GCC retail group; historical LuLu brand claims limited to GCC operations, excluding non-GCC group businesses.. Other-country rollout underway is not completed; no verified benefit size. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Annual2025 printed104/physical105 describes operational UAE forecasting/replenishment with sales, seasonality, promotion and store inputs. Other-market rollout remains underway.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "e0125fbde89a5fbe160681729e1821d725d2e54fee5bc940e548e11ce91a38c0", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-LULU02", "companyId": "lulu-retail", "sourceId": "RET-S06", "dimension": "autonomy", "claim": "Planning teams review AI outputs and exceptions, approving replenishment recommendations within defined rules and thresholds.", "url": "https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf", "originalSourceUrl": "https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf", "sourceTitle": "LuLu Retail Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "LuLu Retail", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical105, printed104, oversight and automation paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed GCC retail group; historical LuLu brand claims limited to GCC operations, excluding non-GCC group businesses.. Bounded automation, not fully autonomous buying; thresholds not numerically disclosed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Same annual passage explicitly describes planning-team review, exception handling, approvals and bounded automated operation. This establishes specified decision controls, not unrestricted autonomy.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "7f3b3c9ef114015c9dcc8892052840ba0be818e1dd36eb1636512b7715004c56", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-LULU03", "companyId": "lulu-retail", "sourceId": "RET-S06", "dimension": "governance", "claim": "LuLu describes data-quality, access and security controls plus forecast and exception monitoring around operational AI.", "url": "https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf", "originalSourceUrl": "https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf", "sourceTitle": "LuLu Retail Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "LuLu Retail", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical105, printed104, controls, oversight and KPI paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed GCC retail group; historical LuLu brand claims limited to GCC operations, excluding non-GCC group businesses.. Self-report, not audit of controls or independent forecast validation. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Same annual passage specifies data quality, access/security controls, risk framework and forecast/exception monitoring around operational AI. These are issuer-reported controls, not independent compliance assurance.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "7d7ebdce4f45e6ed3189e6187a0e05e64718480d3e78224bff1d4839e821bd36", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-LULU04", "companyId": "lulu-retail", "sourceId": "RET-S07", "dimension": "interfaces", "claim": "LuLu deployed AI shopping robots in UAE stores to guide shoppers to products and communicate promotions.", "url": "https://www.luluretail.com/media/news/lulu-brings-ai-powered-robotic-shopping-experience-to-uae-stores/", "originalSourceUrl": "https://www.luluretail.com/media/news/lulu-brings-ai-powered-robotic-shopping-experience-to-uae-stores/", "sourceTitle": "AI-powered robotic shopping experience in UAE stores", "corroboratingSourceIds": [], "publisher": "LuLu Retail", "sourceType": "corporate-operating-announcement", "publicationDate": "2026-10-01", "accessedDate": "2026-10-10", "locator": "Body deployment and Odigo robot capabilities paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed GCC retail group; historical LuLu brand claims limited to GCC operations, excluding non-GCC group businesses.. Number of stores and adoption are not disclosed; no enterprise autonomy inference. Application: operator-service-delivery.", "access": "full-source-read | Review: full-source-read", "rationale": "Full1October2026 release reports shopping robots deployed in UAE stores with navigation/product/promotion functions. Before cutoff; no undisclosed store count or sales effect inferred.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "51eaef0f0543476f9e9667ae097a85f6cd3ea7d144fdd6b7ae9b7211fb8b1fed", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-LULU05", "companyId": "lulu-retail", "sourceId": "RET-S08", "dimension": "ecosystem", "claim": "LuLu launched the Yellow.ai-powered Salem WhatsApp assistant integrated with customer and order systems.", "url": "https://www.luluretail.com/media/news/lulu-hypermarket-launches-new-ai-powered-customer-experiences-on-whatsapp-in-collaboration-with-yellowai", "originalSourceUrl": "https://www.luluretail.com/media/news/lulu-hypermarket-launches-new-ai-powered-customer-experiences-on-whatsapp-in-collaboration-with-yellowai", "sourceTitle": "AI-powered customer experiences on WhatsApp with Yellow.ai", "corroboratingSourceIds": [], "publisher": "LuLu Retail", "sourceType": "corporate-operating-announcement", "publicationDate": "2023-02-28", "accessedDate": "2026-10-10", "locator": "Body launch, CRM/order integration and live-agent handoff paragraphs", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Listed GCC retail group; historical LuLu brand claims limited to GCC operations, excluding non-GCC group businesses.. Historical brand announcement includes non-GCC locations; retained scope only GCC operations. Outcomes not audited. Application: operator-service-delivery.", "access": "full-source-read | Review: full-source-read", "rationale": "Full February2023 release describes launched Salem assistant with customer/order-system integration and live-agent handoff. Retain GCC operating scope only; non-GCC brand operations and historical continuity are separate.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "e0917b557803613b763fed0eeb642fe3c8c237fa6d1ad72718bbc7e37c90386a", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 3, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": 3, "knowledge": null, "governance": 3 }, "points": 15, "score": 56, "coverage": 5, "rank": 1, "sourceCount": 3, "searchBoundary": { "companyId": "lulu-retail", "date": "2026-10-10", "queries": [ "site:luluretail.com artificial intelligence annual report2025" ], "passagesChecked": "2025 annual printed104 operation, approvals and controls read; 2026robot release and 2023WhatsApp integration release read. Non-GCC operations excluded.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S06", "RET-S07", "RET-S08" ] }, "audit": { "eligibleClaims": 5, "excludedClaims": 0, "eligibleSourceUrls": 3, "undatedEligibleClaims": 0, "latestSourceDate": "2026-10-01", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "RET-LULU01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 3, "winningClaimIds": [ "RET-LULU05" ], "contribution": 11.1111 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "RET-LULU04" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": 3, "winningClaimIds": [ "RET-LULU02" ], "contribution": 11.1111 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "RET-LULU03" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf", "scoreWithoutSource": 22.22 }, { "url": "https://www.luluretail.com/media/news/lulu-brings-ai-powered-robotic-shopping-experience-to-uae-stores/", "scoreWithoutSource": 44.44 }, { "url": "https://www.luluretail.com/media/news/lulu-hypermarket-launches-new-ai-powered-customer-experiences-on-whatsapp-in-collaboration-with-yellowai", "scoreWithoutSource": 44.44 } ], "scenarioResults": [ { "scenario": "baseline", "score": 55.56, "rank": 1 }, { "scenario": "implemented-only", "score": 55.56, "rank": 1 }, { "scenario": "detail-cap", "score": 37.04, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 52.94, "rank": 1 }, { "scenario": "ai-core-2", "score": 60.0, "rank": 1 }, { "scenario": "workforce-0.5", "score": 58.82, "rank": 1 }, { "scenario": "workforce-2", "score": 50.0, "rank": 2 }, { "scenario": "ecosystem-0.5", "score": 52.94, "rank": 1 }, { "scenario": "ecosystem-2", "score": 60.0, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 52.94, "rank": 1 }, { "scenario": "interfaces-2", "score": 60.0, "rank": 1 }, { "scenario": "lineage-0.5", "score": 58.82, "rank": 1 }, { "scenario": "lineage-2", "score": 50.0, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 58.82, "rank": 1 }, { "scenario": "experimentation-2", "score": 50.0, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 52.94, "rank": 2 }, { "scenario": "autonomy-2", "score": 60.0, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 58.82, "rank": 1 }, { "scenario": "knowledge-2", "score": 50.0, "rank": 2 }, { "scenario": "governance-0.5", "score": 52.94, "rank": 2 }, { "scenario": "governance-2", "score": 60.0, "rank": 1 } ], "scenarioRankRange": [ 1, 2 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "majid-al-futtaim", "name": "Majid Al Futtaim", "country": "United Arab Emirates", "sector": "Retail & Consumer", "scope": "Majid Al Futtaim group; claims restricted to Retail, named store, HR or training scope. Not Carrefour SA worldwide.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "majid-al-futtaim", "evidence": [ { "id": "RET-MAF01", "companyId": "majid-al-futtaim", "sourceId": "RET-S03", "dimension": "workforce", "claim": "MAF launched an AI Academy with separate hands-on, technical and senior-leader learning paths.", "url": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "originalSourceUrl": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "sourceTitle": "Majid Al Futtaim Sustainability Report 2024", "corroboratingSourceIds": [], "publisher": "Majid Al Futtaim", "sourceType": "sustainability-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "Physical/printed80–81, AI Academy and GO/DEVELOP/ELEVATE pathways", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Majid Al Futtaim group; claims restricted to Retail, named store, HR or training scope. Not Carrefour SA worldwide.. Other academy satisfaction and coverage figures cannot be attributed to AI Academy. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Original2024 report pages80-81 describes launched AI Academy with three role-specific pathways. Do not transfer other academy participation or satisfaction figures into AI coverage or proficiency.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "5be057aa54dc8fac3ba23a17d103036ec26cc6ca590053ca4f5ba197f7330a18", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-MAF02", "companyId": "majid-al-futtaim", "sourceId": "RET-S03", "dimension": "ai-core", "claim": "MAF describes an AI pricing engine considering scenarios, recommendations and promotions in grocery retail.", "url": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "originalSourceUrl": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "sourceTitle": "Majid Al Futtaim Sustainability Report 2024", "corroboratingSourceIds": [], "publisher": "Majid Al Futtaim", "sourceType": "sustainability-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "Physical/printed70, AI customer-service spotlight", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Majid Al Futtaim group; claims restricted to Retail, named store, HR or training scope. Not Carrefour SA worldwide.. Portfolio SKU and country totals are not AI coverage denominators; no autonomous price-setting authority established. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Original report page70 describes an implemented pricing engine considering scenarios, recommendations and promotions. No autonomous pricing authority or measured causal impact inferred.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ba2e7e92388561255cd72aa4bbf1580d5992cc07ea431a3afd726ce2dfa6aee0", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-MAF03", "companyId": "majid-al-futtaim", "sourceId": "RET-S03", "dimension": "knowledge", "claim": "MAF uses AI text analysis to classify customer feedback into operating issues for its retail marketing team.", "url": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "originalSourceUrl": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "sourceTitle": "Majid Al Futtaim Sustainability Report 2024", "corroboratingSourceIds": [], "publisher": "Majid Al Futtaim", "sourceType": "sustainability-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "Physical/printed70, third solution in spotlight", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Majid Al Futtaim group; claims restricted to Retail, named store, HR or training scope. Not Carrefour SA worldwide.. No maintained organizational knowledge system or evaluation protocol; reported speed comparison not independently verified. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Original report page70 describes AI analysis classifying customer feedback into operating issues. This supports internal information use, not an enterprise-wide maintained knowledge system.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "403067dcccd4af32b5c28d03ae4b9d000f1ba3e963ff3015940d7aa2b3194fb8", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-MAF04", "companyId": "majid-al-futtaim", "sourceId": "RET-S03", "dimension": "interfaces", "claim": "MAF uses AI-assisted screening questions and job-fit scores in recruitment.", "url": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "originalSourceUrl": "https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2", "sourceTitle": "Majid Al Futtaim Sustainability Report 2024", "corroboratingSourceIds": [], "publisher": "Majid Al Futtaim", "sourceType": "sustainability-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "Physical/printed76, Talent Acquisition", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Majid Al Futtaim group; claims restricted to Retail, named store, HR or training scope. Not Carrefour SA worldwide.. No audited absence of bias or autonomous hiring decision inferred. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Original report page76 describes AI-assisted screening questions and job-fit scores. Mechanism qualifies; no claim of absence of bias, human decision authority or audited hiring benefit.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "71cfce74518ea5e46d7af718deb55d84822753648654c3d6ea27222ea1693c46", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-MAF05", "companyId": "majid-al-futtaim", "sourceId": "RET-S04", "dimension": "interfaces", "claim": "Carrefour City+ launched AI-assisted item recognition and automatic basket charging at a named Dubai store.", "url": "https://www.majidalfuttaim.com/en/media-centre/press-releases/detail/2021/09/carrefour-city-plus", "originalSourceUrl": "https://www.majidalfuttaim.com/en/media-centre/press-releases/detail/2021/09/carrefour-city-plus", "sourceTitle": "Carrefour City+ launch", "corroboratingSourceIds": [], "publisher": "Majid Al Futtaim", "sourceType": "corporate-operating-announcement", "publicationDate": "2021-09-06", "accessedDate": "2026-10-10", "locator": "Launch body describing Mall of the Emirates store and checkout flow", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Majid Al Futtaim group; claims restricted to Retail, named store, HR or training scope. Not Carrefour SA worldwide.. 2021 single-store evidence; current continuity and wider rollout unconfirmed. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Full2021 company release describes opened City+ store with item recognition and automatic basket/payment workflow. 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Not Carrefour SA worldwide.. UAE supermarkets were future expansion; not every Carrefour store or privacy assurance. Application: operator-service-delivery.", "access": "full-source-read | Review: full-source-read", "rationale": "Full May2025 release explicitly describes deployed audience AI with3D sensors and retail-media targeting in UAE hypermarkets. Future supermarket rollout is not achieved coverage.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "9484474a69561f16df7d4bf0b744f029bd7e61eb5a4c7e3a5c12a36b3a6b15c4", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": 3, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": 2, "governance": null }, "points": 14, "score": 52, "coverage": 5, "rank": 2, "sourceCount": 3, "searchBoundary": { "companyId": "majid-al-futtaim", "date": "2026-10-10", "queries": [ "site:majidalfuttaim.com artificial intelligence Academy2024" ], "passagesChecked": "2024 PDF AI passages physical61,70,76,80–81 inspected; pricing, recruitment, feedback and training distinguished. 2021 City+ and 2025 Advertima full releases read.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. 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Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "al-meera", "evidence": [ { "id": "RET-ALMEERA01", "companyId": "al-meera", "sourceId": "RET-S09", "dimension": "interfaces", "claim": "Al Meera reports launched smart shopping carts that support login, item scanning and purchase completion.", "url": "https://sacorporatewebprodqc001.blob.core.windows.net/public/documents/Al%20Meera%20AR%202024%2013%20APR%202025%20ENG%20FINAL%20(1).pdf", "originalSourceUrl": "https://sacorporatewebprodqc001.blob.core.windows.net/public/documents/Al%20Meera%20AR%202024%2013%20APR%202025%20ENG%20FINAL%20(1).pdf", "sourceTitle": "Al Meera Annual Report 2024", "corroboratingSourceIds": [], "publisher": "Al Meera", "sourceType": "annual-report", "publicationDate": "2025", "accessedDate": "2026-10-10", "locator": "Physical9–10, printed14–17, Chairman and Board reports", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Al Meera Q.P.S.C.; named Qatar retail implementations, not assumed across Oman operations.. No store-count coverage or causal commercial result; AI mechanism itself not specified. Application: operator-service-delivery.", "access": "full-source-read | Review: full-source-read", "rationale": "Original company-linked2024 annual PDF pages9-10 describes introduced smart carts, login, scanning and checkout in an explicitly AI-framed retail passage. No assumption of autonomous item recognition or disclosed model accuracy; distinct from the2023 autonomous-store case.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "ff4b8839e75dbdbfe3504d623ddd818bc54ba9aac5395f6debf3be8d0a9a8cb2", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-ALMEERA02", "companyId": "al-meera", "sourceId": "RET-S10", "dimension": "ecosystem", "claim": "Al Meera selected Zippin for a named AI checkout-free store project.", "url": "https://www.getzippin.com/blog/al-meera-partners-with-zippin-to-open-first-checkout-free-stores-in-qatar", "originalSourceUrl": "https://www.getzippin.com/blog/al-meera-partners-with-zippin-to-open-first-checkout-free-stores-in-qatar", "sourceTitle": "Al Meera partners with Zippin", "corroboratingSourceIds": [], "publisher": "Zippin", "sourceType": "partner-announcement", "publicationDate": "2022-08-22", "accessedDate": "2026-10-10", "locator": "Announcement body on planned stores and checkout-free technology", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Al Meera Q.P.S.C.; named Qatar retail implementations, not assumed across Oman operations.. Vendor announcement is not independent support or implementation evidence. Later annual report records opening but does not name technology partner. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Full Zippin announcement names the partner and AI checkout project but describes future opening. A later Al Meera store description does not itself confirm partner continuity for this separate claim.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "5e8124eeffa18a3d26c6183a98a16fb385f38b57bde27968e2dddbc6d5d40c58", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": 1, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 4, "score": 15, "coverage": 2, "rank": 3, "sourceCount": 2, "searchBoundary": { "companyId": "al-meera", "date": "2026-10-10", "queries": [ "site:almeera.com.qa Smart2023 artificial" ], "passagesChecked": "Old2024 report URL redirected; recovered full company-CDN PDF via new corporate annual-report page. Physical9–10 read. 2025 report click failed. Zippin2022 full future-plan announcement read.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S09", "RET-S10" ] }, "audit": { "eligibleClaims": 2, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 0, "latestSourceDate": "2025", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "RET-ALMEERA02" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "RET-ALMEERA01" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://sacorporatewebprodqc001.blob.core.windows.net/public/documents/Al%20Meera%20AR%202024%2013%20APR%202025%20ENG%20FINAL%20(1).pdf", "scoreWithoutSource": 3.7 }, { "url": "https://www.getzippin.com/blog/al-meera-partners-with-zippin-to-open-first-checkout-free-stores-in-qatar", "scoreWithoutSource": 11.11 } ], "scenarioResults": [ { "scenario": "baseline", "score": 14.81, "rank": 3 }, { "scenario": "implemented-only", "score": 11.11, "rank": 3 }, { "scenario": "detail-cap", "score": 11.11, "rank": 3 }, { "scenario": "ai-core-0.5", "score": 15.69, "rank": 3 }, { "scenario": "ai-core-2", "score": 13.33, "rank": 3 }, { "scenario": "workforce-0.5", "score": 15.69, "rank": 3 }, { "scenario": "workforce-2", "score": 13.33, "rank": 3 }, { "scenario": "ecosystem-0.5", "score": 13.73, "rank": 3 }, { "scenario": "ecosystem-2", "score": 16.67, "rank": 3 }, { "scenario": "interfaces-0.5", "score": 9.8, "rank": 3 }, { "scenario": "interfaces-2", "score": 23.33, "rank": 3 }, { "scenario": "lineage-0.5", "score": 15.69, "rank": 3 }, { "scenario": "lineage-2", "score": 13.33, "rank": 3 }, { "scenario": "experimentation-0.5", "score": 15.69, "rank": 3 }, { "scenario": "experimentation-2", "score": 13.33, "rank": 3 }, { "scenario": "autonomy-0.5", "score": 15.69, "rank": 3 }, { "scenario": "autonomy-2", "score": 13.33, "rank": 3 }, { "scenario": "knowledge-0.5", "score": 15.69, "rank": 3 }, { "scenario": "knowledge-2", "score": 13.33, "rank": 3 }, { "scenario": "governance-0.5", "score": 15.69, "rank": 3 }, { "scenario": "governance-2", "score": 13.33, "rank": 3 } ], "scenarioRankRange": [ 3, 3 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "mezzan", "name": "Mezzan Holding", "country": "Kuwait", "sector": "Retail & Consumer", "scope": "Mezzan Holding group; supplier questionnaire workflow, not autonomous supplier risk decisions.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "mezzan", "evidence": [ { "id": "RET-MEZZAN01", "companyId": "mezzan", "sourceId": "RET-S14", "dimension": "interfaces", "claim": "Mezzan used GenAI to develop an ESG questionnaire, with deployment targeting its top20 high-spend suppliers.", "url": "https://www.mezzan.com/wp-content/uploads/2026/08/Mezzan-Sustainability-Report-2025-English-.pdf", "originalSourceUrl": "https://www.mezzan.com/wp-content/uploads/2026/08/Mezzan-Sustainability-Report-2025-English-.pdf", "sourceTitle": "Mezzan Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "Mezzan Holding", "sourceType": "sustainability-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical53, Case Study: ESG Risk Screening for High-Spend Suppliers", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Mezzan Holding group; supplier questionnaire workflow, not autonomous supplier risk decisions.. GenAI generated the questionnaire; the source does not confirm20 deployments or responses, autonomous supplier classification or supplier compliance. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Original2025 sustainability report physical53 describes completed GenAI development of an ESG questionnaire with specified risk domains. This narrow drafting mechanism supports3; rollout targeted20 suppliers but completed deployment, responses and ongoing AI screening are not established.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "9e6675898ef99e7ffb888c872151af6bad44cbba29f600bbea93df9c91bbf17f", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 3, "score": 11, "coverage": 1, "rank": 4, "sourceCount": 1, "searchBoundary": { "companyId": "mezzan", "date": "2026-10-10", "queries": [ "site:mezzan.com artificial intelligence sustainability2025" ], "passagesChecked": "2025 sustainability physical53 questionnaire case read; general ERP future rollout and governance not AI controls. Arabic annual download attempt failed.", "languages": [ "English", "Arabic" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S14" ] }, "audit": { "eligibleClaims": 1, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2026", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "RET-MEZZAN01" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.mezzan.com/wp-content/uploads/2026/08/Mezzan-Sustainability-Report-2025-English-.pdf", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 11.11, "rank": 4 }, { "scenario": "implemented-only", "score": 11.11, "rank": 3 }, { "scenario": "detail-cap", "score": 7.41, "rank": 4 }, { "scenario": "ai-core-0.5", "score": 11.76, "rank": 4 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 4 }, { "scenario": "workforce-0.5", "score": 11.76, "rank": 4 }, { "scenario": "workforce-2", "score": 10.0, "rank": 4 }, { "scenario": "ecosystem-0.5", "score": 11.76, "rank": 4 }, { "scenario": "ecosystem-2", "score": 10.0, "rank": 4 }, { "scenario": "interfaces-0.5", "score": 5.88, "rank": 5 }, { "scenario": "interfaces-2", "score": 20.0, "rank": 4 }, { "scenario": "lineage-0.5", "score": 11.76, "rank": 4 }, { "scenario": "lineage-2", "score": 10.0, "rank": 4 }, { "scenario": "experimentation-0.5", "score": 11.76, "rank": 4 }, { "scenario": "experimentation-2", "score": 10.0, "rank": 4 }, { "scenario": "autonomy-0.5", "score": 11.76, "rank": 4 }, { "scenario": "autonomy-2", "score": 10.0, "rank": 4 }, { "scenario": "knowledge-0.5", "score": 11.76, "rank": 4 }, { "scenario": "knowledge-2", "score": 10.0, "rank": 4 }, { "scenario": "governance-0.5", "score": 11.76, "rank": 4 }, { "scenario": "governance-2", "score": 10.0, "rank": 5 } ], "scenarioRankRange": [ 3, 5 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "baladna", "name": "Baladna", "country": "Qatar", "sector": "Retail & Consumer", "scope": "Baladna Q.P.S.C.; Qatar farming and production plans. Foreign projects not assumed operational.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "baladna", "evidence": [ { "id": "RET-BALADNA01", "companyId": "baladna", "sourceId": "RET-S11", "dimension": "ai-core", "claim": "Baladna planned an AI-supported integrated utilities-metering system.", "url": "https://baladna.com/storage/uploads/iKUuHjMbSSYZfRre3VkWieoWq.pdf", "originalSourceUrl": "https://baladna.com/storage/uploads/iKUuHjMbSSYZfRre3VkWieoWq.pdf", "sourceTitle": "Baladna Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Baladna", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical41, printed80–81, Looking ahead to2026", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Baladna Q.P.S.C.; Qatar farming and production plans. Foreign projects not assumed operational.. Not installed AI; current water/energy savings cannot be attributed to it. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Annual2025 physical41 places AI-supported integrated metering under Looking Ahead2026. Investment and intended monitoring are specific commitment, not proven deployment.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "29d5581d9284a92d2163cbdc239bc159516db4f44871935ca679bb7c93d33849", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "RET-BALADNA02", "companyId": "baladna", "sourceId": "RET-S11", "dimension": "interfaces", "claim": "Baladna planned AI-capable feed-management software integrated with inventory, ERP and feed-delivery equipment.", "url": "https://baladna.com/storage/uploads/iKUuHjMbSSYZfRre3VkWieoWq.pdf", "originalSourceUrl": "https://baladna.com/storage/uploads/iKUuHjMbSSYZfRre3VkWieoWq.pdf", "sourceTitle": "Baladna Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Baladna", "sourceType": "annual-report", "publicationDate": "2026", "accessedDate": "2026-10-10", "locator": "Physical39, printed76–77, Looking ahead to2026", "status": "planned", "level": 1, "eligible": true, "limitation": "Claim scope: Baladna Q.P.S.C.; Qatar farming and production plans. Foreign projects not assumed operational.. No completed deployment, benefits or international rollout inferred. Application: internal-operating-practice.", "access": "full-source-read | Review: full-source-read", "rationale": "Annual2025 physical39 explicitly says feed-management software will be implemented and integrated. Retain planned anchor; other completed farming technology is not this AI practice.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "ad76902c3d88f6ce29c912404610d3768d11b9faaff1d37a0af40355ef613276", "review": { "decision": "retain", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 1, "workforce": null, "ecosystem": null, "interfaces": 1, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 2, "score": 7, "coverage": 2, "rank": 5, "sourceCount": 1, "searchBoundary": { "companyId": "baladna", "date": "2026-10-10", "queries": [ "site:baladna.com AI annual report2025" ], "passagesChecked": "2025 report extracted; physical20,26,34,39,41 AI passages read. Market-level AI and future plans separated; metering/feed integration retained as planned.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S11" ] }, "audit": { "eligibleClaims": 2, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2026", "lineage": [ { "dimension": "ai-core", "level": 1, "winningClaimIds": [ "RET-BALADNA01" ], "contribution": 3.7037 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 1, "winningClaimIds": [ "RET-BALADNA02" ], "contribution": 3.7037 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://baladna.com/storage/uploads/iKUuHjMbSSYZfRre3VkWieoWq.pdf", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 7.41, "rank": 5 }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 7.41, "rank": 4 }, { "scenario": "ai-core-0.5", "score": 5.88, "rank": 6 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 4 }, { "scenario": "workforce-0.5", "score": 7.84, "rank": 5 }, { "scenario": "workforce-2", "score": 6.67, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 7.84, "rank": 5 }, { "scenario": "ecosystem-2", "score": 6.67, "rank": 5 }, { "scenario": "interfaces-0.5", "score": 5.88, "rank": 5 }, { "scenario": "interfaces-2", "score": 10.0, "rank": 5 }, { "scenario": "lineage-0.5", "score": 7.84, "rank": 5 }, { "scenario": "lineage-2", "score": 6.67, "rank": 5 }, { "scenario": "experimentation-0.5", "score": 7.84, "rank": 5 }, { "scenario": "experimentation-2", "score": 6.67, "rank": 5 }, { "scenario": "autonomy-0.5", "score": 7.84, "rank": 5 }, { "scenario": "autonomy-2", "score": 6.67, "rank": 5 }, { "scenario": "knowledge-0.5", "score": 7.84, "rank": 5 }, { "scenario": "knowledge-2", "score": 6.67, "rank": 5 }, { "scenario": "governance-0.5", "score": 7.84, "rank": 5 }, { "scenario": "governance-2", "score": 6.67, "rank": 6 } ], "scenarioRankRange": [ 4, 6 ], "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "extra", "name": "United Electronics Company (eXtra)", "country": "Saudi Arabia", "sector": "Retail & Consumer", "scope": "United Electronics Company; governance evidence at parent level, not automatically subsidiary consumer-finance deployment.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "extra", "evidence": [ { "id": "RET-EXTRA01", "companyId": "extra", "sourceId": "RET-S02", "dimension": "governance", "claim": "An issuer notice describes supervision of an Executive Management Committee for Data and AI during fiscal 2024.", "url": "https://www.saudiexchange.sa/wps/portal/saudiexchange/newsandreports/issuer-news/issuer-announcements/issuer-announcements-details/?anCat=1&anId=86829&cs=4003&locale=en", "originalSourceUrl": "https://www.saudiexchange.sa/wps/portal/saudiexchange/newsandreports/issuer-news/issuer-announcements/issuer-announcements-details/?anCat=1&anId=86829&cs=4003&locale=en", "sourceTitle": "eXtra addendum to EGM invitation", "corroboratingSourceIds": [], "publisher": "United Electronics Company via Saudi Exchange", "sourceType": "issuer-disclosure", "publicationDate": "2025-04-30", "accessedDate": "2026-10-10", "locator": "Additional information, board-member remuneration item", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: United Electronics Company; governance evidence at parent level, not automatically subsidiary consumer-finance deployment.. Remuneration proposal is not proof of approved payment or AI control effectiveness. Application: internal-operating-practice.", "access": "full-source-read", "rationale": "Specific existing AI oversight committee and reporting period.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "08b29191ae3cd673b22e22a64dc8c02ea2440edb6229b35b205bcf5a1683b771", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": 2 }, "points": 2, "score": 7, "coverage": 1, "rank": 5, "sourceCount": 1, "searchBoundary": { "companyId": "extra", "date": "2026-10-10", "queries": [ "United Electronics Company artificial intelligence" ], "passagesChecked": "Issuer committee paragraph fully read. Arabic company name+الذكاء الاصطناعي results mainly news-platform AI summaries, excluded. No operational AI inferred from committee.", "languages": [ "English", "Arabic" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S02" ] }, "audit": { "eligibleClaims": 1, "excludedClaims": 0, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2025-04-30", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 2, "winningClaimIds": [ "RET-EXTRA01" ], "contribution": 7.4074 } ], "singleSourceRemoval": [ { "url": "https://www.saudiexchange.sa/wps/portal/saudiexchange/newsandreports/issuer-news/issuer-announcements/issuer-announcements-details/?anCat=1&anId=86829&cs=4003&locale=en", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 7.41, "rank": 5 }, { "scenario": "implemented-only", "score": 7.41, "rank": 5 }, { "scenario": "detail-cap", "score": 7.41, "rank": 4 }, { "scenario": "ai-core-0.5", "score": 7.84, "rank": 5 }, { "scenario": "ai-core-2", "score": 6.67, "rank": 6 }, { "scenario": "workforce-0.5", "score": 7.84, "rank": 5 }, { "scenario": "workforce-2", "score": 6.67, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 7.84, "rank": 5 }, { "scenario": "ecosystem-2", "score": 6.67, "rank": 5 }, { "scenario": "interfaces-0.5", "score": 7.84, "rank": 4 }, { "scenario": "interfaces-2", "score": 6.67, "rank": 6 }, { "scenario": "lineage-0.5", "score": 7.84, "rank": 5 }, { "scenario": "lineage-2", "score": 6.67, "rank": 5 }, { "scenario": "experimentation-0.5", "score": 7.84, "rank": 5 }, { "scenario": "experimentation-2", "score": 6.67, "rank": 5 }, { "scenario": "autonomy-0.5", "score": 7.84, "rank": 5 }, { "scenario": "autonomy-2", "score": 6.67, "rank": 5 }, { "scenario": "knowledge-0.5", "score": 7.84, "rank": 5 }, { "scenario": "knowledge-2", "score": 6.67, "rank": 5 }, { "scenario": "governance-0.5", "score": 3.92, "rank": 6 }, { "scenario": "governance-2", "score": 13.33, "rank": 3 } ], "scenarioRankRange": [ 3, 6 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "alshaya", "name": "Alshaya Group", "country": "Kuwait", "sector": "Retail & Consumer", "scope": "M.H. Alshaya operating group; retail-media service evidence not inherited by every franchise brand.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "alshaya", "evidence": [ { "id": "RET-ALSHAYA01", "companyId": "alshaya", "sourceId": "RET-S13", "dimension": "interfaces", "claim": "Alshaya Media Connect advertises an AI-enabled retail-media service combining loyalty purchase data and intent signals.", "url": "https://www.alshaya.com/en/alshaya-media-connect/", "originalSourceUrl": "https://www.alshaya.com/en/alshaya-media-connect/", "sourceTitle": "Alshaya Media Connect service page", "corroboratingSourceIds": [], "publisher": "Alshaya", "sourceType": "corporate-service-page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Offsite Display & Video service description", "status": "planned", "level": null, "eligible": false, "limitation": "Claim scope: M.H. Alshaya operating group; retail-media service evidence not inherited by every franchise brand.. Service catalogue alone does not establish actual operating deployment; no transfer to franchise stores. Application: operator-service-offering. Exclusion: New claim has no explicit accepted review decision. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full-source-read | Review: full-source-read", "rationale": "Full undated service catalogue advertises AI-enabled retail-media offering. It does not establish actual operating deployment or a specific internal implementation commitment. Product availability alone is insufficient under the public-method boundary. Exclusion: New claim has no explicit accepted review decision. No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 1, "exclusionReasons": [ "New claim has no explicit accepted review decision.", "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "cb258c98269f49af51d7c3231086248cc9c35505f51610ebf5dc7462728b28d4", "review": { "decision": "exclude", "record": "expansion/retail/method-review.json", "exactClaimBinding": true, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 1, "searchBoundary": { "companyId": "alshaya", "date": "2026-10-10", "queries": [ "site:alshaya.com artificial intelligence Media Connect" ], "passagesChecked": "2024launch and current service catalogue read. Future-tense/advertised offering coded only1. Publicly indexed UAT supplier portal is not production deployment evidence and was not used.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S13", "RET-S12" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 1, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "bmmi", "name": "BMMI", "country": "Bahrain", "sector": "Retail & Consumer", "scope": "BMMI B.S.C. group consumer distribution, retail and services; not similarly abbreviated institutions.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "bmmi", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": { "companyId": "bmmi", "date": "2026-10-10", "queries": [ "site:bmmigroup.com artificial intelligence annual report2025" ], "passagesChecked": "Results archive read;2025 annual/ESG download failed, direct server403. Accessible2024 annual searched artificial/digital without match. No internal AI inference from external-event participation.", "languages": [ "English" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S15", "RET-S16" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "jarir", "name": "Jarir Marketing Company", "country": "Saudi Arabia", "sector": "Retail & Consumer", "scope": "Listed Jarir Marketing Company; selling AI-enabled devices is not internal AI adoption.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "jarir", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": { "companyId": "jarir", "date": "2026-10-10", "queries": [ "site:jarir.com artificial intelligence annual report2025" ], "passagesChecked": "2025 annual physical12 plan to sell AI products and personalization text screened. Arabic company+الذكاء الاصطناعي found media interview lead, not full company primary source.", "languages": [ "English", "Arabic" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S01" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "trafco", "name": "Trafco Group", "country": "Bahrain", "sector": "Retail & Consumer", "scope": "Trafco Group B.S.C.; consumer distribution and controlled food/logistics operations; scope varies by subsidiary.", "scopeLimitations": "Company/group scope follows each cited claim. Headquarters is not the geography of all operations. Public disclosures and search completeness differ; no national or population inference.", "cluster": "trafco", "evidence": [], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 0, "score": 0, "coverage": 0, "rank": null, "sourceCount": 0, "searchBoundary": { "companyId": "trafco", "date": "2026-10-10", "queries": [ "site:trafco.com artificial intelligence annual report2025" ], "passagesChecked": "2025 annual searched artificial/digital/technology; general cold-store/dairy technology not explicit AI. Arabic query results news-site AI summary labels excluded.", "languages": [ "English", "Arabic" ], "searchCompleteness": "Bounded, non-exhaustive. Nine-theme lens applied to source passages; not nine independent exhaustive searches.", "reviewedSourceIds": [ "RET-S17" ] }, "audit": { "eligibleClaims": 0, "excludedClaims": 0, "eligibleSourceUrls": 0, "undatedEligibleClaims": 0, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [], "scenarioResults": [ { "scenario": "baseline", "score": 0.0, "rank": null }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 0.0, "rank": null }, { "scenario": "ai-core-0.5", "score": 0.0, "rank": null }, { "scenario": "ai-core-2", "score": 0.0, "rank": null }, { "scenario": "workforce-0.5", "score": 0.0, "rank": null }, { "scenario": "workforce-2", "score": 0.0, "rank": null }, { "scenario": "ecosystem-0.5", "score": 0.0, "rank": null }, { "scenario": "ecosystem-2", "score": 0.0, "rank": null }, { "scenario": "interfaces-0.5", "score": 0.0, "rank": null }, { "scenario": "interfaces-2", "score": 0.0, "rank": null }, { "scenario": "lineage-0.5", "score": 0.0, "rank": null }, { "scenario": "lineage-2", "score": 0.0, "rank": null }, { "scenario": "experimentation-0.5", "score": 0.0, "rank": null }, { "scenario": "experimentation-2", "score": 0.0, "rank": null }, { "scenario": "autonomy-0.5", "score": 0.0, "rank": null }, { "scenario": "autonomy-2", "score": 0.0, "rank": null }, { "scenario": "knowledge-0.5", "score": 0.0, "rank": null }, { "scenario": "knowledge-2", "score": 0.0, "rank": null }, { "scenario": "governance-0.5", "score": 0.0, "rank": null }, { "scenario": "governance-2", "score": 0.0, "rank": null } ], "scenarioRankRange": null, "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "eand-group", "name": "e& group", "country": "UAE", "sector": "Telecommunications", "scope": "Emirates Telecommunications Group Company PJSC (e&), consolidated group", "scopeLimitations": "Evidence may concern e& UAE or other subsidiaries; group evidence cannot describe UAE enterprises generally.", "cluster": "eand-family", "evidence": [ { "id": "TEL-009", "companyId": "eand-group", "sourceId": "T-S06", "dimension": "governance", "claim": "e& describes central AI use-case review by a steering committee with use-case owners and risk thresholds.", "url": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "originalSourceUrl": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "sourceTitle": "e& Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "e&", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed p.128 / PDF page index129, Responsible AI", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates Telecommunications Group Company PJSC (e&), consolidated group. Report also lists full governance-platform rollout across all units as a 2026 priority; not proof of universal implementation. Application: internal.", "access": "full_page | Review: full_pdf", "rationale": "PDF p129 specifies operational committee review and risk criteria. Group claim; incomplete rollout remains possible.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "5388a5dcd069d5ce9f04144acf44403ee833de8d924886dcd278012aadff6b07", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-010", "companyId": "eand-group", "sourceId": "T-S06", "dimension": "lineage", "claim": "e& reports its governance platform supports continuous monitoring and audit trails.", "url": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "originalSourceUrl": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "sourceTitle": "e& Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "e&", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed p.128 / PDF page index129, AI risk framework", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Emirates Telecommunications Group Company PJSC (e&), consolidated group. Audit-trail statement does not establish reproducibility of every model decision; full unit rollout still a priority. Application: internal.", "access": "full_page | Review: full_pdf", "rationale": "Audit-trail statement explicit on p129; not independent proof of trace completeness.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "8461e9ed28b7396541420d90ea25865133683590446a6f263fcd9130842dae94", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-011", "companyId": "eand-group", "sourceId": "T-S06", "dimension": "workforce", "claim": "e& launched mandatory responsible-AI training in September 2025 and reports Citizen X employee model-building activity.", "url": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "originalSourceUrl": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "sourceTitle": "e& Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "e&", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed p.128 / PDF page index129, Embedding responsible AI across workforce", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Emirates Telecommunications Group Company PJSC (e&), consolidated group. Training mandate is not completion; employee-built models are not automatically production deployments. Application: internal.", "access": "full_page | Review: full_pdf", "rationale": "p129 names launched training and employee model-building coverage. Not a tested competence result.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "e780e2a7384f64f82e077663e52b3f949cf71486913935fbad231df3b2c0ed23", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-012", "companyId": "eand-group", "sourceId": "T-S07", "dimension": "autonomy", "claim": "e& UAE and TM Forum published a roadmap towards Level 4/5 autonomous networks with human governance and policy controls.", "url": "https://www.eand.com/en/news/22-06-26-eand-tm-strategic-blueprint.html", "originalSourceUrl": "https://www.eand.com/en/news/22-06-26-eand-tm-strategic-blueprint.html", "sourceTitle": "e& and TM Forum announce strategic blueprint for Autonomous Networks", "corroboratingSourceIds": [], "publisher": "e&", "sourceType": "company_press_release", "publicationDate": "2026-06-22", "accessedDate": "2026-10-10", "locator": "Main release, roadmap paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: e& UAE network operations. Roadmap is not achieved enterprise-wide autonomy; applies to e& UAE, not all e& subsidiaries. Application: internal-planned.", "access": "full_page", "rationale": "Specific technical transformation roadmap.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "564675156ecf92088d48036f78e372e0ac9e4baff695c0359bdc1c0857ac2559", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-M01", "companyId": "eand-group", "sourceId": "T-S06", "dimension": "ai-core", "claim": "e& reports deployed AI network optimization, predictive maintenance and AI-based call protection.", "url": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "originalSourceUrl": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "sourceTitle": "e& Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "e&", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed p129 / PDF index130, Transforming our networks; Strengthening security", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: e& consolidated group; exact operating-entity coverage not itemized. Company-reported; not independently verified. Group scope cannot establish each local subsidiary practice. Added by separate AI source-review pass. Application: internal.", "access": "full_page | Review: full_primary_pdf_readable", "rationale": "Independently reopened the readable primary PDF. PDF index130 (printed129), Transforming our networks through AI, names Huawei 5G-Advanced AI Core, NWDAF for real-time optimization and MDAF for predictive maintenance; Strengthening security and fraud protection explicitly states deployment of Mavenir CallShield using sentiment analysis, call profiling and pattern detection for real-time blocking. This supports implemented internal network practice plus concrete operating mechanisms at level3. Do not infer universal subsidiary coverage, total eligible-network coverage, independent performance validation or enterprise maturity. Reported outcome percentages are unnecessary to this anchor.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "cec48c05dc82afba393e56d0c0757b474b0a11f26d03862ecd7f5b44f560ae31", "review": { "decision": "accept", "record": "research/root-additions-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-M02", "companyId": "eand-group", "sourceId": "T-S06", "dimension": "interfaces", "claim": "e& identifies Agentic Billing Super Advisor among deployed internal AI solutions.", "url": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "originalSourceUrl": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "sourceTitle": "e& Integrated Annual Report 2025", "corroboratingSourceIds": [], "publisher": "e&", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed p129 / PDF index130, Key deployments; internal expansion paragraph", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: e& consolidated group; specific deployment business unit not identified. Company-reported; not independently verified. Group scope cannot establish each local subsidiary practice. Added by separate AI source-review pass. Application: internal.", "access": "full_page", "rationale": "Explicit deployed internal workflow; outcome claims separate.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "213a443cbeb43c4ef2d397636fe9829cf325e284b5fef186173b17bd7ea6bf68", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": 3, "ecosystem": null, "interfaces": 2, "lineage": 2, "experimentation": null, "autonomy": 1, "knowledge": null, "governance": 3 }, "points": 14, "score": 52, "coverage": 6, "rank": 1, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 6, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 5, "latestSourceDate": "2026-06-22", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "TEL-M01" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": 3, "winningClaimIds": [ "TEL-011" ], "contribution": 11.1111 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 2, "winningClaimIds": [ "TEL-M02" ], "contribution": 7.4074 }, { "dimension": "lineage", "level": 2, "winningClaimIds": [ "TEL-010" ], "contribution": 7.4074 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": 1, "winningClaimIds": [ "TEL-012" ], "contribution": 3.7037 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 3, "winningClaimIds": [ "TEL-009" ], "contribution": 11.1111 } ], "singleSourceRemoval": [ { "url": "https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf", "scoreWithoutSource": 3.7 }, { "url": "https://www.eand.com/en/news/22-06-26-eand-tm-strategic-blueprint.html", "scoreWithoutSource": 48.15 } ], "scenarioResults": [ { "scenario": "baseline", "score": 51.85, "rank": 1 }, { "scenario": "implemented-only", "score": 48.15, "rank": 1 }, { "scenario": "detail-cap", "score": 40.74, "rank": 1 }, { "scenario": "ai-core-0.5", "score": 49.02, "rank": 1 }, { "scenario": "ai-core-2", "score": 56.67, "rank": 1 }, { "scenario": "workforce-0.5", "score": 49.02, "rank": 1 }, { "scenario": "workforce-2", "score": 56.67, "rank": 1 }, { "scenario": "ecosystem-0.5", "score": 54.9, "rank": 1 }, { "scenario": "ecosystem-2", "score": 46.67, "rank": 1 }, { "scenario": "interfaces-0.5", "score": 50.98, "rank": 1 }, { "scenario": "interfaces-2", "score": 53.33, "rank": 1 }, { "scenario": "lineage-0.5", "score": 50.98, "rank": 1 }, { "scenario": "lineage-2", "score": 53.33, "rank": 1 }, { "scenario": "experimentation-0.5", "score": 54.9, "rank": 1 }, { "scenario": "experimentation-2", "score": 46.67, "rank": 1 }, { "scenario": "autonomy-0.5", "score": 52.94, "rank": 1 }, { "scenario": "autonomy-2", "score": 50.0, "rank": 1 }, { "scenario": "knowledge-0.5", "score": 54.9, "rank": 1 }, { "scenario": "knowledge-2", "score": 46.67, "rank": 1 }, { "scenario": "governance-0.5", "score": 49.02, "rank": 1 }, { "scenario": "governance-2", "score": 56.67, "rank": 1 } ], "scenarioRankRange": [ 1, 1 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "beyon", "name": "Beyon / Batelco", "country": "Bahrain", "sector": "Telecommunications", "scope": "Beyon B.S.C., consolidated group including Batelco by Beyon", "scopeLimitations": "Batelco local operations and Beyon Cyber subsidiary claims must not imply whole-group adoption.", "cluster": "beyon-family", "evidence": [ { "id": "TEL-031", "companyId": "beyon", "sourceId": "T-S21", "dimension": "interfaces", "claim": "Batelco expanded Basma to handle billing, device searches and fraud-reporting support through backend integration.", "url": "https://beyon.com/wp-content/uploads/2026/03/Beyon_ESG2025_English.pdf", "originalSourceUrl": "https://beyon.com/wp-content/uploads/2026/03/Beyon_ESG2025_English.pdf", "sourceTitle": "Beyon Sustainability Report 2025", "corroboratingSourceIds": [], "publisher": "Beyon", "sourceType": "company_sustainability_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.58-59 / PDF page index30, Batelco by Beyon", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Batelco by Beyon, Bahrain. No accuracy, escalation or eligible-interaction denominator. Application: internal.", "access": "full_page | Review: full_pdf", "rationale": "p30 describes Basma's operating functions and backend integration. Scope is Batelco, not every Beyon subsidiary.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "537d597cd3b23611382b779324cf0c80aec8f6dbd0218215afb014e83e85892c", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-032", "companyId": "beyon", "sourceId": "T-S20", "dimension": "ai-core", "claim": "Batelco reports AI network self-healing, code testing and fraud-management use cases.", "url": "https://beyon.com/wp-content/uploads/2026/03/Beyon-AR2025-ENGLISH.pdf", "originalSourceUrl": "https://beyon.com/wp-content/uploads/2026/03/Beyon-AR2025-ENGLISH.pdf", "sourceTitle": "Beyon Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Beyon", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.26-27 / PDF page index14, Batelco by Beyon", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Batelco by Beyon, Bahrain. Broad aggregate improvement claim lacks metric definitions and attribution. Application: internal.", "access": "full_page | Review: full_pdf", "rationale": "p14 explicitly describes AI in internal operations; broad use cases suffice for 2, not audited benefits.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "20a277ac4fbfbbfa0c6f67c3fb0352c7baa336d7bbb85dc7d6f8dc8bd8758978", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-033", "companyId": "beyon", "sourceId": "T-S20", "dimension": "autonomy", "claim": "Beyon Cyber launched Oryx AI for alert investigation with live queries and human-in-loop oversight.", "url": "https://beyon.com/wp-content/uploads/2026/03/Beyon-AR2025-ENGLISH.pdf", "originalSourceUrl": "https://beyon.com/wp-content/uploads/2026/03/Beyon-AR2025-ENGLISH.pdf", "sourceTitle": "Beyon Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Beyon", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.30-31 / PDF page index16, Beyon Cyber", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: Beyon Cyber managed security operations. Classed service-delivery operating practice, not proof of use in Batelco or group functions; deployment volume unreported. Application: internal-service-delivery.", "access": "full_page | Review: full_pdf", "rationale": "p16 describes AI integrated into Beyon Cyber managed-service delivery, with query orchestration and oversight. Correct name to Orryx AI; do not imply whole-group autonomy.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "654005481bb97133a09feb0e65f5ebfd8191d24f7d3498c586e3105114bf6738", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 2, "workforce": null, "ecosystem": null, "interfaces": 3, "lineage": null, "experimentation": null, "autonomy": 3, "knowledge": null, "governance": null }, "points": 8, "score": 30, "coverage": 3, "rank": 2, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 3, "excludedClaims": 0, "eligibleSourceUrls": 2, "undatedEligibleClaims": 3, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": 2, "winningClaimIds": [ "TEL-032" ], "contribution": 7.4074 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": 3, "winningClaimIds": [ "TEL-031" ], "contribution": 11.1111 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": 3, "winningClaimIds": [ "TEL-033" ], "contribution": 11.1111 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://beyon.com/wp-content/uploads/2026/03/Beyon-AR2025-ENGLISH.pdf", "scoreWithoutSource": 11.11 }, { "url": "https://beyon.com/wp-content/uploads/2026/03/Beyon_ESG2025_English.pdf", "scoreWithoutSource": 18.52 } ], "scenarioResults": [ { "scenario": "baseline", "score": 29.63, "rank": 2 }, { "scenario": "implemented-only", "score": 29.63, "rank": 2 }, { "scenario": "detail-cap", "score": 22.22, "rank": 4 }, { "scenario": "ai-core-0.5", "score": 27.45, "rank": 2 }, { "scenario": "ai-core-2", "score": 33.33, "rank": 2 }, { "scenario": "workforce-0.5", "score": 31.37, "rank": 2 }, { "scenario": "workforce-2", "score": 26.67, "rank": 4 }, { "scenario": "ecosystem-0.5", "score": 31.37, "rank": 2 }, { "scenario": "ecosystem-2", "score": 26.67, "rank": 2 }, { "scenario": "interfaces-0.5", "score": 25.49, "rank": 4 }, { "scenario": "interfaces-2", "score": 36.67, "rank": 2 }, { "scenario": "lineage-0.5", "score": 31.37, "rank": 2 }, { "scenario": "lineage-2", "score": 26.67, "rank": 2 }, { "scenario": "experimentation-0.5", "score": 31.37, "rank": 2 }, { "scenario": "experimentation-2", "score": 26.67, "rank": 2 }, { "scenario": "autonomy-0.5", "score": 25.49, "rank": 4 }, { "scenario": "autonomy-2", "score": 36.67, "rank": 2 }, { "scenario": "knowledge-0.5", "score": 31.37, "rank": 2 }, { "scenario": "knowledge-2", "score": 26.67, "rank": 4 }, { "scenario": "governance-0.5", "score": 31.37, "rank": 2 }, { "scenario": "governance-2", "score": 26.67, "rank": 3 } ], "scenarioRankRange": [ 2, 4 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "ooredoo-group", "name": "Ooredoo group", "country": "Qatar", "sector": "Telecommunications", "scope": "Ooredoo Q.P.S.C., consolidated group", "scopeLimitations": "Group includes multiple MENA markets; Qatar headquarters is not operational scope.", "cluster": "ooredoo-family", "evidence": [ { "id": "TEL-017", "companyId": "ooredoo-group", "sourceId": "T-S11", "dimension": "interfaces", "claim": "Ooredoo reports deploying chatbots and AI-enabled care tools across its markets.", "url": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "originalSourceUrl": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "sourceTitle": "Ooredoo Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Ooredoo", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.4-5 / PDF page index3, Customer Experience", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Ooredoo group, multiple markets unspecified. No country-level coverage, accuracy or containment figures. Application: internal.", "access": "full_page | Review: full_pdf", "rationale": "p3 explicitly reports deployment in customer-care operations. Internal service delivery, not merely product sale.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "942c327aae1ebec8e9151921ad68b1776cbb5c033da403a4f1599af14e57d39a", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-018", "companyId": "ooredoo-group", "sourceId": "T-S11", "dimension": "ecosystem", "claim": "Ooredoo reports expanded collaborations covering AI, cloud and open APIs.", "url": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "originalSourceUrl": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "sourceTitle": "Ooredoo Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Ooredoo", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.4-5 / PDF page index3, Innovation and Platforms", "status": "unclear", "level": null, "eligible": false, "limitation": "Claim scope: Ooredoo Q.P.S.C., consolidated group. Retained contextual statement; excluded from internal evidence scoring. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained.", "access": "full_page", "rationale": "Broad collaboration description lacks specific implemented internal AI mechanism. Exclusion: No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained." ], "claimSha256": "5de9ae13acbe8f39254609ae9f7bf1d683bde485328256ec90d856b68298ee35", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-019", "companyId": "ooredoo-group", "sourceId": "T-S12", "dimension": "ai-core", "claim": "Ooredoo reports sovereign AI cloud launch in Qatar enabled by Syntys and NVIDIA GPUs in June 2025.", "url": "https://www.ooredoo.com/en/media/news_view/ooredoo-group-fy-2025/", "originalSourceUrl": "https://www.ooredoo.com/en/media/news_view/ooredoo-group-fy-2025/", "sourceTitle": "Ooredoo Group FY 2025", "corroboratingSourceIds": [], "publisher": "Ooredoo", "sourceType": "company_results_release", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Syntys / sovereign AI cloud paragraph", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Syntys/Ooredoo Qatar sovereign AI cloud. Commercial launch does not prove client adoption, profitability or internal AI maturity. Application: customer-product. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained.", "access": "full_page", "rationale": "Selling live AI infrastructure is distinct from internal operating-model use. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Customer product alone does not establish internal operating practice.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "67c34801252644f4dc0d62e823d06313b766f088bd0fb449b2c55de2b1a3e9fa", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-M03", "companyId": "ooredoo-group", "sourceId": "T-S11", "dimension": "ai-core", "claim": "Ooredoo reports deployed AI models for personalized customer offers.", "url": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "originalSourceUrl": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "sourceTitle": "Ooredoo Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Ooredoo", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF index10, Foundational Enablers, AI-Driven Transformation", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Ooredoo consolidated group; market coverage not itemized. Company-reported; not independently verified. Group scope cannot establish each local subsidiary practice. Added by separate AI source-review pass. Application: internal.", "access": "full_page", "rationale": "Explicit implemented commercial operating process.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "541dca31593db93aef4869ccfbb88af08f83f06e68fda7fcabc824551d426dfc", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-M04", "companyId": "ooredoo-group", "sourceId": "T-S11", "dimension": "workforce", "claim": "Ooredoo reports AI-driven recruitment and CV screening across operating companies.", "url": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "originalSourceUrl": "https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf", "sourceTitle": "Ooredoo Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Ooredoo", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "PDF index10, Foundational Enablers 1-2", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Ooredoo consolidated group; exact operating companies not itemized. Company-reported; not independently verified. Group scope cannot establish each local subsidiary practice. Added by separate AI source-review pass. 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Company-reported; not independently verified. Group scope cannot establish each local subsidiary practice. Added by separate AI source-review pass. 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(stc Kuwait), local consolidated operations. Do not equate digitization or cloud migration with AI-native capability. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained.", "access": "full_page", "rationale": "Generic AI phrasing does not establish AI in the individually listed digital projects. Exclusion: No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained." ], "claimSha256": "a16ddcbc5bedc9778d55a9b54e78ce02c81a3c2379698d310264d62a9e92699c", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": 2, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": 1, "autonomy": null, "knowledge": null, "governance": 2 }, "points": 5, "score": 19, "coverage": 3, "rank": 5, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 3, "excludedClaims": 1, "eligibleSourceUrls": 1, "undatedEligibleClaims": 3, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": 2, "winningClaimIds": [ "TEL-028" ], "contribution": 7.4074 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "TEL-029" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": 2, "winningClaimIds": [ "TEL-027" ], "contribution": 7.4074 } ], "singleSourceRemoval": [ { "url": "https://cws.stc.com.kw/DigitalStatic/AnnualReport2025/index.html", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 18.52, "rank": 5 }, { "scenario": "implemented-only", "score": 14.81, "rank": 5 }, { "scenario": "detail-cap", "score": 18.52, "rank": 5 }, { "scenario": "ai-core-0.5", "score": 19.61, "rank": 5 }, { "scenario": "ai-core-2", "score": 16.67, "rank": 6 }, { "scenario": "workforce-0.5", "score": 15.69, "rank": 5 }, { "scenario": "workforce-2", "score": 23.33, "rank": 5 }, { "scenario": "ecosystem-0.5", "score": 19.61, "rank": 5 }, { "scenario": "ecosystem-2", "score": 16.67, "rank": 5 }, { "scenario": "interfaces-0.5", "score": 19.61, "rank": 5 }, { "scenario": "interfaces-2", "score": 16.67, "rank": 5 }, { "scenario": "lineage-0.5", "score": 19.61, "rank": 5 }, { "scenario": "lineage-2", "score": 16.67, "rank": 5 }, { "scenario": "experimentation-0.5", "score": 17.65, "rank": 5 }, { "scenario": "experimentation-2", "score": 20.0, "rank": 5 }, { "scenario": "autonomy-0.5", "score": 19.61, "rank": 5 }, { "scenario": "autonomy-2", "score": 16.67, "rank": 6 }, { "scenario": "knowledge-0.5", "score": 19.61, "rank": 5 }, { "scenario": "knowledge-2", "score": 16.67, "rank": 5 }, { "scenario": "governance-0.5", "score": 15.69, "rank": 5 }, { "scenario": "governance-2", "score": 23.33, "rank": 5 } ], "scenarioRankRange": [ 5, 6 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "stc-bahrain", "name": "stc Bahrain", "country": "Bahrain", "sector": "Telecommunications", "scope": "stc Bahrain Company B.S.C. (closed), Bahrain operations", "scopeLimitations": "Wholly owned subsidiary of stc group per 2024 report; parent policies not automatically local implementation.", "cluster": "stc-family", "evidence": [ { "id": "TEL-034", "companyId": "stc-bahrain", "sourceId": "T-S22", "dimension": "ai-core", "claim": "stc Bahrain reports deployed AI assurance for network performance, particularly fixed wireless access.", "url": "https://www.stc.com.bh/media-center/template?id=4346", "originalSourceUrl": "https://www.stc.com.bh/media-center/template?id=4346", "sourceTitle": "stc Bahrain wins awards for AI-powered CX assurance and convergent billing", "corroboratingSourceIds": [], "publisher": "stc Bahrain", "sourceType": "company_press_release", "publicationDate": "2025-10-26", "accessedDate": "2026-10-10", "locator": "AI and Machine Learning Customer Experience award paragraph", "status": "deployed", "level": 3, "eligible": true, "limitation": "Claim scope: stc Bahrain Company B.S.C. (closed), Bahrain operations. Company award announcement, not independent operational audit. Application: internal.", "access": "full_page | Review: full_page", "rationale": "Dated release describes deployed AI network assurance with specific mechanisms. Award itself is not verification.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "f8825c67f136ec7d998038383b0dc7a3f0ce934afd782dfe3e3dd09c0bd0ad0c", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-035", "companyId": "stc-bahrain", "sourceId": "T-S22", "dimension": "knowledge", "claim": "stc Bahrain assurance combines topology discovery, cross-domain fault correlation and predictive analytics.", "url": "https://www.stc.com.bh/media-center/template?id=4346", "originalSourceUrl": "https://www.stc.com.bh/media-center/template?id=4346", "sourceTitle": "stc Bahrain wins awards for AI-powered CX assurance and convergent billing", "corroboratingSourceIds": [], "publisher": "stc Bahrain", "sourceType": "company_press_release", "publicationDate": "2025-10-26", "accessedDate": "2026-10-10", "locator": "AI assurance mechanism paragraph", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: stc Bahrain Company B.S.C. (closed), Bahrain operations. Context retained to avoid stretching a technical use case across unrelated dimensions. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained.", "access": "full_page", "rationale": "Network observability does not establish organizational knowledge-management capability. Exclusion: No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained." ], "claimSha256": "4638bd449d15c4806cd52ecf80dae4b6e6ab0c0af03a3ae6f9a274dadcceff1a", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-036", "companyId": "stc-bahrain", "sourceId": "T-S23", "dimension": "ecosystem", "claim": "stc Bahrain and Huawei agreed joint development of AI-powered services and network operations.", "url": "https://www.stc.com.bh/media-center/template?id=4188", "originalSourceUrl": "https://www.stc.com.bh/media-center/template?id=4188", "sourceTitle": "stc Bahrain signs partnership agreement with Huawei", "corroboratingSourceIds": [], "publisher": "stc Bahrain", "sourceType": "company_press_release", "publicationDate": "2025-06-16", "accessedDate": "2026-10-10", "locator": "Main agreement and roadmap paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: stc Bahrain Company B.S.C. (closed), Bahrain operations. Agreement does not establish deployed joint capability. Application: internal-planned.", "access": "full_page | Review: full_page", "rationale": "Agreement explicitly includes future AI network optimization; retain announced status, not deployment.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "3017a68801f105cf116ee3267421050aebabafc745d931c97b2a843dad6a04f7", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-037", "companyId": "stc-bahrain", "sourceId": "T-S24", "dimension": "autonomy", "claim": "stc Bahrain markets agentic AI for customer business workflows on Bahrain-hosted infrastructure.", "url": "https://business.stc.com.bh/ai-solutions/stc-agentic-ai", "originalSourceUrl": "https://business.stc.com.bh/ai-solutions/stc-agentic-ai", "sourceTitle": "stc Agentic AI product page", "corroboratingSourceIds": [], "publisher": "stc Bahrain", "sourceType": "company_product_page", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Product introduction and Autonomous Intelligence", "status": "unclear", "level": null, "eligible": false, "limitation": "Claim scope: stc Bahrain Company B.S.C. (closed), Bahrain operations. Undated promotional page; neither adoption nor performance verified. Application: customer-product. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "indexed_excerpt", "rationale": "Customer product claims do not establish operator internal autonomy. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": null, "exclusionReasons": [ "Customer product alone does not establish internal operating practice.", "No qualifying AI-specific operating anchor retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "32a8b4ac8134eb1a24550862201bc603c1f114dace5940dcba85ebd45623afe0", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": 3, "workforce": null, "ecosystem": 1, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 4, "score": 15, "coverage": 2, "rank": 6, "sourceCount": 3, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 2, "excludedClaims": 2, "eligibleSourceUrls": 2, "undatedEligibleClaims": 0, "latestSourceDate": "2025-10-26", "lineage": [ { "dimension": "ai-core", "level": 3, "winningClaimIds": [ "TEL-034" ], "contribution": 11.1111 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "TEL-036" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.stc.com.bh/media-center/template?id=4346", "scoreWithoutSource": 3.7 }, { "url": "https://www.stc.com.bh/media-center/template?id=4188", "scoreWithoutSource": 11.11 } ], "scenarioResults": [ { "scenario": "baseline", "score": 14.81, "rank": 6 }, { "scenario": "implemented-only", "score": 11.11, "rank": 6 }, { "scenario": "detail-cap", "score": 11.11, "rank": 7 }, { "scenario": "ai-core-0.5", "score": 9.8, "rank": 8 }, { "scenario": "ai-core-2", "score": 23.33, "rank": 5 }, { "scenario": "workforce-0.5", "score": 15.69, "rank": 5 }, { "scenario": "workforce-2", "score": 13.33, "rank": 6 }, { "scenario": "ecosystem-0.5", "score": 13.73, "rank": 7 }, { "scenario": "ecosystem-2", "score": 16.67, "rank": 5 }, { "scenario": "interfaces-0.5", "score": 15.69, "rank": 6 }, { "scenario": "interfaces-2", "score": 13.33, "rank": 6 }, { "scenario": "lineage-0.5", "score": 15.69, "rank": 6 }, { "scenario": "lineage-2", "score": 13.33, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 15.69, "rank": 6 }, { "scenario": "experimentation-2", "score": 13.33, "rank": 7 }, { "scenario": "autonomy-0.5", "score": 15.69, "rank": 6 }, { "scenario": "autonomy-2", "score": 13.33, "rank": 7 }, { "scenario": "knowledge-0.5", "score": 15.69, "rank": 6 }, { "scenario": "knowledge-2", "score": 13.33, "rank": 7 }, { "scenario": "governance-0.5", "score": 15.69, "rank": 5 }, { "scenario": "governance-2", "score": 13.33, "rank": 6 } ], "scenarioRankRange": [ 5, 8 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "zain-group", "name": "Zain group", "country": "Kuwait", "sector": "Telecommunications", "scope": "Mobile Telecommunications Company K.S.C.P. (Zain), consolidated group", "scopeLimitations": "Group includes Kuwait and other markets; Kuwait-specific claims are explicitly bounded.", "cluster": "zain-family", "evidence": [ { "id": "TEL-022", "companyId": "zain-group", "sourceId": "T-S15", "dimension": "governance", "claim": "Zain established a Group Data Office owning data-management and privacy frameworks.", "url": "https://zain.com/AR2025/technology/", "originalSourceUrl": "https://zain.com/AR2025/technology/", "sourceTitle": "Zain Annual Report 2025: Technology", "corroboratingSourceIds": [], "publisher": "Zain", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Personal Data Privacy Policies and Framework", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Mobile Telecommunications Company K.S.C.P. (Zain), consolidated group. Entity-level appointments and policy adoption remain in progress; data privacy is narrower than comprehensive AI governance. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "access": "full_page | Review: full_page", "rationale": "Data Office/privacy section lacks explicit AI governance linkage. General data management cannot score AI-specific Governance. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass.", "proposedLevel": 3, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass." ], "claimSha256": "12e0bd2494c137f27a8fd480095dd30e914294da47c7b3ca634e0139621a0676", "review": { "decision": "exclude", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-023", "companyId": "zain-group", "sourceId": "T-S15", "dimension": "experimentation", "claim": "Zain describes a structured portfolio of AI trials across network operations, customer experience and energy.", "url": "https://zain.com/AR2025/technology/", "originalSourceUrl": "https://zain.com/AR2025/technology/", "sourceTitle": "Zain Annual Report 2025: Technology", "corroboratingSourceIds": [], "publisher": "Zain", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Advancing AI-Driven Network and Operations Intelligence", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Mobile Telecommunications Company K.S.C.P. (Zain), consolidated group. Trial portfolio is not proven enterprise-wide experimentation discipline. Application: internal.", "access": "full_page | Review: full_page", "rationale": "Structured AI trial portfolio explicit; evidence supports trials, not enterprise evaluation maturity.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "f08f565c11c5d55c92bc730a79ceff499632b6559dd6ced362c5895b49d591ce", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-024", "companyId": "zain-group", "sourceId": "T-S15", "dimension": "knowledge", "claim": "Zain Kuwait deployed FME Mate with Huawei to retrieve fault data and topologies and recommend corrections.", "url": "https://zain.com/AR2025/technology/", "originalSourceUrl": "https://zain.com/AR2025/technology/", "sourceTitle": "Zain Annual Report 2025: Technology", "corroboratingSourceIds": [], "publisher": "Zain", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Advancing AI-Driven Network and Operations Intelligence, Kuwait paragraph", "status": "deployed", "level": 1, "eligible": true, "limitation": "Claim scope: Zain Kuwait wireless-site engineering. Group report; Kuwait-specific deployment must not be projected to all subsidiaries. Application: internal.", "access": "full_page | Review: full_page", "rationale": "Same page calls Kuwait wireless fault use a trial before describing FME Mate as deployed. Preserve pilot/production ambiguity; conservative pilot code until clarified.", "proposedLevel": 3, "exclusionReasons": [], "claimSha256": "d19c0a1a18f817dd9aff8b591cb3a56db6fa76d41dcc7a30e40185f0ef16e5ce", "review": { "decision": "downgrade", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-025", "companyId": "zain-group", "sourceId": "T-S15", "dimension": "autonomy", "claim": "Zain reports introduction of closed-loop orchestration across several network domains and markets.", "url": "https://zain.com/AR2025/technology/", "originalSourceUrl": "https://zain.com/AR2025/technology/", "sourceTitle": "Zain Annual Report 2025: Technology", "corroboratingSourceIds": [], "publisher": "Zain", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Leadership in Autonomous Network Innovation", "status": "deployed", "level": 2, "eligible": true, "limitation": "Claim scope: Mobile Telecommunications Company K.S.C.P. (Zain), consolidated group. No precise decision authority, deployment denominator or market breakdown. Application: internal.", "access": "full_page | Review: full_page", "rationale": "Group network AI closed-loop operation explicit. Geography/coverage not fully specified.", "proposedLevel": 2, "exclusionReasons": [], "claimSha256": "e74e31af788695e7abe22d5d8d82f84c3365c292dc9b2ce329b6cd25a4ee02b9", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-026", "companyId": "zain-group", "sourceId": "T-S16", "dimension": "ai-core", "claim": "Zain's 4WARD strategy identifies AI across operations and services as a growth priority.", "url": "https://zain.com/AR2025/strategy/", "originalSourceUrl": "https://zain.com/AR2025/strategy/", "sourceTitle": "Zain Annual Report 2025: Strategy", "corroboratingSourceIds": [], "publisher": "Zain", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Force 2: Digital Zain", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Mobile Telecommunications Company K.S.C.P. (Zain), consolidated group. Useful strategic context only. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained.", "access": "full_page", "rationale": "Generic strategic AI priority is not a specific eligible operating practice. Exclusion: No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained." ], "claimSha256": "b7b0f3f0b8aa881e8ae2718c0443c470186a5511950f53d5604ea971f8ae03e5", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": null, "interfaces": null, "lineage": null, "experimentation": 1, "autonomy": 2, "knowledge": 1, "governance": null }, "points": 4, "score": 15, "coverage": 3, "rank": 6, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 3, "excludedClaims": 2, "eligibleSourceUrls": 1, "undatedEligibleClaims": 3, "latestSourceDate": null, "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": 1, "winningClaimIds": [ "TEL-023" ], "contribution": 3.7037 }, { "dimension": "autonomy", "level": 2, "winningClaimIds": [ "TEL-025" ], "contribution": 7.4074 }, { "dimension": "knowledge", "level": 1, "winningClaimIds": [ "TEL-024" ], "contribution": 3.7037 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://zain.com/AR2025/technology/", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 14.81, "rank": 6 }, { "scenario": "implemented-only", "score": 7.41, "rank": 7 }, { "scenario": "detail-cap", "score": 14.81, "rank": 6 }, { "scenario": "ai-core-0.5", "score": 15.69, "rank": 6 }, { "scenario": "ai-core-2", "score": 13.33, "rank": 7 }, { "scenario": "workforce-0.5", "score": 15.69, "rank": 5 }, { "scenario": "workforce-2", "score": 13.33, "rank": 6 }, { "scenario": "ecosystem-0.5", "score": 15.69, "rank": 6 }, { "scenario": "ecosystem-2", "score": 13.33, "rank": 7 }, { "scenario": "interfaces-0.5", "score": 15.69, "rank": 6 }, { "scenario": "interfaces-2", "score": 13.33, "rank": 6 }, { "scenario": "lineage-0.5", "score": 15.69, "rank": 6 }, { "scenario": "lineage-2", "score": 13.33, "rank": 6 }, { "scenario": "experimentation-0.5", "score": 13.73, "rank": 7 }, { "scenario": "experimentation-2", "score": 16.67, "rank": 6 }, { "scenario": "autonomy-0.5", "score": 11.76, "rank": 7 }, { "scenario": "autonomy-2", "score": 20.0, "rank": 5 }, { "scenario": "knowledge-0.5", "score": 13.73, "rank": 7 }, { "scenario": "knowledge-2", "score": 16.67, "rank": 5 }, { "scenario": "governance-0.5", "score": 15.69, "rank": 5 }, { "scenario": "governance-2", "score": 13.33, "rank": 6 } ], "scenarioRankRange": [ 5, 7 ], "canBecomeUnranked": false, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "du", "name": "du", "country": "UAE", "sector": "Telecommunications", "scope": "Emirates Integrated Telecommunications Company PJSC (du)", "scopeLimitations": "UAE operator and digital-service businesses; service offerings are not internal adoption.", "cluster": "du", "evidence": [ { "id": "TEL-013", "companyId": "du", "sourceId": "T-S08", "dimension": "workforce", "claim": "du commenced Microsoft 365 Copilot testing with a selected employee group.", "url": "https://www.du.ae/about/media-centre/newsdetail/du-launches-microsoft-365-copilot", "originalSourceUrl": "https://www.du.ae/about/media-centre/newsdetail/du-launches-microsoft-365-copilot", "sourceTitle": "du launches Microsoft 365 Copilot for enhanced workplace efficiency", "corroboratingSourceIds": [], "publisher": "du", "sourceType": "company_press_release", "publicationDate": "2025-03-13", "accessedDate": "2026-10-10", "locator": "Main release, first-phase paragraph", "status": "pilot", "level": 1, "eligible": true, "limitation": "Claim scope: Emirates Integrated Telecommunications Company PJSC (du). Headline says launch; body restricts evidence to selected-group testing. Application: internal.", "access": "full_page | Review: full_page", "rationale": "Full release specifies first-phase testing with selected employees. Retain pilot.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "8d63520391880b8f47942e8f63fc84b910b52ba8ce4f54468dc47260e76871b6", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-014", "companyId": "du", "sourceId": "T-S09", "dimension": "interfaces", "claim": "du and Microsoft describe planned call insights, post-call analytics and VoiceBot support for du contact centres.", "url": "https://www.du.ae/about/media-centre/newsdetail/du-announces-collaboration-with-microsoft", "originalSourceUrl": "https://www.du.ae/about/media-centre/newsdetail/du-announces-collaboration-with-microsoft", "sourceTitle": "du announces collaboration with Microsoft to transform AI call center operations", "corroboratingSourceIds": [], "publisher": "du", "sourceType": "company_press_release", "publicationDate": "2025-03-05", "accessedDate": "2026-10-10", "locator": "Project technologies and subscriber-copilot paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Emirates Integrated Telecommunications Company PJSC (du). Future-tense description; deployment completion and call-volume coverage not established. Application: internal-planned.", "access": "full_page", "rationale": "Specific internal customer-service redesign.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "5635b9b7e65a3207d5079ed345b1583cbb0ef3380916336fb0eec474ada3becc", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-015", "companyId": "du", "sourceId": "T-S09", "dimension": "ecosystem", "claim": "du announced a named Microsoft collaboration for its internal contact-centre AI transformation.", "url": "https://www.du.ae/about/media-centre/newsdetail/du-announces-collaboration-with-microsoft", "originalSourceUrl": "https://www.du.ae/about/media-centre/newsdetail/du-announces-collaboration-with-microsoft", "sourceTitle": "du announces collaboration with Microsoft to transform AI call center operations", "corroboratingSourceIds": [], "publisher": "du", "sourceType": "company_press_release", "publicationDate": "2025-03-05", "accessedDate": "2026-10-10", "locator": "Main release, collaboration paragraphs", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Emirates Integrated Telecommunications Company PJSC (du). No verified implementation milestone in announcement. Application: internal-planned.", "access": "full_page", "rationale": "Specified partner and internal workflow.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "478c2f2d1d00e36baac479c89dc060a52448bc0ade20d7b1ca15c09420c21c91", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-016", "companyId": "du", "sourceId": "T-S10", "dimension": "ai-core", "claim": "du announced AI Park planning and a National Hybrid AI offering for government and enterprise customers.", "url": "https://www.du.ae/about/media-centre/newsdetail/du-launches-ai-park-and-national-hybrid-ai-at-gitex-2025", "originalSourceUrl": "https://www.du.ae/about/media-centre/newsdetail/du-launches-ai-park-and-national-hybrid-ai-at-gitex-2025", "sourceTitle": "du launches AI Park and National Hybrid AI at GITEX 2025", "corroboratingSourceIds": [], "publisher": "du", "sourceType": "company_press_release", "publicationDate": "2025-10-13", "accessedDate": "2026-10-10", "locator": "AI Park and National Hybrid AI sections", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Emirates Integrated Telecommunications Company PJSC (du). Five-year phased park plan; distinguish proposed capacity from live capacity. Application: customer-product. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained.", "access": "full_page", "rationale": "External offering and infrastructure pipeline do not establish internal AI-native operations. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Customer product alone does not establish internal operating practice.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "0e1da39c899c9561337da4c579b17dbc215bc608584e2aa771ebff362833c060", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 3, "score": 11, "coverage": 3, "rank": 8, "sourceCount": 3, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 3, "excludedClaims": 1, "eligibleSourceUrls": 2, "undatedEligibleClaims": 0, "latestSourceDate": "2025-03-13", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": 1, "winningClaimIds": [ "TEL-013" ], "contribution": 3.7037 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "TEL-015" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": 1, "winningClaimIds": [ "TEL-014" ], "contribution": 3.7037 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.du.ae/about/media-centre/newsdetail/du-announces-collaboration-with-microsoft", "scoreWithoutSource": 3.7 }, { "url": "https://www.du.ae/about/media-centre/newsdetail/du-launches-microsoft-365-copilot", "scoreWithoutSource": 7.41 } ], "scenarioResults": [ { "scenario": "baseline", "score": 11.11, "rank": 8 }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 11.11, "rank": 7 }, { "scenario": "ai-core-0.5", "score": 11.76, "rank": 7 }, { "scenario": "ai-core-2", "score": 10.0, "rank": 8 }, { "scenario": "workforce-0.5", "score": 9.8, "rank": 8 }, { "scenario": "workforce-2", "score": 13.33, "rank": 6 }, { "scenario": "ecosystem-0.5", "score": 9.8, "rank": 8 }, { "scenario": "ecosystem-2", "score": 13.33, "rank": 7 }, { "scenario": "interfaces-0.5", "score": 9.8, "rank": 8 }, { "scenario": "interfaces-2", "score": 13.33, "rank": 6 }, { "scenario": "lineage-0.5", "score": 11.76, "rank": 8 }, { "scenario": "lineage-2", "score": 10.0, "rank": 8 }, { "scenario": "experimentation-0.5", "score": 11.76, "rank": 8 }, { "scenario": "experimentation-2", "score": 10.0, "rank": 8 }, { "scenario": "autonomy-0.5", "score": 11.76, "rank": 7 }, { "scenario": "autonomy-2", "score": 10.0, "rank": 8 }, { "scenario": "knowledge-0.5", "score": 11.76, "rank": 8 }, { "scenario": "knowledge-2", "score": 10.0, "rank": 8 }, { "scenario": "governance-0.5", "score": 11.76, "rank": 8 }, { "scenario": "governance-2", "score": 10.0, "rank": 8 } ], "scenarioRankRange": [ 6, 8 ], "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "mobily", "name": "Mobily", "country": "KSA", "sector": "Telecommunications", "scope": "Etihad Etisalat Company (Mobily), Saudi operating group", "scopeLimitations": "Primarily Saudi operations; e& investment relationship means not fully independent of e& group.", "cluster": "eand-family", "evidence": [ { "id": "TEL-005", "companyId": "mobily", "sourceId": "T-S04", "dimension": "autonomy", "claim": "Mobily reports SON deployment with outage detection, anomaly detection and automatic neighbouring-cell reconfiguration.", "url": "https://apisit.mobily.com.sa/wps/wcm/connect/e30b0c6d-81ce-485a-bdbb-891eaab24db8/Mobily%2BAR25%2B%2BEnglish.pdf?CACHEID=ROOTWORKSPACE-e30b0c6d-81ce-485a-bdbb-891eaab24db8-pSH9C9c&MOD=AJPERES", "originalSourceUrl": "https://apisit.mobily.com.sa/wps/wcm/connect/e30b0c6d-81ce-485a-bdbb-891eaab24db8/Mobily%2BAR25%2B%2BEnglish.pdf?CACHEID=ROOTWORKSPACE-e30b0c6d-81ce-485a-bdbb-891eaab24db8-pSH9C9c&MOD=AJPERES", "sourceTitle": "Mobily Annual Report 2025: Technology and Innovation", "corroboratingSourceIds": [], "publisher": "Mobily", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.104-105, AI, Automation and Intelligent Network Operations", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Etihad Etisalat Company (Mobily), Saudi operating group. Indexed report passage; PDF fetch failed; rollback authority and eligible-cell coverage not disclosed. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "access": "indexed_excerpt_full_pdf_fetch_failed | Review: fetch_failed", "rationale": "Direct PDF retrieval failed again. Indexed-only material cannot score under protocol. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 3, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "505c90c72f5e5233f44cc235d80ecf7f909268304c702e913525c8e85d42aaa8", "review": { "decision": "exclude", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-006", "companyId": "mobily", "sourceId": "T-S04", "dimension": "ai-core", "claim": "Mobily reports AI anomaly detection and visual inspection in maintenance operations.", "url": "https://apisit.mobily.com.sa/wps/wcm/connect/e30b0c6d-81ce-485a-bdbb-891eaab24db8/Mobily%2BAR25%2B%2BEnglish.pdf?CACHEID=ROOTWORKSPACE-e30b0c6d-81ce-485a-bdbb-891eaab24db8-pSH9C9c&MOD=AJPERES", "originalSourceUrl": "https://apisit.mobily.com.sa/wps/wcm/connect/e30b0c6d-81ce-485a-bdbb-891eaab24db8/Mobily%2BAR25%2B%2BEnglish.pdf?CACHEID=ROOTWORKSPACE-e30b0c6d-81ce-485a-bdbb-891eaab24db8-pSH9C9c&MOD=AJPERES", "sourceTitle": "Mobily Annual Report 2025: Technology and Innovation", "corroboratingSourceIds": [], "publisher": "Mobily", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.104-105, AI, Automation and Intelligent Network Operations", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Etihad Etisalat Company (Mobily), Saudi operating group. Indexed report passage; output verification and deployment coverage unreported. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "access": "indexed_excerpt_full_pdf_fetch_failed | Review: fetch_failed", "rationale": "Same failed PDF; remove points until eligible readable primary support exists. Exclusion: No qualifying AI-specific operating anchor retained. Excluded by independent source-review pass. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": 2, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Excluded by independent source-review pass.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "2747b496574dcc899a1a9ace540d6f04f7179d448cfd262e45cca1ddf235c258", "review": { "decision": "exclude", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-007", "companyId": "mobily", "sourceId": "T-S04", "dimension": "knowledge", "claim": "Mobily reports Moen providing self-service network insights and analytics.", "url": "https://apisit.mobily.com.sa/wps/wcm/connect/e30b0c6d-81ce-485a-bdbb-891eaab24db8/Mobily%2BAR25%2B%2BEnglish.pdf?CACHEID=ROOTWORKSPACE-e30b0c6d-81ce-485a-bdbb-891eaab24db8-pSH9C9c&MOD=AJPERES", "originalSourceUrl": "https://apisit.mobily.com.sa/wps/wcm/connect/e30b0c6d-81ce-485a-bdbb-891eaab24db8/Mobily%2BAR25%2B%2BEnglish.pdf?CACHEID=ROOTWORKSPACE-e30b0c6d-81ce-485a-bdbb-891eaab24db8-pSH9C9c&MOD=AJPERES", "sourceTitle": "Mobily Annual Report 2025: Technology and Innovation", "corroboratingSourceIds": [], "publisher": "Mobily", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Printed pp.104-105, AI, Automation and Intelligent Network Operations", "status": "deployed", "level": null, "eligible": false, "limitation": "Claim scope: Etihad Etisalat Company (Mobily), Saudi operating group. Retained context; not qualifying AI knowledge evidence. Application: internal. Exclusion: No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "indexed_excerpt_full_pdf_fetch_failed", "rationale": "Analytics access alone does not establish AI-mediated institutional knowledge. Exclusion: No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "proposedLevel": null, "exclusionReasons": [ "No qualifying AI-specific operating anchor retained.", "Complete readable source support not established; indexed-only evidence cannot score." ], "claimSha256": "e5b92d4f1110942d1d56c955c7c350cd5403beeef23ca8cc194b83f8064400b2", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-008", "companyId": "mobily", "sourceId": "T-S05", "dimension": "ecosystem", "claim": "Ericsson and Mobily signed an MoU to explore intent-driven networks using cognitive loops and orchestration.", "url": "https://www.ericsson.com/en/press-releases/5/2025/ericsson-and-mobily-sign-mou-at-leap-2025-to-explore-intent-driven-autonomous-networks", "originalSourceUrl": "https://www.ericsson.com/en/press-releases/5/2025/ericsson-and-mobily-sign-mou-at-leap-2025-to-explore-intent-driven-autonomous-networks", "sourceTitle": "Ericsson and Mobily sign MoU at LEAP 2025 to explore intent-driven autonomous networks", "corroboratingSourceIds": [], "publisher": "Ericsson", "sourceType": "partner_press_release", "publicationDate": "2025-02-10", "accessedDate": "2026-10-10", "locator": "Main announcement paragraphs 1-2", "status": "aspiration", "level": 1, "eligible": true, "limitation": "Claim scope: Etihad Etisalat Company (Mobily), Saudi operating group. MoU establishes intended collaboration, not implemented network autonomy. Application: internal-planned.", "access": "full_page | Review: full_page", "rationale": "Dated MoU names internal network development and AI mechanisms. Specific announced work, not implementation.", "proposedLevel": 1, "exclusionReasons": [], "claimSha256": "91fceeb072508c0d8f8f9f0c44f785c5dfa700334af11d7f57deaac380b6c8df", "review": { "decision": "retain", "record": "research/method-telecom-review.json", "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } } ], "profile": { "ai-core": null, "workforce": null, "ecosystem": 1, "interfaces": null, "lineage": null, "experimentation": null, "autonomy": null, "knowledge": null, "governance": null }, "points": 1, "score": 4, "coverage": 1, "rank": 9, "sourceCount": 2, "searchBoundary": { "method": "See the sector research log; completeness not established" }, "audit": { "eligibleClaims": 1, "excludedClaims": 3, "eligibleSourceUrls": 1, "undatedEligibleClaims": 0, "latestSourceDate": "2025-02-10", "lineage": [ { "dimension": "ai-core", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "workforce", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "ecosystem", "level": 1, "winningClaimIds": [ "TEL-008" ], "contribution": 3.7037 }, { "dimension": "interfaces", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "lineage", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "experimentation", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "autonomy", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "knowledge", "level": null, "winningClaimIds": [], "contribution": 0.0 }, { "dimension": "governance", "level": null, "winningClaimIds": [], "contribution": 0.0 } ], "singleSourceRemoval": [ { "url": "https://www.ericsson.com/en/press-releases/5/2025/ericsson-and-mobily-sign-mou-at-leap-2025-to-explore-intent-driven-autonomous-networks", "scoreWithoutSource": 0.0 } ], "scenarioResults": [ { "scenario": "baseline", "score": 3.7, "rank": 9 }, { "scenario": "implemented-only", "score": 0.0, "rank": null }, { "scenario": "detail-cap", "score": 3.7, "rank": 9 }, { "scenario": "ai-core-0.5", "score": 3.92, "rank": 9 }, { "scenario": "ai-core-2", "score": 3.33, "rank": 9 }, { "scenario": "workforce-0.5", "score": 3.92, "rank": 9 }, { "scenario": "workforce-2", "score": 3.33, "rank": 9 }, { "scenario": "ecosystem-0.5", "score": 1.96, "rank": 9 }, { "scenario": "ecosystem-2", "score": 6.67, "rank": 9 }, { "scenario": "interfaces-0.5", "score": 3.92, "rank": 9 }, { "scenario": "interfaces-2", "score": 3.33, "rank": 9 }, { "scenario": "lineage-0.5", "score": 3.92, "rank": 9 }, { "scenario": "lineage-2", "score": 3.33, "rank": 9 }, { "scenario": "experimentation-0.5", "score": 3.92, "rank": 9 }, { "scenario": "experimentation-2", "score": 3.33, "rank": 9 }, { "scenario": "autonomy-0.5", "score": 3.92, "rank": 9 }, { "scenario": "autonomy-2", "score": 3.33, "rank": 9 }, { "scenario": "knowledge-0.5", "score": 3.92, "rank": 9 }, { "scenario": "knowledge-2", "score": 3.33, "rank": 9 }, { "scenario": "governance-0.5", "score": 3.92, "rank": 9 }, { "scenario": "governance-2", "score": 3.33, "rank": 9 } ], "scenarioRankRange": [ 9, 9 ], "canBecomeUnranked": true, "independentAssurance": "Not established; source counts do not measure independence" } }, { "id": "vodafone-qatar", "name": "Vodafone Qatar", "country": "Qatar", "sector": "Telecommunications", "scope": "Vodafone Qatar P.Q.S.C.", "scopeLimitations": "Do not attribute Vodafone Group Plc worldwide practices to Vodafone Qatar.", "cluster": "vodafone-qatar", "evidence": [ { "id": "TEL-020", "companyId": "vodafone-qatar", "sourceId": "T-S13", "dimension": "ecosystem", "claim": "Vodafone Qatar reports expanded Microsoft collaboration for cloud, AI and digital services.", "url": "https://www.vodafone.qa/en/investor-relations/financials/Annual-Report-2025", "originalSourceUrl": "https://www.vodafone.qa/en/investor-relations/financials/Annual-Report-2025", "sourceTitle": "Vodafone Qatar Annual Report 2025", "corroboratingSourceIds": [], "publisher": "Vodafone Qatar", "sourceType": "company_annual_report", "publicationDate": null, "accessedDate": "2026-10-10", "locator": "Telco to TechCo section", "status": "unclear", "level": null, "eligible": false, "limitation": "Claim scope: Vodafone Qatar P.Q.S.C.. No attributable internal deployment coverage or mechanism. Application: customer-product. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained.", "access": "full_page", "rationale": "AI service partnership description does not establish internal operating practice. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained.", "proposedLevel": null, "exclusionReasons": [ "Customer product alone does not establish internal operating practice.", "No qualifying AI-specific operating anchor retained." ], "claimSha256": "729789eb8bf3c0df98f638d1cadbeec227824ebd65cc68abc46852ac4eb4ff79", "review": { "decision": "no separate claim challenge recorded", "record": null, "exactClaimBinding": false, "kind": "AI-assisted source challenge; not human validation" } }, { "id": "TEL-021", "companyId": "vodafone-qatar", "sourceId": "T-S14", "dimension": "interfaces", "claim": "Vodafone Qatar and KAIC intend to co-develop FlowTeam based on TENN.ai for client needs.", "url": "https://www.vodafone.qa/en/about-us/vodafone-qatar-and-kaic-sign-stategic-mou-to-advance-ai", "originalSourceUrl": "https://www.vodafone.qa/en/about-us/vodafone-qatar-and-kaic-sign-stategic-mou-to-advance-ai", "sourceTitle": "Vodafone Qatar and KAIC sign strategic MoU to advance AI innovation", "corroboratingSourceIds": [], "publisher": "Vodafone Qatar", "sourceType": "company_press_release", "publicationDate": "2025-09-13", "accessedDate": "2026-10-10", "locator": "Main release paragraphs 1-3", "status": "aspiration", "level": null, "eligible": false, "limitation": "Claim scope: Vodafone Qatar P.Q.S.C.. Indexed excerpt; full-page fetch failed. MoU is not deployed system. Application: customer-product. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained. Complete readable source support not established; indexed-only evidence cannot score.", "access": "indexed_excerpt_full_page_fetch_failed", "rationale": "Customer offering, not qualifying internal transformation. Exclusion: Customer product alone does not establish internal operating practice. No qualifying AI-specific operating anchor retained. 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source counts do not measure independence" } } ], "studies": [ { "id": "L01", "title": "Generative AI at Work", "url": "https://doi.org/10.1093/qje/qjae044", "publisher": "Quarterly Journal of Economics", "publicationDate": "2025-02-04", "design": "Staggered workplace rollout; observational causal design", "population": "Customer-support agents", "sampleSize": "5,172 agents", "setting": "One business-software firm; mainly Philippines", "keyFinding": "Issues resolved per hour rose 15% on average; gains varied by experience.", "limitations": "One firm and tool; not randomized; not enterprise ROI.", "transferabilityToGCC": "Test language, work mix and experience locally.", "measurementImplication": "Measure accepted output, quality and worker heterogeneity.", "sourceLocator": "Abstract; IV.B Table I; V", "evidenceType": "peer-reviewed empirical", "accessedDate": "2026-10-10" }, { "id": "L02", "title": "Experimental evidence on the productivity effects of generative artificial intelligence", "url": "https://doi.org/10.1126/science.adh2586", "publisher": "Science", "publicationDate": "2023-07-13", "design": "Preregistered randomized online experiment", "population": "College-educated professionals", "sampleSize": "453 participants", "setting": "Incentivized professional writing tasks", "keyFinding": "Time fell 40%; assessed quality rose 18%.", "limitations": "Short tasks; no whole-firm cost or sustained-use result.", "transferabilityToGCC": "Test equivalent Arabic/English tasks; do not transfer effect size.", "measurementImplication": "Separate task speed, quality and total workflow outcomes.", "sourceLocator": "Abstract", "evidenceType": "peer-reviewed experiment", "accessedDate": "2026-10-10" }, { "id": "L03", "title": "Navigating the Jagged Technological Frontier", "url": "https://www.hbs.edu/ris/download.aspx?name=24-013.pdf", "publisher": "Harvard Business School working paper 24-013", "publicationDate": "2023-09", "design": "Randomized field experiment", "population": "BCG consultants", "sampleSize": "758 participants", "setting": "Consulting tasks with GPT-4", "keyFinding": "Within-frontier tasks improved speed and assessed performance; effects depend on task suitability.", "limitations": "Selected tasks and one employer; working-paper versions change.", "transferabilityToGCC": "Local role/task evaluations needed.", "measurementImplication": "Evaluate task boundaries and human review rather than access alone.", "sourceLocator": "Abstract; study design; HBS institute summary 2023-09-21", "evidenceType": "working-paper experiment", "accessedDate": "2026-10-10" }, { "id": "L04", "title": "The Cybernetic Teammate: A Field Experiment on Generative AI Reshaping Teamwork and Expertise", "url": "https://www.nber.org/papers/w33641", "publisher": "NBER working paper 33641; published Organization Science June 2026", "publicationDate": "2025-04", "design": "Preregistered randomized field experiment", "population": "Procter & Gamble professionals", "sampleSize": "776 participants", "setting": "Product innovation; individuals and teams, with/without AI", "keyFinding": "Individuals with AI matched teams without AI on evaluated performance.", "limitations": "One company and bounded innovation exercise; not proof teams should disappear.", "transferabilityToGCC": "Test expertise-sharing locally.", "measurementImplication": "Assess role boundaries and coordination, not only individual use.", "sourceLocator": "NBER abstract and published-version record; DOI 10.1287/orsc.2025.20702", "evidenceType": "original field experiment", "accessedDate": "2026-10-10" }, { "id": "L05", "title": "Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity", "url": "https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study-paper.pdf", "publisher": "METR", "publicationDate": "2025-07-10", "design": "Task-randomized controlled trial", "population": "Experienced open-source developers", "sampleSize": "16 developers; 246 tasks", "setting": "Mature repositories; February–June 2025 tools", "keyFinding": "AI-allowed tasks took 19% longer despite perceived speedup.", "limitations": "Small selected sample; period-specific tools and mature projects.", "transferabilityToGCC": "Do not generalize to GCC developers or current tools.", "measurementImplication": "Compare measured performance with participant estimates.", "sourceLocator": "Abstract; pair with L06 update", "evidenceType": "original experiment", "accessedDate": "2026-10-10" }, { "id": "L06", "title": "We are Changing our Developer Productivity Experiment Design", "url": "https://metr.org/blog/2026-02-24-uplift-update/", "publisher": "METR", "publicationDate": "2026-02-24", "design": "Follow-up randomized study; methodological disclosure", "population": "Experienced open-source developers", "sampleSize": "57 developers; 800+ tasks; 143 repositories", "setting": "Follow-up begun August 2025", "keyFinding": "Authors judge newer productivity estimates unreliable because of selection and time-measurement problems.", "limitations": "Nonparticipation, selective tasks, changed pay and concurrent agents.", "transferabilityToGCC": "Monitor participation and task-selection changes in repeated panels.", "measurementImplication": "Record nonresponse, mode and task-mix changes; avoid timeless technology claims.", "sourceLocator": "Opening findings; Wider adoption; Details of the productivity study", "evidenceType": "original follow-up methods report", "accessedDate": "2026-10-10" }, { "id": "L07", "title": "Shifting Work Patterns with Generative AI", "url": "https://www.nber.org/system/files/working_papers/w33795/revisions/w33795.rev1.pdf", "publisher": "NBER working paper 33795, revision 1", "publicationDate": "2025-05-06", "design": "Six-month randomized field experiment", "population": "Knowledge workers", "sampleSize": "7,137 workers", "setting": "Cross-industry; integrated email, document and meeting tools", "keyFinding": "Email and document behaviors changed; meeting time did not significantly change.", "limitations": "Specific revision; user-subgroup and intention-to-treat estimates differ; vendor researchers.", "transferabilityToGCC": "Coordination mechanisms need local testing.", "measurementImplication": "Measure shared workflows and adoption denominators, not only individual time saved.", "sourceLocator": "Abstract, pinned May 6 2025 revision", "evidenceType": "working-paper experiment", "accessedDate": "2026-10-10" }, { "id": "L08", "title": "Firm Data on AI", "url": "https://wfhresearch.com/wp-content/uploads/2026/03/Firm-Data-on-AI-16-February-2026.pdf", "publisher": "NBER working paper 34836; author-hosted February version", "publicationDate": "2026-02", "design": "Stratified firm surveys", "population": "Senior executives", "sampleSize": "Almost 6,000 respondents", "setting": "US, UK, Germany, Australia", "keyFinding": "Widespread reported use coexisted with limited reported employment/productivity effects; expectations were stronger.", "limitations": "Self-reported impacts, four non-GCC economies; later revision differs.", "transferabilityToGCC": "Context, not a GCC prevalence estimate.", "measurementImplication": "Keep adoption, realized effects and expectations separate.", "sourceLocator": "Abstract, February 2026 version; later NBER revision noted", "evidenceType": "original survey", "accessedDate": "2026-10-10" }, { "id": "L09", "title": "The Productivity J-Curve: How Intangibles Complement General Purpose Technologies", "url": "https://www.nber.org/papers/w25148", "publisher": "NBER; American Economic Journal: Macroeconomics 13(1), 2021", "publicationDate": "2021-01", "design": "Economic model and historical empirical analysis", "population": "Historical technology/intangible investment series", "sampleSize": "Not a company AI trial", "setting": "R&D, software and hardware investment", "keyFinding": "Complementary intangible investment can delay measured productivity gains.", "limitations": "Model/historical mechanism; not proof any AI investment pays back.", "transferabilityToGCC": "Treat reorganization as a hypothesis with explicit costs.", "measurementImplication": "Track complementary investment, timing and delayed outcomes.", "sourceLocator": "Abstract; published-version record", "evidenceType": "peer-reviewed theory and empirical analysis", "accessedDate": "2026-10-10" }, { "id": "L10", "title": "Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance", "url": "https://doi.org/10.1016/j.im.2021.103434", "publisher": "Information & Management 58(3)", "publicationDate": "2021-04-01", "design": "Instrument development and cross-sectional survey", "population": "Senior technology managers recruited through professional network", "sampleSize": "143 responses", "setting": "Multiple industries", "keyFinding": "AI capability combines technical, human and organizational resources associated with creativity/performance.", "limitations": "Self-selection and same-source reporting; association is not causal; pre-GenAI.", "transferabilityToGCC": "Revalidate content and measurement in each target language/context.", "measurementImplication": "Treat capability as multidimensional; do not equate software ownership with capability.", "sourceLocator": "Empirical-study sample; instrument Appendix A", "evidenceType": "peer-reviewed survey and measurement", "accessedDate": "2026-10-10" }, { "id": "L11", "title": "Measuring and Explaining Management Practices Across Firms and Countries", "url": "https://www.nber.org/papers/w12216", "publisher": "NBER; Quarterly Journal of Economics 122(4), 2007", "publicationDate": "2007-11", "design": "Structured management-practice interviews and linked performance analysis", "population": "Medium-sized manufacturers", "sampleSize": "732 firms", "setting": "US, France, Germany, UK", "keyFinding": "Practice scores were associated with several firm-performance measures.", "limitations": "Observational, historical manufacturing sample; not an AI index.", "transferabilityToGCC": "Adapt behavior-anchored interviewing; test transfer.", "measurementImplication": "Use trained interviewers and operational examples rather than executive self-labels.", "sourceLocator": "Abstract; published-version record; original interview method", "evidenceType": "peer-reviewed organizational measurement", "accessedDate": "2026-10-10" }, { "id": "L12", "title": "Handbook on Constructing Composite Indicators: Methodology and User Guide", "url": "https://doi.org/10.1787/9789264043466-en", "publisher": "OECD / European Union / EC-JRC", "publicationDate": "2008-08-22", "design": "Methodological handbook", "population": "Composite-indicator builders", "sampleSize": "Not applicable", "setting": "Country indices; methodological principles transferable", "keyFinding": "Normalization, weighting, aggregation and sensitivity choices affect interpretation and rankings.", "limitations": "Does not validate Mayden dimensions, cutoffs or weights.", "transferabilityToGCC": "Adapt principles; disclose cohort and missingness.", "measurementImplication": "Predeclare construct, anchors, weighting and uncertainty/sensitivity.", "sourceLocator": "Sections 1.5–1.8; toolbox", "evidenceType": "measurement guidance", "accessedDate": "2026-10-10" }, { "id": "L13", "title": "Best Practices for Developing and Validating Scales for Health, Social, and Behavioral Research: A Primer", "url": "https://doi.org/10.3389/fpubh.2018.00149", "publisher": "Frontiers in Public Health", "publicationDate": "2018-06-11", "design": "Methodological primer", "population": "Scale developers", "sampleSize": "Not applicable", "setting": "Health, social and behavioral research", "keyFinding": "Separates item development, scale development and evaluation.", "limitations": "General guidance; reflective-scale procedures may not fit a formative index.", "transferabilityToGCC": "Cognitive interviews and invariance testing needed.", "measurementImplication": "Test comprehension, content validity, reliability and criterion validity.", "sourceLocator": "Three phases/nine steps; validity discussion", "evidenceType": "peer-reviewed measurement guidance", "accessedDate": "2026-10-10" }, { "id": "L14", "title": "Artificial Intelligence Risk Management Framework (AI RMF 1.0)", "url": "https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf", "publisher": "NIST", "publicationDate": "2023-01", "design": "Voluntary risk-management framework", "population": "Organizations developing/deploying/using AI", "sampleSize": "Not applicable", "setting": "Cross-sector governance", "keyFinding": "Organizes risk management around Govern, Map, Measure and Manage.", "limitations": "Normative guidance, not evidence that compliance causes productivity; version-specific.", "transferabilityToGCC": "Contextualize controls; no automatic local compliance claim.", "measurementImplication": "Assess ownership, measurement, incidents and lifecycle controls.", "sourceLocator": "Part 2, AI RMF Core", "evidenceType": "official framework", "accessedDate": "2026-10-10" }, { "id": "L15", "title": "When combinations of humans and AI are useful: A systematic review and meta-analysis", "url": "https://doi.org/10.1038/s41562-024-02024-1", "publisher": "Nature Human Behaviour", "publicationDate": "2024-10-28", "design": "Preregistered systematic review/meta-analysis", "population": "Human–AI task experiments", "sampleSize": "106 experiments; 370 effect sizes", "setting": "Studies published January 2020–June 2023", "keyFinding": "Average combinations underperformed the better standalone agent; task type moderated results.", "limitations": "Historical systems; heterogeneous tasks; does not measure whole enterprises.", "transferabilityToGCC": "Local baselines essential; no assumed human–AI synergy.", "measurementImplication": "Compare feasible human-only, AI-only and combined workflows.", "sourceLocator": "Abstract; eligibility; Discussion", "evidenceType": "peer-reviewed evidence synthesis", "accessedDate": "2026-10-10" }, { "id": "L16", "title": "Conventional Wisdom on Measurement: A Structural Equation Perspective", "url": "https://doi.org/10.1037/0033-2909.110.2.305", "publisher": "Psychological Bulletin", "publicationDate": "1991-09", "design": "Conceptual measurement-model analysis", "population": null, "sampleSize": null, "setting": "Structural equation measurement", "keyFinding": "Consequences of indicator selection and consistency depend on whether indicators cause or reflect a construct.", "limitations": "Conceptual argument, not validation of an AI index.", "transferabilityToGCC": "Applicable design distinction; requires local construct work.", "measurementImplication": "Treat the public composite as a designed disclosure index; do not use internal consistency alone to validate it.", "sourceLocator": "Author-uploaded original paper; abstract and opening measurement-model discussion: https://www.researchgate.net/profile/Kenneth-Bollen-2/publication/232516427_Conventional_Wisdom_on_Measurement_A_Structural_Equation_Perspective/links/02e7e52818cc621782000000/Conventional-Wisdom-on-Measurement-A-Structural-Equation-Perspective.pdf", "evidenceType": "peer-reviewed-methodological", "accessedDate": "2026-10-10" }, { "id": "L17", "title": "Weights and importance in composite indicators: Closing the gap", "url": "https://doi.org/10.1016/j.ecolind.2017.03.056", "publisher": "Ecological Indicators / European Commission JRC", "publicationDate": "2017-09", "design": "Methodological analysis with three case studies", "population": "Resource Governance, Good Country and Water Retention indices", "sampleSize": null, "setting": "Composite indicators", "keyFinding": "Nominal weights can differ from statistical importance because indicators vary and correlate.", "limitations": "Case-study methods do not establish optimal weights for Mayden; small selected cohorts limit statistical estimation.", "transferabilityToGCC": "Relevant to any proposed regional composite.", "measurementImplication": "Publish nominal weights and deterministic sensitivity; avoid claiming equal weights mean equal effective importance.", "sourceLocator": "JRC author repository abstract: https://publications.jrc.ec.europa.eu/repository/handle/JRC105460 ; archived original abstract and introduction: https://pmc.ncbi.nlm.nih.gov/articles/PMC5473177/", "evidenceType": "peer-reviewed-methodological", "accessedDate": "2026-10-10" }, { "id": "L18", "title": "COSMIN methodology for assessing the content validity of PROMs: User manual", "url": "https://www.cosmin.nl/wp-content/uploads/COSMIN-methodology-for-content-validity-user-manual-v1.pdf", "publisher": "COSMIN", "publicationDate": "2018-02", "design": "Methodological manual", "population": "Patient-reported outcome measurement instruments", "sampleSize": null, "setting": "Health measurement", "keyFinding": "Content validity concerns relevance, comprehensiveness and comprehensibility, including intended respondents' understanding.", "limitations": "PROM guidance; clinical thresholds and grading do not automatically transfer to organisations.", "transferabilityToGCC": "Adapt interview principles with Arabic/English executives and operational users.", "measurementImplication": "Test interpretation, omitted content and response processes before statistical scoring.", "sourceLocator": "February 2018 preface; PDF pages 13, 29 and content-validity review sections.", "evidenceType": "methodological-guideline", "accessedDate": "2026-10-10" }, { "id": "L19", "title": "PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Reviews", "url": "https://doi.org/10.1186/s13643-020-01542-z", "publisher": "Systematic Reviews", "publicationDate": "2021-01-26", "design": "Reporting-guideline development", "population": "Systematic-review searches", "sampleSize": null, "setting": "Research synthesis", "keyFinding": "Sixteen reporting items improve transparency of searches; the checklist is for reporting, not a complete conduct standard.", "limitations": "Adapting a search log does not make a corporate review systematic or exhaustive.", "transferabilityToGCC": "Useful audit discipline for multilingual source discovery.", "measurementImplication": "Log platform, exact query, date, limits, supplementary routes, deduplication and retrieved counts.", "sourceLocator": "Full original article: https://pmc.ncbi.nlm.nih.gov/articles/PMC7839230/ ; introduction and checklist.", "evidenceType": "reporting-guideline", "accessedDate": "2026-10-10" }, { "id": "L20", "title": "The PRISMA 2020 statement: an updated guideline for reporting systematic reviews", "url": "https://doi.org/10.1136/bmj.n71", "publisher": "The BMJ", "publicationDate": "2021-03-29", "design": "Reporting-guideline development", "population": "Systematic reviews", "sampleSize": null, "setting": "Research synthesis reporting", "keyFinding": "Provides a 27-item checklist and flow diagrams for transparent systematic-review reporting.", "limitations": "Checklist adoption alone is not proof of complete searching or low bias.", "transferabilityToGCC": "Applicable reporting distinctions across research settings.", "measurementImplication": "Call the current synthesis a scoped rapid evidence review; reserve systematic-review claims for a documented eligible design.", "sourceLocator": "Publisher indexed original statement; official checklist overview: https://www.prisma-statement.org/prisma-2020-statement . Direct BMJ page retrieval returned 403 on review date.", "evidenceType": "reporting-guideline", "accessedDate": "2026-10-10" }, { "id": "L21", "title": "PROV-DM: The PROV Data Model", "url": "https://www.w3.org/TR/prov-dm/", "publisher": "W3C", "publicationDate": "2013-04-30", "design": "Normative data-model specification", "population": null, "sampleSize": null, "setting": "Domain-independent provenance", "keyFinding": "Represents entities, activities, agents and derivations so information can be traced across transformations.", "limitations": "A provenance graph records derivation; it does not establish factual truth.", "transferabilityToGCC": "Useful architecture independent of geography.", "measurementImplication": "Link fetched artifacts, extracted spans, claims, reviews and releases with stable IDs and hashes.", "sourceLocator": "Full W3C Recommendation; abstract, introduction and core structures.", "evidenceType": "technical-standard", "accessedDate": "2026-10-10" }, { "id": "L22", "title": "Large Firms With at Least 20 Employees Biggest AI Users", "url": "https://www.census.gov/library/stories/2026/05/ai-use-businesses.html", "publisher": "U.S. Census Bureau", "publicationDate": "2026-05-26", "design": "Original statistical-agency survey analysis", "population": "U.S. businesses covered by Business Trends and Outlook Survey", "sampleSize": null, "setting": "U.S. business AI use", "keyFinding": "The article describes a November 2025 question change from production-related AI to any business function and measures use over a recent two-week period.", "limitations": "Exact analytic sample not reported in inspected article; wording change limits historical comparisons.", "transferabilityToGCC": "Question design is transferable; U.S. prevalence is not a GCC estimate.", "measurementImplication": "Keep a stable reference period and bridge instrument revisions before publishing adoption trends.", "sourceLocator": "Full agency article; question-change paragraph and discussion of the AI supplement.", "evidenceType": "original-survey-analysis", "accessedDate": "2026-10-10" }, { "id": "L23", "title": "How Organizations Use AI: Evidence from ChatGPT", "url": "https://arxiv.org/abs/2608.12236v1", "publisher": "arXiv author preprint", "publicationDate": "2026-08-12", "design": "Observational linked enterprise-account and usage analysis", "population": "ChatGPT Enterprise organisations and workers", "sampleSize": "More than 1,500 organisations and 17 million messages at the six-month adoption horizon, as described in the abstract", "setting": "Account records through March 2026", "keyFinding": "Adoption differs in speed, breadth and purpose; account use is linked to roles and firm characteristics.", "limitations": "Preprint; inspected abstract only. Selected customers of one product, observational data and incomplete enterprise-tool coverage.", "transferabilityToGCC": "Suggests useful behavioural dimensions, not transferable adoption norms.", "measurementImplication": "Separate available seats, active use, task purpose and role coverage; triangulate surveys with permitted telemetry.", "sourceLocator": "Version 1 author abstract; no claim to have reviewed appendices or causal identification.", "evidenceType": "empirical-preprint-observational", "accessedDate": "2026-10-10" }, { "id": "L24", "title": "Generative AI and Sales Productivity: Field Experiments in Online Retail", "url": "https://arxiv.org/abs/2510.12049v6", "publisher": "arXiv author preprint", "publicationDate": "2026-06-29", "design": "Series of randomized field experiments", "population": "Users and products of one cross-border retail platform", "sampleSize": "Millions of users and products; exact pooled total not stated in inspected abstract", "setting": "Seven workflows, experiments in 2023–2024", "keyFinding": "Sales effects differ across workflow applications, with some showing no effect.", "limitations": "Preprint version 6; inspected abstract only. One platform and bounded sales outcomes do not establish enterprise profitability.", "transferabilityToGCC": "Workflow-specific evaluation principle transfers; effect sizes cannot be assigned to GCC sectors.", "measurementImplication": "Record baseline, task, outcome, comparison and period; keep sales effects separate from capability scoring.", "sourceLocator": "Version 6 author abstract; original submission 14 October 2025, revised title used here.", "evidenceType": "empirical-preprint-experimental", "accessedDate": "2026-10-10" }, { "id": "L25", "title": "Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI", "url": "https://www.nber.org/system/files/working_papers/w33777/w33777.pdf", "publisher": "NBER working paper 33777", "publicationDate": "2026-03", "design": "Linked adoption surveys and administrative records; observational difference-in-differences", "population": "Workers in 11 chatbot-exposed Danish occupations", "sampleSize": "Approximately 25,000 complete responses per survey round; latest round spans about 7,000 workplaces", "setting": "November–December 2023 and 2024 surveys; labour records through December 2024", "keyFinding": "Task reorganisation coexists with estimates near zero for earnings and recorded hours.", "limitations": "Working paper; nonrandom adoption, identification assumptions, short horizon; no post-adoption firm financial analysis.", "transferabilityToGCC": "Danish labour institutions and occupational selection prevent direct GCC effect transfer.", "measurementImplication": "Separate task change, capacity, pay, employment and profit outcomes.", "sourceLocator": "Downloaded March 2026 revision; title page, introduction and sections 1–2 inspected.", "evidenceType": "empirical-working-paper-observational", "accessedDate": "2026-10-10" }, { "id": "L26", "title": "Measuring AI agent autonomy in practice", "url": "https://www.anthropic.com/research/measuring-agent-autonomy", "publisher": "Anthropic original research", "publicationDate": "2026-02-18", "design": "Provider telemetry with model-assisted classification", "population": "Public API tool calls and interactive Claude Code activity", "sampleSize": "998,481 sampled API tool calls; separate 500,000-session analysis; other denominators differ by exhibit", "setting": "Late 2025–early 2026", "keyFinding": "Autonomy and oversight vary with product context and user experience.", "limitations": "Single provider; model classifications; API requests cannot be joined into complete sessions; evaluations may resemble production.", "transferabilityToGCC": "Measurement concepts transfer; traffic shares are not regional enterprise prevalence.", "measurementImplication": "Track action authority, interventions, reversibility and session boundaries separately.", "sourceLocator": "Full original article: methods, figures 1–6 and limitations; appendix not independently audited.", "evidenceType": "provider-original-observational", "accessedDate": "2026-10-10" }, { "id": "L27", "title": "An Evaluation of DWP’s Microsoft 365 Copilot Trial", "url": "https://www.gov.uk/government/publications/an-evaluation-of-dwps-microsoft-copilot-365-trial/an-evaluation-of-dwps-microsoft-365-copilot-trial", "publisher": "UK Department for Work and Pensions", "publicationDate": "2026-01-29", "design": "Nonrandom post-implementation surveys, adjusted regressions and interviews", "population": "DWP employees with and without licences", "sampleSize": "1,716 licensed respondents of 3,549 invited; 2,535 comparison respondents of 9,300 invited; 19 interviews", "setting": "Survey fieldwork February–March 2025", "keyFinding": "Respondents described task benefits and verification overhead.", "limitations": "No pre-trial baseline or objective outcome metrics; self-selection and nonresponse; regression does not remove all bias.", "transferabilityToGCC": "Office workflow questions transfer; reported benefits do not estimate GCC effects.", "measurementImplication": "Collect baselines and observed task metrics before rollout.", "sourceLocator": "Full HTML; chapters 1, 3, 4 and limitations in chapter 5.", "evidenceType": "official-original-evaluation", "accessedDate": "2026-10-10" }, { "id": "L28", "title": "Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial", "url": "https://doi.org/10.1001/jamanetworkopen.2024.40969", "publisher": "JAMA Network Open", "publicationDate": "2024-10-28", "design": "Stratified randomised, blinded-rating trial", "population": "US-licensed generalist physicians recruited through three academic institutions", "sampleSize": "50 physicians, 25 per arm; 244 completed case responses across six vignettes", "setting": "November–December 2023; one-hour sessions", "keyFinding": "Access to a chatbot did not significantly improve the primary diagnostic-reasoning score.", "limitations": "Small vignette study, one model, limited prompting instruction; not patient outcomes.", "transferabilityToGCC": "Interaction-design lesson is relevant; clinical effects require local evaluation.", "measurementImplication": "Test the combined human-and-AI workflow against usual practice.", "sourceLocator": "Full original XML via Europe PMC: https://www.ebi.ac.uk/europepmc/webservices/rest/PMC11519755/fullTextXML ; Methods, Results, Discussion and Limitations.", "evidenceType": "peer-reviewed-randomized-trial", "accessedDate": "2026-10-10" }, { "id": "L29", "title": "The impact of AI on the workplace: Main findings from the OECD AI surveys of employers and workers", "url": "https://doi.org/10.1787/ea0a0fe1-en", "publisher": "OECD Social, Employment and Migration Working Papers", "publicationDate": "2023-03-22", "design": "Separate employer telephone and worker online-panel surveys", "population": "Manufacturing and finance in seven countries; employer firms with at least 20 staff", "sampleSize": "5,334 workers; 2,053 firms", "setting": "Main fieldwork mid-January to mid-February 2022", "keyFinding": "Sector-specific surveys capture implementation and workers' experiences.", "limitations": "Perceptions and associations; online-panel coverage; predates mass generative-AI adoption; matched worker-employer pairs are not established in the inspected method.", "transferabilityToGCC": "Sector wording is useful; no GCC country was sampled.", "measurementImplication": "Ask operators as well as leadership; define the technology and reference period.", "sourceLocator": "Full OECD PDF; executive summary and chapter 2, physical pages 13 and 22–24: https://www.oecd.org/content/dam/oecd/en/publications/reports/2023/03/the-impact-of-ai-on-the-workplace-main-findings-from-the-oecd-ai-surveys-of-employers-and-workers_ad686e91/ea0a0fe1-en.pdf", "evidenceType": "official-original-survey", "accessedDate": "2026-10-10" }, { "id": "L30", "title": "RICS artificial intelligence in construction report 2025", "url": "https://www.rics.org/news-insights/artificial-intelligence-in-construction-report", "publisher": "Royal Institution of Chartered Surveyors", "publicationDate": "2025", "design": "Professional-body online sentiment surveys", "population": "Construction and surveying professionals globally", "sampleSize": "Over 2,200 Q1 respondents; separate skills survey over 2,000", "setting": "Six AI questions in Q1 2025 Global Construction Monitor; separate Q2 skills module", "keyFinding": "Respondents distinguish pilots, regular process use and organisation-wide adoption.", "limitations": "Sentiment sample; UK accounts for 48% of Q1 responses; MEA is not a GCC estimate; two survey bases must not be pooled.", "transferabilityToGCC": "Project and process distinctions transfer; regional estimates do not.", "measurementImplication": "Keep project, asset, organisation and respondent scope explicit.", "sourceLocator": "Full HTML sections 2, 3.1 and 3.7; publication year confirmed, exact day not taken from report.", "evidenceType": "professional-body-original-survey", "accessedDate": "2026-10-10" }, { "id": "L31", "title": "tau-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains", "url": "https://arxiv.org/html/2406.12045v1", "publisher": "Author preprint / Sierra research", "publicationDate": "2024-06-17", "design": "Synthetic interactive agent benchmark with repeated trials", "population": "Simulated retail and airline service tasks", "sampleSize": "115 retail tasks and 50 airline tasks", "setting": "Version 1; synthetic users, policies and databases", "keyFinding": "Repeated success differs from success in a single run; final-state tests can miss policy violations.", "limitations": "Simplified environments, simulated users and annotation assumptions; not production business outcomes.", "transferabilityToGCC": "Local policies, language and permissions require separate tests.", "measurementImplication": "Measure consistency and policy compliance in addition to task completion.", "sourceLocator": "Full HTML sections 3–5, table 1 and reward limitations.", "evidenceType": "original-benchmark-preprint", "accessedDate": "2026-10-10" }, { "id": "L32", "title": "GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks", "url": "https://arxiv.org/html/2510.04374v1", "publisher": "OpenAI author preprint", "publicationDate": "2025-10-05", "design": "Expert-authored task benchmark with pairwise deliverable evaluation", "population": "Digital knowledge work selected from nine US economic sectors", "sampleSize": "1,320 tasks across 44 occupations; public gold subset 220 tasks", "setting": "Version 1; professional deliverables", "keyFinding": "Capabilities can be assessed through work products and expert comparisons.", "limitations": "Precisely specified, mainly one-shot tasks; limited tacit context and interaction; capability and modelled savings do not establish realised enterprise ROI.", "transferabilityToGCC": "Task-design method transfers; US occupational selection is not a GCC workforce sample.", "measurementImplication": "Separate model evaluation from deployment and financial evidence.", "sourceLocator": "Full HTML sections 2–5 and automated-grader limitations A.6.3.", "evidenceType": "provider-original-benchmark-preprint", "accessedDate": "2026-10-10" }, { "id": "L33", "title": "SABER: Small Actions, Big Errors — Safeguarding Mutating Steps in LLM Agents", "url": "https://arxiv.org/html/2512.07850v1", "publisher": "Amazon AGI Foundations author preprint", "publicationDate": "2025-11-26", "design": "Agent benchmark re-audit and safeguard experiments", "population": "tau-bench airline/retail variants and selected SWE-bench evaluation", "sampleSize": "50 airline and 115 retail tasks; three runs for tau-bench experiments", "setting": "Version 1; three model families; 30-turn cap", "keyFinding": "Authors identify annotation problems and test safeguards around state-changing actions.", "limitations": "Simulated confirmation; preprint; gains are not universal in table 2 and narrative overstates consistency.", "transferabilityToGCC": "Audit technique transfers; benchmark effects are not GCC operating effects.", "measurementImplication": "Review test labels, action consequences and regressions before deployment.", "sourceLocator": "Full HTML sections 5–8, tables 2–3; table/narrative discrepancy recorded.", "evidenceType": "original-benchmark-methods-preprint", "accessedDate": "2026-10-10" }, { "id": "L34", "title": "Population-scale cross-sectional observational study for AI-powered TB screening on one million CXRs", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC12241593/", "doi": "10.1038/s41746-025-01832-7", "publisher": "npj Digital Medicine; full text in PMC", "publicationDate": "2025-07-09", "design": "Retrospective observational evaluation; authors also describe a quasi-prospective approach.", "population": "Adult TB screening at Capital Health Screening Centre, Abu Dhabi", "sampleSize": "1.04 million test chest X-ray scans", "setting": "January 2022–January 2024; low-prevalence screening", "keyFinding": "Reported AUROC 98.51%; overall false-negative rate 1.57% at the optimal threshold.", "limitations": "Potential 80% reporting reduction is calculated from test classifications, not demonstrated prospective savings. Zero TB false negatives follows annotation review/correction. Lower external-dataset AUROC; sputum confirmation restricted to radiologist-flagged cases.", "transferabilityToGCC": "Local screening evidence; not enterprise capability or general clinical benefit.", "measurementImplication": "Separate diagnostic evaluation, projected workload and observed operating change.", "sourceLocator": "Main text: Results, Safety in Automation, Clinical Impact, Discussion, Methods, Competing interests", "evidenceType": "original-observational-model-evaluation", "accessedDate": "2026-10-10", "sourceAccess": "Relevant main text inspected through PMC; Nature direct redirect failed; supplements not independently audited.", "interests": "Authors declare no competing interests; M42/CHSC affiliations and model-developer involvement are disclosed.", "linkedClaimIds": [ "HLT-013" ], "scoreEffect": "None; adds scientific context only." } ], "reports": [ { "slug": "ai-native-enterprise", "title": "What makes an AI-native enterprise?", "dek": "A research synthesis on work, organizational capability and the evidence needed to measure them.", "kind": "Research synthesis", "sector": null, "arSummary": "تركيب بحثي يدرس كيف يغيّر الذكاء الاصطناعي العمل والقدرات المؤسسية، وما يلزم لقياس ذلك دون تعميم غير مبرر.", "body": "# What makes an AI-native enterprise?\r\n## The operating model matters more than the announcement\r\n\r\nMayden AI Native Index · Research foundation · 10 October 2026\r\n\r\n**An AI-native enterprise repeatedly converts AI capability into useful, accountable work across a defined organizational scope.** This is Mayden's proposed working definition, to be tested through field research. It asks what changes in decisions, workflows, responsibilities and learning when AI becomes part of normal operations.\r\n\r\nA company can have thousands of AI licences and little operating change. Another can use a small number of models inside essential workflows, with clear ownership, sound evidence and effective fallback arrangements. A benchmark that rewards purchasing activity will miss this difference. A benchmark that rewards autonomy without considering consequence may reward poor judgment.\r\n\r\nThe research offers a strong foundation for measuring these distinctions. It does not yet provide a universally validated definition of an AI-native enterprise. Mayden's opportunity is to build that measurement carefully, beginning with Saudi Arabia, the UAE, Qatar, Kuwait and Bahrain.\r\n\r\n### Productivity gains are real, conditional and uneven\r\n\r\nThe published customer-support study by Brynjolfsson, Li and Raymond follows 5,172 agents. AI assistance increased resolved issues per hour by 15% on average, with larger gains among less experienced workers. This is evidence about a particular deployed workflow; it is not a forecast for every company's profit margin. The published figures differ from earlier working-paper versions, a reminder that a research database needs version control. [Published study](https://doi.org/10.1093/qje/qjae044).\r\n\r\nIn a randomized experiment with 453 professionals, Noy and Zhang found faster completion and higher assessed quality on writing tasks. That demonstrates potential at the task level. It does not incorporate every approval, integration, legal review or downstream correction in an enterprise process. The Index should therefore ask whether faster drafting improved the accepted final output and the total process. [Writing experiment](https://doi.org/10.1126/science.adh2586).\r\n\r\nThe consulting experiment involving 758 BCG professionals reinforces the need to evaluate task suitability. Its findings describe a jagged boundary: apparently similar tasks can sit on different sides of what the tool handles reliably. A serious company must know where its deployed systems succeed and where users need another approach. Counting deployments cannot reveal that knowledge. [HBS working paper](https://www.hbs.edu/ris/download.aspx?name=24-013.pdf).\r\n\r\nThe executive question is precise: **Which material workflow performs better, against which baseline, after counting the work needed to make its outputs usable?**\r\n\r\n### AI can change expertise and collaboration\r\n\r\nIn the Procter & Gamble experiment, 776 professionals worked on product innovation challenges. Individuals equipped with AI matched the assessed performance of teams without AI. The study opens an important question about how expertise can travel across functional boundaries. It does not establish that companies should remove teams or that temporary experimental performance will persist in ordinary operations. [Cybernetic Teammate study](https://www.nber.org/papers/w33641).\r\n\r\nA separate six-month experiment with 7,137 knowledge workers found changes in email and document work without a significant change in meeting time in its May 2025 revision. The contrast points toward a useful hypothesis: individual changes can be easier than coordinated organizational changes. Giving people tools does not itself settle decision rights, redesign handoffs or change shared routines. [Pinned working-paper revision](https://www.nber.org/system/files/working_papers/w33795/revisions/w33795.rev1.pdf).\r\n\r\nOur next company study will test that hypothesis through actual operating examples. Who now prepares a recommendation? Who verifies it? Who can act on it? Which handoff has disappeared, and which new review step has appeared? A before-and-after workflow can reveal more than a general claim that employees are empowered.\r\n\r\n### Perceived improvement is not measured improvement\r\n\r\nMETR's early-2025 experiment found that 16 experienced open-source developers took longer on AI-allowed tasks, despite believing that the tools accelerated them. The result is bounded by its selected developers, mature repositories and tools of that period. It is useful evidence against treating users' perceived time savings as an objective productivity measure. [Original experiment](https://metr.org/Early_2025_AI_Experienced_OS_Devs_Study-paper.pdf).\r\n\r\nThe same research organization later explained why its follow-up estimates were unreliable. Developers' willingness to participate, their task selection and time accounting changed. Its February 2026 report therefore cannot support a clean claim that the original effect has reversed by a precise amount. This is a lesson for any longitudinal index: the people and work entering the measurement may change alongside the technology. [Follow-up methods report](https://metr.org/blog/2026-02-24-uplift-update/).\r\n\r\nA 2026 survey of almost 6,000 executives across four non-GCC economies also separates widespread reported AI use from limited reported realized effects. Executive expectations were more optimistic than descriptions of past results. These are survey reports, not audited financial effects. For Mayden, the implication is to record use, measured outcomes and future expectations in separate fields. [February 2026 author version](https://wfhresearch.com/wp-content/uploads/2026/03/Firm-Data-on-AI-16-February-2026.pdf).\r\n\r\n### Better combinations must be demonstrated\r\n\r\nAn existing meta-analysis of 106 experiments found that human–AI combinations, on average in that corpus, underperformed the better of the human or AI working alone. Results differed by task type. This is not the claim that AI assistance never helps people; the comparison is with the better standalone performer. Its study window ended in June 2023, so it cannot establish the performance of today's systems. [Published meta-analysis](https://doi.org/10.1038/s41562-024-02024-1).\r\n\r\nThe practical lesson is to test the combination. Human review needs enough information, time and authority to improve the result. A nominal approval click should not be treated as effective oversight. Equally, the correct design may deliberately retain human judgment even where automation is faster, because accountability and consequence matter.\r\n\r\nMayden's score should reward an appropriate operating design. It should not award automatic bonus points for replacing people, maximizing autonomy or adopting a particular model.\r\n\r\n### Capability includes organizational resources\r\n\r\nMikalef and Gupta developed a multidimensional AI capability instrument covering technical, human and organizational resources. Their survey provides useful construct-development precedent, but its associations with performance are not causal proof and its sample predates the current generative-AI environment. This work informs our question design; local validation remains necessary. [AI capability study](https://doi.org/10.1016/j.im.2021.103434).\r\n\r\nThe productivity J-curve literature explains why complementary investments in processes and skills can complicate the timing of measured gains. It provides a plausible mechanism, not an excuse for an unproductive project to continue indefinitely. Assessment should record implementation costs, expected timing, milestones and explicit stopping criteria. [J-curve research](https://www.nber.org/papers/w25148).\r\n\r\nOrganizational measurement also has an established precedent in the management-practices research of Bloom and Van Reenen. Structured evidence about how firms operate can be related to performance. That supports asking for operational examples and training assessors. It does not make a newly authored AI score valid by association. [Management measurement study](https://www.nber.org/papers/w12216).\r\n\r\n### Nine questions that executives should be able to answer\r\n\r\nMayden's existing nine themes can form a coherent research agenda. They remain candidate dimensions, with overlap and weighting to be tested.\r\n\r\n| Theme | The executive question |\r\n|---|---|\r\n| AI Core | Where does AI support material recurring work, using reliable data and a workable fallback? |\r\n| Workforce | Can people use, challenge and improve AI in the roles they actually perform? |\r\n| Ecosystem | Can we evaluate, maintain and, when necessary, change critical AI dependencies? |\r\n| Interfaces | Is AI part of the workflow, and what share of eligible work actually uses it? |\r\n| Decision lineage | Can we reconstruct an important output, its sources and the actions that followed? |\r\n| Experimentation | What evidence causes us to scale, revise or stop a deployment? |\r\n| Operating model | Who owns the outcome and can authorize, halt or reverse consequential actions? |\r\n| Knowledge | Who keeps the organization's usable knowledge current, accessible and trustworthy? |\r\n| Governance | Can we discover uses, evaluate risks and respond to incidents across the lifecycle? |\r\n\r\nThese questions are deliberately about behavior. A policy can contribute evidence, but it does not prove implementation. A training course can contribute evidence, but it does not prove competence. A partnership can provide resources, but it does not prove a deployed operating practice.\r\n\r\nNIST's risk-management framework offers useful structure for lifecycle controls and responsibilities. It is guidance, not a certificate of compliance or a validated productivity score. [AI RMF 1.0](https://nvlpubs.nist.gov/nistpubs/ai/NIST.AI.100-1.pdf).\r\n\r\n### A credible benchmark separates three records\r\n\r\nFirst is **operating capability**: what practices exist, over what scope and with what continuity. Second is **outcome evidence**: changes in quality, speed, cost, revenue, risk and work experience. Third is **evidence strength**: the basis for believing each statement.\r\n\r\nKeeping these records separate prevents a circular argument in which high-performing companies receive high capability scores because their outcomes were included in the score, and the same outcomes are then used to claim the score predicts performance.\r\n\r\nIt also prevents public visibility from masquerading as capability. A company with a detailed annual report may be easier to assess than a private firm. Lack of published evidence is an information gap. It is not proof that a practice is absent. The initial public-document product must therefore identify itself as a **Public Evidence Index**, with explicit limits on what its ranking means.\r\n\r\nNormalization cannot repair a weak construct. The OECD/JRC handbook makes weighting, aggregation and sensitivity part of the design problem. A fixed scale is useful for repeat measurement, but the anchors still need justification. [Composite-indicator handbook](https://doi.org/10.1787/9789264043466-en).\r\n\r\n### Build a regional benchmark that can travel\r\n\r\nThe five-market starting scope is a deliberate launch cohort, not a claim to cover the entire GCC. Country, sector, company size, ownership and operating scope should be visible. A regional subsidiary must not inherit a global parent's capabilities without evidence that those practices apply locally.\r\n\r\nArabic and English instruments require more than literal translation. Participants should explain what they understood each question to mean; researchers should investigate differences by language and interview mode. Voice answers need confirmation before being transformed into scores. Scale-development guidance supports an explicit progression from item design to testing and validation. [Measurement primer](https://doi.org/10.3389/fpubh.2018.00149).\r\n\r\nThe valuable longitudinal question is not simply who ranks first this year. It is **which operating changes predict sustained improvements, under which conditions, and which apparently promising practices fail to deliver**. Answering it requires repeat participants, preserved instrument versions, comparable outcomes and honest treatment of firms that stop responding.\r\n\r\nA useful confidential report can help sustain that participation. It should show an organization's supported strengths, unresolved questions and a few concrete priorities. The public research can then explain patterns without exposing entrusted company evidence.\r\n\r\n### Research note\r\n\r\nThis report is a scoped rapid evidence review and Mayden design synthesis. It is not a systematic review or a new statistical meta-analysis. Fifteen research and methodological records were selected through targeted primary-source searches on 10 October 2026. Selection prioritized identifiable original studies, peer-reviewed measurement work and authoritative methodological guidance; it was not an exhaustive database search.\r\n\r\nExperiments, observational studies, executive surveys, an existing meta-analysis and frameworks are kept distinct. No pooled productivity percentage is calculated. Source access ranged from publisher/author abstracts and indexed passages to relevant report sections; this is not a claim that every full paper and appendix was independently audited. Publication versions are pinned where findings changed. None of these sources establishes GCC company rankings, validates Mayden's nine dimensions or replaces proprietary fieldwork.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "banking-public-evidence", "title": "AI in Gulf banking: inspect the operating evidence", "dek": "A purposeful review of ten banking entities across five headquarters markets.", "kind": "Industry evidence review", "sector": "Banking", "arSummary": "مراجعة استكشافية لأدلة عشرة بنوك في خمس أسواق. تصف النتائج الأدلة المنشورة، ولا تثبت ترتيب النضج المؤسسي أو أداء الدول.", "body": "# The AI-native bank: follow the decisions, not the announcements\r\n## Mayden AI Native Index — banking evidence briefing\r\nResearch cut-off: 10 October 2026. Public-evidence research edition, not a verified maturity assessment.\r\n\r\n### A bank becomes more AI-native when its operating system changes\r\n\r\nThe most useful question for a bank's board is no longer whether the institution uses artificial intelligence. It is whether AI is changing how the bank gathers knowledge, reaches decisions, delivers work and corrects mistakes. A collection of disconnected assistants can coexist with unchanged approval chains, inaccessible knowledge and weak measurement. Conversely, a tightly governed application in a critical workflow may reveal more organisational change than a large collection of announced experiments.\r\n\r\nThis briefing examines a deliberately selected cohort of ten banks: Saudi National Bank and Al Rajhi Bank in Saudi Arabia; Emirates NBD and First Abu Dhabi Bank in the UAE; QNB and Commercial Bank in Qatar; National Bank of Kuwait and Kuwait Finance House in Kuwait; and National Bank of Bahrain and BBK in Bahrain. Two organisations per market provide a starting comparison, not a representative sample. Oman is outside this edition. Group-level disclosures describe international operations as well as home markets and cannot be interpreted as country-level adoption rates.\r\n\r\nThe research separates reported implementation, future plans and unknowns. The associated register contains sources and bounded claims against nine Mayden themes. It measures what public evidence supports. It does not establish which bank is objectively the most AI-native.\r\n\r\n### The clearest operating signals come from specific workflows\r\n\r\nCommercial Bank of Qatar's 2025 report names deployed document recognition in onboarding, trade finance and lending, together with a retrieval-supported email assistant. It also describes its own AI computing environment. These disclosures connect technical capability to work rather than simply announcing a partner. The same report presents agentic capabilities as a future direction; those must remain separate from live tools. [Commercial Bank Annual Report 2025, p.37](https://www.cbq.qa/-/media/project/cbq/cbqwebsite/documents/corporate-affairs/annual-reports-en/cbq-annual-report-2025--en.pdf).\r\n\r\nSaudi National Bank describes AI-assisted segmentation and retail cross-selling. Its subsidiary SNB Capital also reports AI deployment, but that evidence belongs to the subsidiary's asset-management functions and must not silently become a bank-wide claim. [SNB Annual Report 2025, printed pp.36–39; company-authored report on Argaam mirror](https://argaamplus.s3.amazonaws.com/07ce1455-a38a-41b1-ad0e-78b4c0e01090.pdf).\r\n\r\nAl Rajhi's report describes predictive models inside credit, fraud and customer-lifecycle decisions. It also explains the assembly of consented information from banking and external channels. The methodological distinction matters: the relevant signal is the named decision mechanism, not the accompanying digital-banking awards. [Al Rajhi 2025 innovation review](https://www.alrajhibank.com.sa/ir25/customer_engagement_and_responsible_banking/innovation_and_product_development.html).\r\n\r\nFor an executive, the follow-up is straightforward: select one material workflow and identify precisely which decisions AI supports, what evidence it uses, who can override it, and how success is measured.\r\n\r\n### Reusable infrastructure may be the organisational turning point\r\n\r\nEmirates NBD describes Leap as a shared agent engineering framework supplying controls such as authentication, audit records and evaluation. This is a potentially important organisational pattern: common infrastructure can reduce repeated work and make rules portable across teams. The disclosure is an architectural claim by the bank, not an independent control audit. [Emirates NBD's Leap account](https://www.emiratesnbd.com/en/innovation/insights/why-banks-should-build-their-own-agentic-ai-framework).\r\n\r\nFAB describes an AI Innovation Hub, a common data platform and a group responsible-AI framework. Its February 2026 announcement distinguishes agents already embedded in operations from a larger portfolio of cases still progressing. That difference must survive any chart, headline or ranking. [FAB enterprise AI announcement](https://www.bankfab.com/en-ae/about-fab/group/in-the-media/fab-accelerates-enterprise-ai-adoption).\r\n\r\nThese cases suggest a proposition for field research: reusable organisational capabilities may explain sustainable deployment better than the number of pilots. This proposition remains a question for field research. Relevant evidence would include time to release a second use case, reuse of controls, duplicated engineering effort, incident patterns and the cost of changing an underlying model.\r\n\r\n### Knowledge must become usable without losing its authority\r\n\r\nKuwait Finance House describes employee assistants including ShariaBot, which refers to specific fatwas issued by its supervisory board, and ComplianceBot. This provides a concrete example of institutional knowledge entering daily work. It raises a deeper assessment question: can the institution demonstrate that an answer uses the current authorised source and that later changes propagate correctly? A source reference is useful, but it does not establish a complete audit trail. [KFH's internal AI initiatives](https://www.kfh.com/en/home/Personal/news/2025/KFH-Reinforces-Banking-Leadership-with-Forward-Looking-AI-Initiatives.html).\r\n\r\nCustomer conversations are another knowledge source. Emirates NBD's PCA account describes turning calls into structured signals for service improvement. Its disclosed language boundary is especially useful for regional research: English is supported, while Arabic is described as a future extension. The Index should retain language coverage and avoid translating an English deployment into an implied bilingual capability. [Emirates NBD PCA case](https://www.emiratesnbd.com/en/innovation/insights/turning-millions-of-customer-conversations-into-better-banking-experiences-with-ai).\r\n\r\nKFH separately describes automated call analysis and quality assessment. Mayden's diagnostic should ask how such evaluation affects employees, how incorrect assessments are challenged, and how quality scores are validated across language and customer groups. [KFH contact-centre case](https://www.kfh.com/en/home/Personal/news/2025/KFH-Adopts-Advanced-AI-Technologies-to-Enhance-Customer-Experience.html).\r\n\r\n### Workforce access is an input; changed work is the outcome to investigate\r\n\r\nNBK's sustainability report makes a useful distinction between tool availability and actual use: about one-third of staff were consistent Copilot Web users in 2025. The bank also discloses restrictions on confidential information and disables document transfer. Its broader AI risk and accountability framework remains a roadmap item. These specifics are more informative than an access announcement, while still leaving productivity and behavioural change untested. The report's default scope is Kuwait operations. [NBK Sustainability Report 2025, printed p.192; company-authored report on Argaam mirror](https://argaamplus.s3.amazonaws.com/4b4e6fe7-09dd-4311-be33-c97735b392e7.pdf).\r\n\r\nIndexed passages from NBB's annual report and results release point to completed academy cohorts and a service chatbot. Full direct retrieval could not be completed under this edition's verification protocol, so these remain unresolved research leads and are excluded from release scoring. They should be revisited before drawing conclusions about NBB's operating practices. [NBB Annual Report 2025, pp.68–69](https://nbbonline.com/wp-content/uploads/2026/03/Annual-Financial-and-Sustainability-Report-2025-3.pdf), [NBB 2025 results](https://nbbonline.com/news/27971/).\r\n\r\nBBK offers a different combination: an innovation centre with a stated engineering team and annual-report disclosures about AI in board documentation and management oversight. A narrow board tool may be operationally meaningful even when enterprise coverage is unknown. [BBK innovation centre](https://www.bbkonline.com/bbk-partners-with-array-to-launch-innovation-center-hiring-bahraini-software-engineers-to-accelerate-digital-transformation/), [BBK Annual Report 2025](https://www.bbkonline.com/wp-content/uploads/2026/04/BBK-2025-Annual-Report-English-Spread.pdf).\r\n\r\n### Governance should be visible in decisions\r\n\r\nQNB's published AI and Data Governance Statement names an executive AI committee and a cross-functional review forum for material use cases. This gives researchers a concrete place to examine responsibility. The next evidential step is to inspect what those bodies approve, reject, monitor and stop. Their existence should not be equated with their effectiveness. [QNB governance statement, printed p.3](https://www.qnb.com/sites/qnb/qnbqatar/document/en/DataAIGovernanceStatement-).\r\n\r\nAl Rajhi explicitly says that its formal AI governance framework is still being developed. That candour should remain visible alongside evidence of deployed predictive models. QNB, meanwhile, reports beginning to use Copilot within internal audit and including AI ethics in mandatory risk training. A useful profile preserves these different stages rather than forcing every institution into a single transformation story. [Al Rajhi data privacy review](https://www.alrajhibank.com.sa/ir25/corporate_governance/data_privacy_and_information_management.html), [QNB Annual Report 2025, PDF pp.83 and120](https://vpr.hkma.gov.hk/statics/assets/doc/100336/ar_25/ar_25_eng.pdf).\r\n\r\nAcross the cohort, the useful diagnostic is a decision reconstruction. Choose a consequential AI-supported decision and trace its information, model, approval, challenge and correction. The exercise can reveal whether governance is part of operations or remains an overlay. Greater autonomy should earn credit only where delegated authority, limits, escalation and recovery are demonstrable. More autonomy is not automatically better banking.\r\n\r\n### The evidence edition must be honest about what its ranking means\r\n\r\nA normalized public-evidence score can help readers compare documented practices against the same rubric. It cannot remove disclosure bias. A bank that publishes a detailed technology account may receive more evidence credit than an equally capable bank that publishes little. Missing material should be labelled unknown, not operational failure. Multiple announcements about the same programme should not accumulate extra points.\r\n\r\nThis review is predominantly English, uses company-authored disclosures and was conducted within a bounded search. FAB's annual PDF could not be retrieved; NBB's indexed-only annual and results leads are excluded from scoring. SNB and NBK sustainability text required company-authored reports on third-party mirrors; those copies were not hash-matched to issuer files. These unequal access conditions limit comparison. The database records those limits. No respondent interviews, system tests or independent operating audits were performed. Reported business improvements also need their own ledger: baseline, scope, period, denominator and attribution. They should remain outside capability scoring so that Mayden can later investigate the relationship between capability and results.\r\n\r\n### A benchmark worth returning to\r\n\r\nBank AI benchmarking already has established competitors. Emirates NBD's June 2026 announcement links to the Evident Middle East and Africa bank index. Regional bank AI measurement already exists; this edition neither claims priority nor imports another provider's rank into its own score. [Emirates NBD announcement](https://www.emiratesnbd.com/en/media-center/emirates-nbd-ranked-1-in-inaugural-evident-ai-index-for-banks), [Evident regional index](https://evidentinsights.com/banking-ai-index-mea).\r\n\r\nMayden's opportunity is to connect public evidence to a repeatable company diagnostic and longitudinal research. Executives should leave with a sharper picture of where their operating model works, where responsibility is unclear, and which intervention to test next. The benchmark becomes durable when the same organisations return, their evidence improves, and the study can distinguish enduring organisational change from an attractive announcement.\r\n\r\n**Executive reflection:** Could your bank reconstruct one important AI-supported decision, show who remains accountable, and demonstrate an improvement after the cost of checking and correcting the output?\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "telecom-public-evidence", "title": "AI in Gulf telecoms: the next test is how the business operates", "dek": "Separate internal AI practice, customer offerings and future infrastructure across ten telecom entities.", "kind": "Industry evidence review", "sector": "Telecommunications", "arSummary": "مراجعة لأدلة عشر جهات اتصالات، تفصل بين الاستخدام الداخلي ومنتجات العملاء والخطط المستقبلية، مع إظهار اختلاف نطاقات المجموعات والشركات التابعة.", "body": "# AI in Gulf telecoms: the next test is how the business operates\r\n\r\nMayden AI Native Index | Telecom research brief | 10 October 2026\r\n\r\n**Exploratory public-evidence edition.** This brief examines public disclosures from ten deliberately selected telecom entities headquartered or operating in Saudi Arabia, the UAE, Qatar, Kuwait and Bahrain. It is a structured evidence review, not a representative survey, audited maturity assessment or claim that one national market leads another. The accompanying evidence database preserves claim-level citations, deployment stages and source limitations. Company statements remain company statements.\r\n\r\n## The executive question\r\n\r\nTelecom operators can sell AI infrastructure, apply AI to network engineering and redesign their own enterprise at the same time. These are three different achievements. An operator may host sophisticated customer workloads while its finance, people and service processes remain largely conventional. Another may quietly improve internal work without publishing detailed case studies. A useful AI-native benchmark must distinguish those possibilities.\r\n\r\nThe public evidence already offers more substance than a collection of ambitious announcements. There are named employee platforms, network-assurance mechanisms, customer assistants and governance structures. The unresolved question is their penetration: which share of material work has changed, what decisions the systems can make, who owns failures and whether benefits survive the full cost of running them?\r\n\r\nThis is the opportunity for Mayden. Public research can identify the operating questions worth asking. Proprietary questionnaires and corroborating evidence can establish answers that public communications rarely supply.\r\n\r\n## What the disclosures reveal\r\n\r\n**Governance is becoming an operating system for AI.** stc reports a Responsible AI framework, reusable-model certification and a model registry. Its sustainability report also describes employee training and an internal platform soft-launched in 2023. The time distinction matters: a historical launch is evidence of activity, not proof of today's employee adoption. [stc governance report](https://www.stc.com/content/dam/groupsites/stc-sustainability-report-2025/corporate-governance/)\r\n\r\ne& describes a central governance platform, use-case review, named steering authority, risk processes and mandatory training. Its own forward agenda still includes full platform rollout across business units. A careful assessment therefore credits the disclosed mechanism while preserving incomplete coverage. Governance documents should lead to requests for actual approval, rejection, monitoring and incident records. [e& 2025 report, printed pp.128-129](https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf)\r\n\r\n**Network intelligence offers concrete workflows to inspect.** An indexed passage from Mobily's report identifies potential leads on network automation. Direct PDF access failed in this review. These leads are excluded from scoring and are not treated as verified factual conclusions here. The next source-recovery step should verify deployment scope, override arrangements and the denominator behind any claimed savings. [Mobily 2025 report, printed pp.104-105](https://apisit.mobily.com.sa/wps/wcm/connect/e30b0c6d-81ce-485a-bdbb-891eaab24db8/Mobily%2BAR25%2B%2BEnglish.pdf?CACHEID=ROOTWORKSPACE-e30b0c6d-81ce-485a-bdbb-891eaab24db8-pSH9C9c&MOD=AJPERES)\r\n\r\nThe boundary between deployed practice and future design is equally visible. Ericsson and Mobily's intent-driven-network announcement is an MoU to explore specified capabilities. e& UAE's June 2026 autonomous-network blueprint describes a roadmap towards higher autonomy. Neither announcement alone demonstrates completed transformation. [Ericsson announcement](https://www.ericsson.com/en/press-releases/5/2025/ericsson-and-mobily-sign-mou-at-leap-2025-to-explore-intent-driven-autonomous-networks), [e& roadmap](https://www.eand.com/en/news/22-06-26-eand-tm-strategic-blueprint.html)\r\n\r\n**The most revealing word in a press release may be “pilot.”** du announced an internal Copilot launch, but the body specifies testing with selected employees. Its Microsoft contact-centre announcement sets out proposed call insights, analytics and a VoiceBot. These are useful, bounded transformation signals. They do not establish sustained deployment at scale. The assessment follows the implementation described in the body, rather than the ambition of the headline. [du employee pilot](https://www.du.ae/about/media-centre/newsdetail/du-launches-microsoft-365-copilot), [du contact-centre programme](https://www.du.ae/about/media-centre/newsdetail/du-announces-collaboration-with-microsoft)\r\n\r\n**AI delivery to customers needs a separate lens.** Ooredoo reports deploying AI-enabled care tools across markets and a live sovereign AI cloud launch through Syntys. The first concerns how the operator works; the second concerns what it sells. du's AI Park announcement combines a phased infrastructure plan with a hybrid AI offering. Both may be commercially significant, but infrastructure capacity is not an organizational capability score. [Ooredoo annual report](https://www.ooredoo.com/wp-content/uploads/2026/03/2025-Ooredoo-Annual-Report_English_FINAL.pdf), [Ooredoo results](https://www.ooredoo.com/en/media/news_view/ooredoo-group-fy-2025/), [du AI Park](https://www.du.ae/about/media-centre/newsdetail/du-launches-ai-park-and-national-hybrid-ai-at-gitex-2025)\r\n\r\nVodafone Qatar's reviewed materials emphasize Microsoft collaboration and a proposed FlowTeam solution with KAIC for client needs. They support a customer-service innovation narrative. They do not, in this bounded source set, support scoring internal AI-native work practices. “Unknown from these sources” is the appropriate result. It would be misleading to translate limited disclosure into weak capability. [Vodafone Qatar report](https://www.vodafone.qa/en/investor-relations/financials/Annual-Report-2025), [KAIC announcement](https://www.vodafone.qa/en/about-us/vodafone-qatar-and-kaic-sign-stategic-mou-to-advance-ai)\r\n\r\n**Practical adoption often begins with a specific job.** Zain describes a Kuwait FME Mate field-engineering initiative for access to fault data, topology and recommended corrections. The report mixes trial and deployment language, supporting a limited documented implementation rather than an established scaled rollout. It also describes trial portfolios and broader closed-loop ambitions. Its reported efficiency improvement belongs to the specified engineering use case; expected avoided downtime is a forecast. These distinctions prevent promising engineering evidence becoming a claim about every employee or subsidiary. [Zain technology report](https://zain.com/AR2025/technology/)\r\n\r\nstc Kuwait reports an AI Unit, AI Office and employee AI Week. Those disclosures identify accountable structures and capability-building activity. They leave the assessment to determine whether ownership changes investment decisions, whether labs become maintained production services and whether employees continue using the tools after training. Its detailed technology review also illustrates why ordinary cloud, billing and provisioning improvements should not automatically earn AI credit. [stc Kuwait overview](https://cws.stc.com.kw/DigitalStatic/AnnualReport2025/index.html), [technology review](https://cws.stc.com.kw/DigitalStatic/AnnualReport2025/pdfs/sections/en/Technology%20sector.pdf)\r\n\r\n**Bahrain provides two distinct operating examples.** Batelco's Basma assistant has been extended to billing and other service tasks with backend integration. Beyon Cyber's security platform is described as supporting alert investigation with human oversight in managed security operations. The annual report spells it Oryx AI; the sustainability report uses Orryx.AI. This is bounded service-operation evidence, not proof of whole-Beyon autonomy. These practices belong to different entities within Beyon. stc Bahrain separately reports a deployed AI customer-experience assurance framework using topology and fault correlation, while its Huawei agreement concerns future joint development. [Beyon sustainability report, pp.58-59](https://beyon.com/wp-content/uploads/2026/03/Beyon_ESG2025_English.pdf), [Beyon annual report, pp.26-31](https://beyon.com/wp-content/uploads/2026/03/Beyon-AR2025-ENGLISH.pdf), [stc Bahrain assurance](https://www.stc.com.bh/media-center/template?id=4346), [Huawei agreement](https://www.stc.com.bh/media-center/template?id=4188)\r\n\r\n## The evidence that would change an executive decision\r\n\r\nThe proposed company study asks each participating operator to nominate three material workflows and provide a small, reproducible evidence pack. Each workflow should show its eligible volume, actual AI-assisted share, exceptions, escalation route and business owner. The organization should identify which decisions remain advisory, which require confirmation and which execute automatically. A sample of successful cases is insufficient: the pack needs failures, rework and reversals.\r\n\r\nOutcome measurement should retain handling time, elapsed resolution time, quality, customer impact, model cost, review effort and control failures separately. The same apparent productivity gain can represent faster work, transferred work or reduced service quality. Baseline period, case mix and comparison method should accompany any percentage improvement.\r\n\r\nFor employee adoption, licensed seats and training attendance are useful inputs. Sustained use, changed tasks, demonstrated skills and supervision burden are more informative. For governance, existence of a committee is a starting signal. The consequential evidence is a decision it made and a risk it controlled.\r\n\r\n## A credible comparison needs comparable entities\r\n\r\nThe launch cohort mixes international groups with local operators. stc group, stc Kuwait and stc Bahrain overlap: stc's 2024 subsidiary table records full ownership of Bahrain and 51.84% of Kuwait. e& also reports Mobily among its associates. Country labels in this review identify headquarters or the local operating market; they do not isolate where every disclosed capability operates. [stc ownership table](https://www.stc.com/content/dam/stc/stc-annual-report-2024/subsidiaries-investment.html), [e& report, associates note](https://www.eand.com/content/dam/eand/assets/docs/annual-report/2025/eand-integrated-annualreport-en.pdf)\r\n\r\nThe first public comparison should therefore display group and local-operator strata, source coverage and explicit unknowns. A future local-market study should scope evidence to Saudi stc operations, e& UAE, Ooredoo Qatar, Zain Kuwait and Batelco Bahrain, rather than silently relabel group disclosures. Parent and subsidiary records should remain linked.\r\n\r\n## A stronger headline for the benchmark\r\n\r\n“Who is most AI-native?” is attractive but premature as a factual conclusion from this corpus. The defensible launch story is more useful: **Gulf telecoms are showing how AI enters real operating work. The next test is coverage, accountability and verified value.**\r\n\r\nPublish a workflow map, deployment-stage timeline and evidence profile beside the company table. Let an executive inspect the claim, scope and source behind every mark. Invite companies to correct facts and contribute evidence under a consistent protocol. Over successive waves, the research can establish which practices spread, which gains persist and which organizational changes explain the difference. That longitudinal record is the beginning of a proprietary benchmark.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "energy-public-evidence", "title": "AI in Gulf energy and utilities: examine the operating record", "dek": "Production, maintenance and control: evidence across ten selected operating entities.", "kind": "Industry evidence review", "sector": "Energy & Utilities", "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# AI in energy and utilities: where intelligence enters the operating system\r\n\r\nMayden AI Native Index | Energy & Utilities expansion | Research cutoff: 10 October 2026\r\n\r\n**Research status:** exploratory source review with a separate AI-agent challenge completed. Human and academic validation remain pending. This report does not rank actual company maturity. It covers ten selected entities across Saudi Arabia, the UAE, Qatar, Kuwait and Bahrain; Oman is outside this edition. Eight have proposed qualifying claims in this corpus. Two remain unknown records because current qualifying evidence was not established. Their inclusion makes the disclosure limits visible.\r\n\r\n## The executive finding\r\n\r\nThe most useful distinction in industrial AI is the decision being changed. A system may detect a condition, recommend an intervention, or directly adjust a process. Those are different operating arrangements. Announcements often blur them; organizational measurement must keep them separate.\r\n\r\nFor Mayden, an AI-native energy enterprise should therefore be investigated through observable work: the signal entering a model, the operating decision it changes, the accountable role, the permitted action, and the feedback used to improve it. A portfolio of impressive demonstrations cannot answer those questions by itself. Neither can a large digital-transformation budget.\r\n\r\nThis review finds evidence ranging from automated process control to maintenance prediction, engineering assistance, employee learning and customer service. It also finds procurement intentions and digital platforms whose AI features are described as future additions. These categories remain distinct in the database. Company and supplier statements are attributed disclosures, not independent assurance.\r\n\r\n## Start with the boundary between advice and action\r\n\r\nAramco describes permanent adoption at Fadhili and automatic adjustment at Abqaiq. These are useful starting points for examining bounded industrial autonomy. The database records each named site separately from the wider group. It does not infer enterprise-wide autonomy from a successful local installation. [Aramco operating examples](https://www.aramco.com/en/what-we-do/energy-innovation/digitalization/ai-and-big-data)\r\n\r\nThe next investigative step is practical: identify the approved operating envelope, the conditions requiring operator intervention and the fallback when model confidence or data quality deteriorates. The public passage is a starting point for that inquiry, not an answer to every control question.\r\n\r\nADNOC's Neuron5 release supports a different description: equipment monitoring and maintenance prediction at named facilities. It should not be recast as autonomous maintenance execution. Its pilot benefit figures describe potential improvements, so the outcome ledger preserves that qualification. [ADNOC deployment release](https://www.adnoc.ae/en/news-and-media/press-releases/2024/adnoc-deploys-pioneering-ai-enabled-process-optimization-technology)\r\n\r\nBapco's upstream case illustrates a third boundary. A vendor-issued release describes an initial field-tested model that supports engineers' scenario analysis. Routine post-test operation and wider expansion have not been established. This is evidence of decision support; the source does not establish independent machine authority over production. The release names an upstream executive, which helps prevent its claims being transferred to Bapco Refining. [Geminus-issued release carried by Automation.com](https://www.automation.com/article/geminus-ai-bapco-energies-slb-physics-native-ai-bahrain-energy-infrastructure)\r\n\r\nThe analytical implication is that autonomy should be assessed through delegated authority, not the sophistication of a model name. For a questionnaire, ask an operations leader to select one live workflow and show exactly where responsibility passes between people and software.\r\n\r\n## Distinguish AI work from digital work\r\n\r\nQatarEnergy LNG's 2024 sustainability report explicitly identifies machine learning in predictive maintenance and Bypass Override Risk Assessment. The same section describes broader automation initiatives. Mayden records the AI-specific statement without turning the surrounding digital portfolio into an AI count. The report also separates general digital literacy from formal AI certification. [QatarEnergy LNG report, printed pages 74-75](https://www.qatarenergylng.qa/Portals/0/DNNGalleryPro/uploads/2025/11/12/QatarEnergyLNGSustainabilityReport2024-EN.pdf)\r\n\r\nThis distinction is important for executive assessments. Ask what technique is actually used, where its output enters the workflow and whether the workflow continues to run in production. A rules-based workflow can be valuable without being machine learning. Calling every automation project AI makes comparisons less meaningful.\r\n\r\nThe Saudi Electricity Company 2024 report provides a useful edge case. It calls contracting RPA artificial intelligence but gives no corresponding inference or learning mechanism. That item is retained as an ambiguous, ineligible lead. Separately, the report explicitly puts AI integration within the Technical Committee's remit; this supports a narrower governance claim, without establishing model-level controls. [Annual report, printed pages 156-157 and 252-253](https://www.se.com.sa/-/media/sec/Investors/Saudi-Electricity-Company-Annual-Report-2024_Eng_V21.ashx)\r\n\r\nKNPC's accessible technology magazine is another caution. It describes existing digital maintenance processes while presenting AI predictive maintenance as a future upgrade. Its publication timing is unclear and nearby charts refer to 2018. The record is consequently historical context, not evidence of current AI deployment. The newer annual-report archive was found, but its report contents were not successfully retrieved during this review. [KNPC magazine](https://www.knpc.com/knpc/media/pdf/tech-magazine-1_1.pdf), [annual-report archive](https://www.knpc.com/en/media-center/publications/reports/annual-reports)\r\n\r\n## Follow how staff actually use the tools\r\n\r\nEmployee capability is easier to discuss than to measure. A participant count may show programme coverage; it does not prove proficiency or sustained use. Aramco's training disclosure and ADNOC's annual review provide candidate coverage indicators, but neither justifies a claim that every trained employee can independently operate or evaluate an AI system. [Aramco workforce disclosure](https://europe.aramco.com/en/news-media/news/2025/aramco-deploys-ai), [ADNOC 2025 review](https://reports.adnoc.ae/e/2025-a-year-in-review)\r\n\r\nKuwait Oil Company's academy disclosure is more specific about programme design: completed training, a defined duration and practical oil-and-gas applications. Its evidence is still bounded to the reported cohort. Separate SLB disclosures describe a mud-loss workflow and an earlier proof-of-value project; the latter remains a pilot in this dataset. A supplier release is an interested primary account, not independent corroboration. [KOC academy](https://www.kockw.com/sites/EN/EMagazine/Pages/Events/1082.aspx), [SLB 2023 year-end results](https://investorcenter.slb.com/news-releases/news-release-details/slb-announces-fourth-quarter-and-full-year-2023-results/), [SLB third-quarter 2023 results](https://investorcenter.slb.com/news-releases/news-release-details/slb-announces-third-quarter-2023-results/)\r\n\r\nDEWA's reported engineering and service workflows extend the inquiry beyond industrial plants. Their relevance is the integration of AI into routine work. However, AI generating software test artifacts is not evidence that the AI itself is systematically evaluated. Website-grounded answers likewise do not, by themselves, establish accuracy or knowledge freshness. [DEWA engineering deployment](https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/7/dewa-deploys-agentic-ai-across-its-digital-platforms), [Rammas disclosure](https://dewa.gov.ae/en/about-us/media-publications/latest-news/2026/1/dewas-virtual-employee-rammas-handled)\r\n\r\nA useful company report should show adoption and proficiency separately: eligible staff, active users, assessed competence, workflow dependence and exception rates. These remain proposed measures for future proprietary research, not statistics collected in this edition.\r\n\r\n## Unknowns are research priorities\r\n\r\nKAHRAMAA's accessible platform launch identifies a specific initiative, but its operating applications are largely future-tense. A subsequent Google-related lead could not be read in full and is excluded. Bapco Refining's indexed report and procurement leads likewise do not establish qualifying deployed practice. The group annual-report download was incomplete, and the tender page was inaccessible. [KAHRAMAA launch](https://km.qa/MediaCenter/Pages/NewsDetails.aspx?ItemID=431)\r\n\r\nThese cases do not show that the organizations lack AI capability. They show what this retrieval exercise could establish. A responsible explorer should display the unknown state and its reason directly, rather than hide it beneath a zero labelled maturity.\r\n\r\nThe ten records also do not form ten independent corporate groups. KOC and KNPC share KPC ownership; Bapco Upstream and Bapco Refining are siblings. KAHRAMAA is a public utility corporation. Country labels describe the selected entity's base and do not constrain the geography of group evidence. A cross-country league table would overstate comparability.\r\n\r\n## Questions that would turn disclosures into organizational evidence\r\n\r\nA company interview should move through one named live workflow before asking about the broader portfolio:\r\n\r\n1. What decision or task changed, and on what date did the system enter routine use?\r\n2. Which assets, teams or transactions are eligible, and what share actually uses it?\r\n3. Can the system act directly, or does a named role approve its recommendation?\r\n4. What input, model version, output, approval and override can be reconstructed afterward?\r\n5. What tests and operating thresholds decide whether it is deployed, changed or withdrawn?\r\n6. How are failures, false alerts and near misses captured, including cases where staff bypass it?\r\n7. Who maintains the knowledge or training data, and how are permissions and revisions controlled?\r\n8. What outcome changed against a defined baseline, with what denominator and alternative explanations?\r\n9. Which skills are assessed, rather than inferred from course attendance?\r\n10. What remains reusable when a vendor, model or individual employee changes?\r\n\r\nThese questions require different respondents and artifacts. Operations can explain decision authority; engineering can demonstrate evaluation; workforce teams can substantiate learning; risk and assurance functions can show approvals and incidents. A single confident executive cannot substitute for all four perspectives.\r\n\r\n## What this expansion can support now\r\n\r\nThe immediate product is a transparent evidence collection and an informed executive discussion. It is not a representative estimate of Gulf adoption, a causal evaluation of business impact, or a validated maturity instrument. A separate agent challenged every proposed level-3 claim and sampled lower levels. Its decisions are recorded beside the source proposals; this is not human or academic validation.\r\n\r\nThe underlying JSON preserves source proposals, excluded leads, access states, source locators, implementation states, limitations and per-company search logs. The separate review downgraded the three Bapco field-pilot claims and excluded a general DEWA governance-framework statement. Outcomes are stored separately and do not increase evidence points. Missing themes mean no qualifying evidence was located in this bounded corpus.\r\n\r\nThe next research wave should prioritize current report retrieval for weak-access entities, a systematic Arabic search, direct evidence of decision lineage and AI-specific control operation, and a consistent refresh window. Mayden can then invite organizations to correct factual scope and supply additional artifacts through a documented process, without selling a higher score or implying that payment establishes capability.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "logistics-public-evidence", "title": "AI in Gulf aviation and logistics: trace the operating change", "dek": "A review of selected airlines, logistics groups and airport operators.", "kind": "Industry evidence review", "sector": "Aviation & Logistics", "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# AI at work in GCC aviation and logistics\r\n\r\nExploratory public-evidence research | Evidence cutoff: 10 October 2026 | Source claims challenged for the public evidence edition\r\n\r\nThe most informative question for aviation and logistics executives is where AI changes an operating decision. A passenger-facing assistant can attract attention, but a cargo forecast, instructor-allocation system or terminal workflow provides a more direct view into how a company works. This review examines ten entities headquartered in Saudi Arabia, the UAE, Qatar, Kuwait and Bahrain. It finds several specific operating cases and substantial unanswered questions about their reach, controls and results.\r\n\r\nThis is a purposeful research cohort, not a representative survey of the region. The observations mix airlines, a training business, shipping, ports and logistics groups. Countries identify headquarters. They do not identify the territory in which every application operates. The accompanying evidence file retains disclosure-specificity coding and separate claim-level challenges for all proposed top anchors. It does not certify organizational maturity or produce a national league table.\r\n\r\n## The operating decision is the useful unit of analysis\r\n\r\nThree kinds of implementation help make the concept of an AI-native enterprise concrete: improving allocation, connecting information to work, and governing how systems are developed. These are useful research categories rather than a new scoring system. The Mayden framework retains its nine established dimensions, and repeated announcements do not accumulate points.\r\n\r\nSaudia Academy illustrates allocation in a training operation. Microsoft describes an implemented AI scheduling system delivered with Netways. The record identifies resources and workflow, rather than merely calling the organization digital. Its reported instructor and facility counts provide context. They do not establish adoption across Saudia Group, and the wider transformation outcomes cannot be attributed to AI alone. An interview should ask who can override a schedule and how conflicts, fairness and utilization are evaluated. [Microsoft customer account](https://www.microsoft.com/en/customers/story/1782079017420911107-saudia-academy-dynamics-365-customer-service-travel-and-transportation-en-saudi-arabia)\r\n\r\nQatar Airways Cargo offers a commercial allocation case. Wiremind reports that CARGOSTACK Optimiser went live through a gradual rollout and describes model testing with airline staff. That combination makes the implementation more informative than a partnership announcement: a business process, deployment stage and validation activity are visible. The account still leaves important questions unanswered, including model-comparison results, acceptance criteria and the extent of live coverage. These would be appropriate requests for the proprietary questionnaire. [Wiremind implementation account](https://www.wiremind.io/blog/qatar-airways-cargo-cargostack-optimiser)\r\n\r\nBahri reports vessel-data applications supporting maintenance and voyage-related decisions, alongside a shared enterprise information platform. The operational detail is concentrated in particular shipping businesses. It should not be turned into an assertion that every group function has the same capabilities. For executives, the useful follow-up is whether model recommendations change maintenance decisions and whether their contribution can be separated from sensor installation, process redesign and other investments. [Bahri 2025 annual report, printed pages 106–107](https://www.bahri.sa/media/go4a3eke/bahri-ar25_english-final-to-tadawul.pdf)\r\n\r\n## An interface matters when it connects to work\r\n\r\nDP World describes its Jafza digital twin as part of an AI-driven operating suite, with real-time information connected to basic terminal processes. That supports a bounded internal operating case. It does not establish that all terminal decisions are autonomous or that the same implementation is present throughout DP World's international network. The next layer of evidence would specify actions the system can execute, exceptions requiring approval, fallback arrangements and safety evaluation. [DP World operating description](https://www.dpworld.com/en/insights/how-ai-is-transforming-global-trade-possibilities)\r\n\r\nGulf Air's FalconEye is a different interface case: executives ask questions of live operational information and can use voice to open dashboards. Microsoft describes the airline example in its voice-agent publications. This is evidence about a particular managerial interface. It does not establish automated executive authority, universal answer accuracy or adoption by Bahrain Airport Company. A credible company report would investigate which answers require source references and how users handle conflicting information. [Microsoft voice-agent account](https://techcommunity.microsoft.com/blog/azure-ai-foundry-blog/public-preview-voice-native-agents-in-microsoft-foundry/4502756), [related Microsoft account](https://devblogs.microsoft.com/foundry/agent-service-build2026/)\r\n\r\nInterfaces should therefore be assessed through the work they support. Does an answer lead to a recommendation, a prepared transaction, or an executed action? Who remains responsible? Can the action be reversed? A public statement that a company has adopted a conversational tool rarely answers all four questions. Mayden's interview should.\r\n\r\n## The organizational system is harder to observe\r\n\r\nEmirates Group provides a useful IT example. Its September 2026 disclosure describes ARRIVE, an internal software-engineering framework, and staff onboarding through an AI gateway. The documentation connects AI-assisted development with recorded decisions and human accountability. This is valuable evidence about an operating mechanism. It remains bounded to the described IT context; it is not an enterprise-wide assurance finding. [Emirates ForsaTEK 2026 disclosure](https://www.emirates.com/media-centre/catalysing-the-next-wave-of-innovation-at-the-emirates-group-forsatek-2026-concludes-successfully/)\r\n\r\nThe distinction matters because an AI-native enterprise needs repeatable organizational practices, not simply a catalogue of applications. A strong diagnostic should ask how experimentation changes production standards, how staff know which tools they may use, and whether decision records survive across models and suppliers. The public cases suggest where to investigate these practices. They cannot settle the questions without direct evidence.\r\n\r\n## Plans need to remain visible as plans\r\n\r\nMilaha's annual report describes AI integration beginning at its logistics centre and outlines warehouse-related partnerships. Its Google Cloud announcement also identifies intended data and AI work. These are relevant signals of a named programme. The retrieved wording does not establish the production state, model configuration or operational coverage of every component. The dataset therefore preserves in-progress and planned statuses instead of converting a transformation narrative into completed deployment. [Milaha 2025 annual report](https://www.milaha.com/sites/default/files/2026-02/Milaha-Annual-Report-2025-EN.pdf), [Milaha cloud and AI announcement](https://milaha.com/en/news-and-media/milaha-enhances-data-ai-solutions-google-cloud)\r\n\r\nJazeera Airways requires a different reading across time. Its supplier announced selection of an AI-enabled fuel-analysis platform in 2022. The airline's 2024 report subsequently refers to continued participation in that programme. Together these sources support a bounded implementation record. The supplier's fuel-efficiency comparison is not a Mayden ranking, and it is not evidence of the airline's overall AI maturity. Jazeera's separate customer-experience transformation agreement is recorded but does not, by itself, establish an internal operating capability. [Jazeera 2024 annual report](https://investorrelations.jazeeraairways.com/media/1367/j9-fy24-annual-report-english-mar-4-2025.pdf), [OpenAirlines announcement](https://www.prnewswire.com/news-releases/jazeera-airways-chooses-skybreathe-360-eco-flying-platform-to-cut-carbon-emissions-301567557.html)\r\n\r\n## Scope and missing evidence change the interpretation\r\n\r\nMakhazen demonstrates why corporate identity belongs in the research database. Historical Agility reporting and current group structures cannot be treated as interchangeable. The 2024 report lists an AI-enabled security control, but its consolidated perimeter predates the subsequent restructuring. Employee-AI disclosures from the separately listed UAE Agility Global entity are not automatically evidence for the Kuwait-headquartered company. Readers should be able to see the historical boundary beside the claim. [Historical Agility 2024 report on the current issuer domain](https://makhazen.com/wp-content/uploads/2025/05/Agility-Annual-Report-2024-En.pdf), [Makhazen 2025 report](https://makhazen.com/wp-content/uploads/2026/05/Makhazen-Annual-Report-2025-English.pdf)\r\n\r\nBahrain Airport Company exposes another measurement problem: accessible public evidence may be sparse. The identified town-hall coverage was only recoverable through indexed material and contained broad technology ambitions. It is excluded from scoring. The airport subsidiary also does not inherit the airline's FalconEye evidence. All unknown dimensions mean that this bounded search did not establish a qualifying case. They do not mean that the company lacks AI capability.\r\n\r\nThese limitations are part of the product's value. An executive should be able to correct an entity boundary, supply a missing source, or challenge an implementation status. That creates a better benchmark than presenting a precise-looking number with hidden assumptions.\r\n\r\n## Questions for the operating team\r\n\r\nThe proprietary research should follow one live workflow from input to consequence. Six questions provide a practical starting point:\r\n\r\n1. Which operating decision has changed, and who uses the system regularly?\r\n2. What proportion of the relevant work does it cover, with what exceptions?\r\n3. What can it recommend or execute, and who can override it?\r\n4. Which evaluation or comparison justified deployment and continued use?\r\n5. What evidence links the outcome to AI rather than the wider transformation?\r\n6. How are errors, incidents and model changes recorded and resolved?\r\n\r\nA company should be able to answer with examples and supporting artifacts, while protecting confidential operational information. The resulting report can distinguish a promising pilot from a repeatable organizational capability and identify the next useful improvement. That is a stronger engagement proposition than promising a maturity verdict from public announcements.\r\n\r\nFor publication, use the specific operating cases as the story. Preserve the separation between disclosure strength, implementation state and demonstrated benefit. The research supports a serious discussion about how work is changing; a representative claim about the whole GCC would require a different sampling design and direct company participation.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "manufacturing-public-evidence", "title": "AI in Gulf manufacturing: from process signals to operating decisions", "dek": "Production, maintenance and quality across ten selected industrial entities.", "kind": "Industry evidence review", "sector": "Manufacturing & Materials", "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# The factory decision is the test\r\n## Manufacturing & Materials — Mayden AI Native Index research edition\r\n**Evidence cutoff: 10 October 2026. Ten purposefully selected entities across five markets. Public evidence, not a measurement of actual enterprise maturity.**\r\n\r\nA factory can announce an AI partnership without changing a production decision. It can also run a useful predictive model for years without describing it publicly. An index that treats either possibility carelessly will reward communications activity and punish silence. The manufacturing research therefore asks a narrower, more useful question: what operating practice can an external reader actually trace to a primary source, and how far does that source support the claim?\r\n\r\nThe strongest cases in this corpus describe a decision close to production: identifying an anode-quality issue, prioritising a safety risk, or using AI in an order-planning workflow. These are more informative than an undifferentiated count of digital initiatives. They reveal where information enters work. They do not establish that the whole enterprise is AI-native, that a model is accurate, or that a reported improvement was caused by AI.\r\n\r\nThis report turns those cases into questions executives can use. Its central proposition is an editorial interpretation: an AI-native manufacturer would need repeatable arrangements for deploying, supervising, maintaining and learning from models across operating conditions. A successful isolated application is evidence of an application. The additional organisational arrangements remain an empirical question.\r\n\r\n## What the cohort covers\r\n\r\nThe ten entities are SABIC and Maaden in Saudi Arabia; Emirates Global Aluminium and Ducab in the UAE; Industries Qatar and QAPCO in Qatar; EQUATE and Petrochemical Industries Company in Kuwait; and Aluminium Bahrain and GARMCO in Bahrain. Selection creates a small, purposeful view of chemicals, mining, metals and industrial manufacturing. It is not a census, a sample of the largest ten firms, or a representative estimate of GCC adoption. Oman is outside this five-market expansion.\r\n\r\nCorporate boundaries are consequential. Industries Qatar has exposure to QAPCO; PIC has an ownership relationship with EQUATE. These are not independent observations of entirely separate economic systems. Maaden's investment in Alba also creates overlap, while SABIC has an ownership relationship with an energy-sector company covered elsewhere in the Index. No operating claim is transferred from an investee to its shareholder simply because that relationship exists. Alba's retained cases concern its Bahrain operation or a specified central function, rather than every operation in its changing corporate perimeter.\r\n\r\nThe cohort contains both integrated operators and investment-group structures. Comparing their disclosure evidence can be useful if that distinction is visible. Comparing the resulting numbers as if they represented identical factories would be misleading. Readers should open the company scope and supporting passage before interpreting a difference.\r\n\r\n## Production evidence is strongest when the unit is visible\r\n\r\nSABIC's 2025 report states that its AI and predictive tools are deployed across a majority of global manufacturing plants. It separately describes pilots in demand forecasting, pricing and planning; connected laboratory systems; workforce adoption programmes; and risk assessments that include AI. The separation matters: a broadly deployed production-tool portfolio does not turn each commercial pilot into a production system. Nor does it establish autonomous control. [SABIC Integrated Annual Report 2025, digital and cybersecurity sections, printed pages 60–62](https://www.sabic.com/en/Images/SABIC-Integrated-Annual-Report-2025-EN_tcm1010-49452.pdf)\r\n\r\nThe executive lesson is to insist on a denominator with a stable definition. “Most plants” is more informative than “many use cases,” but the next discussion still needs to identify eligible sites, processes covered, usage frequency and retirement of unsuccessful models. A portfolio count can rise because a company creates more prototypes, because it divides applications differently, or because it has genuinely expanded productive use. Only the last interpretation necessarily changes operating capability.\r\n\r\nEGA provides a more localised example. Its January 2025 lighthouse announcement describes implemented AI vision for real-time carbon-anode quality assessment. That is a named production task with a recognisable observation mechanism. The surrounding announcement also discusses a broader Industry 4.0 portfolio. Those portfolio totals and general digital-skilling numbers should not be relabelled as AI totals. [EGA announcement, 15 January 2025](https://media.ega.ae/ega-designated-industry-40-global-lighthouse-by-world-economic-forum-in-uae-and-world-aluminium-industry-first/)\r\n\r\nA quality model creates several different questions. Does it flag an item for inspection, change a process setting, or reject output automatically? Who resolves disagreement between the model and an experienced operator? What happens when raw-material characteristics change? The public case supports the existence of a particular application; these questions define the deeper evidence needed for an organisational benchmark.\r\n\r\nDucab's Blade announcement identifies AI software used in manufacturing-order planning. The same release describes expected improvements in production and downtime. This review retains the stated planning use and keeps those expected benefits separate. It does not present targets as achieved results or treat every sensor in the wider project as AI. [Ducab Blade announcement, 13 June 2023](https://ducab.com/media-center/press-releases/ducab-group-expedites-operations-with-the-launch-of-blade-the-smart-factory-project-of-the-future-based-on-industry-4-0-technologies/)\r\n\r\nFor a planning application, usefulness depends on more than forecast accuracy. An apparently better plan may violate an equipment constraint, create downstream inventory, or increase schedule instability. A serious company interview should connect the recommendation to the supervisor who accepts it and the production consequences that person monitors.\r\n\r\n## A pilot deserves recognition without being called a rollout\r\n\r\nEGA's December 2024 EX announcement is explicit about an experimental facility and an intended future autonomous production system. It describes the construction of pilot reduction cells, prospective AI capabilities and a longer industrialisation horizon. Existing developed use cases are discussed alongside these ambitions. The future autonomous-control objective remains a different claim from an implemented quality-inspection application. [EGA EX pilot announcement, 9 December 2024](https://media.ega.ae/egas-ex-technology-pilot-to-be-used-to-prove-most-advanced-industry-40-capabilities-in-aluminium-industry/)\r\n\r\nThis distinction is useful even when a later announcement confirms that a physical pilot has started producing metal. Commissioning the industrial asset does not, by itself, establish that a particular AI control is operating routinely. The evidence needed to cross that boundary is a dated account of the AI system entering service, its operating envelope and the accountable owner.\r\n\r\nAlba's 2023 announcement describes completion of a green-anode density prediction model and a dashboard developed with NAIRDC. The review treats this as a specific completed development or pilot rather than assuming sustained production deployment. Separately, the 2025 Seeq announcement contains future integration language despite its adoption-oriented headline. It is retained as a named commitment. [Alba–NAIRDC project, 12 July 2023](https://www.albasmelter.com/en/article/alba-joins-forces-with-nasser-artificial-intelligence-research-and-development-centre-to-advance-in-ai); [Alba–Seeq announcement, 27 April 2025](https://www.albasmelter.com/en/article/alba-sets-regional-benchmark-as-first-middle-east-smelter-to-adopt-ai-powered-seeq-platform)\r\n\r\nExecutives should not read a lower evidence stage as criticism of experimentation. A well-run pilot can prevent an expensive operational mistake. The relevant management question is whether there is a disciplined transition: a baseline, a success criterion, a safety review, an owner, a decision date and a route to withdrawal. A benchmark should be able to recognise that discipline without pretending that intent has already produced enterprise-wide change.\r\n\r\n## The organisation appears in the less theatrical applications\r\n\r\nAlba's first-quarter 2026 company magazine describes AI used to identify high-risk operating areas from incident patterns and risk drivers. It also describes a Scope of Work assistant that helps prepare structured contract documents for an approval process. A conduct-policy chatbot is described with less conclusive deployment language. These are different levels of support, even though they appear in the same publication. The safety narrative does not establish a causal reduction in accidents; the drafting assistant does not establish autonomous contracting. [Alba Al Bayan Q1 2026, physical pages 24–25](https://www.albasmelter.com/uploads/albayan2026_q1.pdf)\r\n\r\nThese examples direct attention beyond the furnace or production line. An enterprise changes when a tool becomes part of how work is specified, reviewed and escalated. Contract drafting can affect procurement quality. Safety prioritisation can influence the allocation of supervisory attention. Both deserve scrutiny because their consequences depend on human interpretation and follow-through.\r\n\r\nThe next interview should ask for a recent decision reconstructed end to end. Which information entered the tool? Which recommendation emerged? Who inspected it? What changed as a result? Could the organisation reproduce the reasoning after a dispute or incident? A useful answer would identify records and responsibilities. A generic assurance that humans remain in control would leave the most important details unresolved.\r\n\r\nAlba's August 2024 course announcement provides another bounded organisational signal: a three-day AI programme underway for 35 employees, with specified generative-AI and prompting topics. Those are participants in an announced course, not independently verified completions or evidence that the workforce has mastered AI. [Alba training announcement, 21 August 2024](https://www.albasmelter.com/en/article/alba-partners-with-nvtcs-randd-arm-to-upskill-employees-with-artificial-intelligence-knowledge)\r\n\r\nA training measure should distinguish attendance, assessed competence and changed work. Different roles need different capabilities. A process engineer may need to recognise model drift; a procurement specialist may need to verify generated specifications; a manager may need to judge when automation is inappropriate. Counting all three people as identically trained loses information that matters to performance.\r\n\r\nAlba also disclosed an updated Code of Conduct with responsible-AI guidance in September 2026. This supports the presence of an issued policy. The disclosure does not supply enough detail to assess whether particular model controls have been applied effectively. [Alba regulatory disclosure, 23 September 2026](https://www.albasmelter.com/uploads/Disclosure_of_MI_Alba_Reinforces_Ethical_Leadership_Through_Updated_Code_of_Conduct_23.09.2026_1.pdf)\r\n\r\n## Exploration is a distinct operating setting\r\n\r\nMaaden's technology and partnership pages describe AI and data modelling in mineral exploration, including work with Ivanhoe Electric. They are useful primary descriptions of an operating activity and a named collaboration. They are live undated pages, however, and do not provide a deployment date, model-performance evaluation or a clear coverage denominator. [Maaden technology page](https://www.maaden.com/our-innovation/tech-innovations); [Maaden partnerships](https://www.maaden.com/discover-us/partnerships)\r\n\r\nExploration is not the same decision environment as continuous process control. Its evidence can involve sparse observations, uncertain geological interpretation and long feedback cycles. A common organisational framework needs sector-specific questions. The principle is to compare how an organisation makes and governs AI-supported decisions, while resisting the temptation to compare unlike model metrics.\r\n\r\nAn exploration team could explain how recommendations change survey priorities, how new field observations are incorporated, and how unsuccessful predictions are documented. A smelter could explain how a model reacts to process changes and how operators override recommendations. These are different technical accounts of a common organisational obligation: keeping a decision system reliable in use.\r\n\r\n## What remains unknown is part of the result\r\n\r\nIndustries Qatar's examined annual-report passages mention AI in the economic and market backdrop. Such language does not demonstrate an implemented group practice. QAPCO's report archive and older PDF links presented access problems; indexed references to a predictive-maintenance plan were not promoted into verified source evidence. EQUATE's accessible executive summary did not yield a qualifying AI practice in the passages searched. For PIC and GARMCO, the bounded company-site search and identity material did not provide qualifying operating evidence.\r\n\r\nThese findings do not establish that those companies lack AI. They establish the limits of this research corpus. Report access failures, different disclosure customs, corporate structure and search vocabulary can all affect visibility. The correct label is unknown within the bounded search, accompanied by the search log.\r\n\r\nA reader must also distinguish a failed retrieval from a source that was read and found inapplicable. The former leaves an unresolved lead. The latter may rule out a particular interpretation, such as treating a general technology investment as AI. The dataset records these differences so that a subsequent researcher can make a targeted correction rather than repeat the entire search.\r\n\r\n## Questions that can turn disclosure research into a benchmark\r\n\r\nA proprietary company assessment should start with two material decisions selected by the firm and one selected by the interviewer. This reduces the chance that the interview covers only a flagship demonstration. For each decision, the respondent should identify the task, operating owner, affected sites, user population, deployment date and current stage. The assessment should then request a small evidence sample rather than unrestricted access to operational systems.\r\n\r\nThe questions should cover nine dimensions with sector-specific anchors. Where is AI materially involved in core work? Which employees have demonstrated role-relevant competence? What responsibilities sit with technology partners? How do outputs enter systems or work queues? Can a material decision be reconstructed from retained records? What evidence determines whether an experiment proceeds? Which actions can the system take, and who can stop it? How is operational knowledge maintained? Which AI-specific controls have actually been applied?\r\n\r\nThese are proposed interview questions, not findings about the cohort. They should be tested with operators for clarity before being used to create a validated organisational measure. A polished questionnaire alone cannot establish validity. Mayden would still need consistent coding, independent adjudication, evidence of reliable responses and investigation of whether the resulting dimensions relate to meaningful external outcomes.\r\n\r\n## Reading the manufacturing evidence exhibits\r\n\r\nThe heatmap compares the ten selected entities across nine dimensions. U means no qualifying evidence was located in the bounded corpus; it does not mean the capability is absent. Level 1 denotes a specific commitment or pilot, level 2 an implemented practice, and level 3 an implemented practice with concrete operating detail. Each cell takes the highest supported, adjudicated level for that dimension. Several descriptions of one application do not accumulate extra points.\r\n\r\nThe points comparison uses a fixed denominator of 27: nine dimensions with a maximum of three points each. The displayed value is the supported total divided by 27 and multiplied by 100, rounded to a whole number. Unknown cells add no documented points, but remain unknown in interpretation. A higher total therefore describes a stronger public evidence record under these rules; it is not a percentage of enterprise maturity or proof of better factory performance. Equal underlying totals share a position, and records without qualifying evidence remain unranked.\r\n\r\nThe anchor-count exhibit separates commitments or pilots, implemented practices, and implemented practices with operating detail. These are claim counts, not counts of independent projects, factories or companies. One operating application may support more than one dimension. This helps explain the distinction between an implemented inspection practice and a future autonomous-control objective without treating every mention of AI as equivalent.\r\n\r\nThe source-dependence exhibit removes one source URL at a time and recalculates the points, showing the result when the most influential URL is removed. A large decrease identifies concentration in the disclosed evidence, often in an annual report. It does not simulate a factory losing its AI capability. Different URLs can also contain related company assertions, so a smaller decrease is not proof of independent corroboration. Read the chart alongside the entity scopes: shareholder and investee records can overlap even when their source URLs differ.\r\n\r\nThe practical outcome of this edition is an inspectable research base and a sharper conversation with manufacturers. A future benchmark becomes credible by preserving these distinctions over time: company versus site, intention versus operation, activity versus outcome, and undisclosed versus absent. The index should make it easier to ask the next difficult question, not make the available evidence appear more complete than it is.\r\n\r\n**Evidence and reproducibility.** The companion manufacturing-evidence.json contains company scopes, source URLs, passage locators, proposed evidence levels and per-company search limits. Separate AI-assisted source challenge is recorded separately; the released calculations use the retained adjudicated decisions. Company assertions remain company assertions. This edition includes no confidential company access, respondent survey, statistical meta-analysis or validation of actual AI-native organisational maturity.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "retail-public-evidence", "title": "AI in Gulf consumer businesses: follow the operating workflow", "dek": "Demand, inventory and service across ten selected retail and consumer entities.", "kind": "Industry evidence review", "sector": "Retail & Consumer", "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# The decision behind the shopping experience\r\n## Retail & Consumer — Mayden AI Native Index research edition\r\n**Evidence cutoff: 10 October 2026. Ten purposefully selected entities across five markets. Public evidence, not a measure of actual enterprise maturity.**\r\n\r\nThe visible face of retail AI is easy to photograph: a checkout-free store, a shopping robot or a conversational assistant. The more consequential organisational question is often less visible. How does a recommendation become a replenishment order? Who checks an AI-generated supplier questionnaire? Which person can reject an automated suggestion, and what information supports that decision?\r\n\r\nThis research examines those questions through primary disclosures from ten retail and consumer businesses. The cases show several ways AI enters work: forecasting, recruitment, customer feedback, checkout, media delivery, supplier engagement and prospective production systems. They do not establish which company is truly the most AI-native. Public communications reveal selected practices under different disclosure incentives. The purpose of this edition is to make those practices and their limitations inspectable.\r\n\r\nMayden's editorial interpretation is that an enduring organisational benchmark should follow decisions through the business. A collection of applications is a starting point. The additional evidence concerns responsibility, maintenance, employee capability, exception handling and evaluation. The cases below show why those details are worth asking about and why a named implementation must remain separate from a plan.\r\n\r\n## A deliberately broad consumer-business cohort\r\n\r\nThe cohort includes Jarir and United Electronics Company, trading as eXtra, in Saudi Arabia; Majid Al Futtaim and LuLu Retail Holdings in the UAE; Al Meera and Baladna in Qatar; Alshaya Group and Mezzan Holding in Kuwait; and BMMI and Trafco Group in Bahrain. There are two entities per market, chosen for purposeful coverage. This is not a representative GCC sample, a market-share ranking or a complete inventory of large consumer businesses.\r\n\r\n“Retail & Consumer” is intentionally broader than grocery retail. Baladna manufactures food; Mezzan and Trafco include distribution and manufacturing activities; other entities operate stores or franchise portfolios. Their tasks and economics differ. A consumer-finance subsidiary also should not automatically stand in for an electronics retailer's operating model. Company scope is therefore part of every evidence record.\r\n\r\nGeographical labels identify the company's home market in this cohort, rather than the location of every implementation. Majid Al Futtaim's named Carrefour evidence concerns its operating business, not Carrefour SA worldwide. LuLu's historical brand announcements can include countries outside the listed GCC group's perimeter; the retained interpretation is restricted to the relevant GCC operations. One company's international reach does not create observations for every country in which it sells.\r\n\r\n## Replenishment makes the management question concrete\r\n\r\nLuLu's 2025 annual report describes AI forecasting and replenishment operating in the UAE. It identifies inputs, integration with core systems, planning-team review, authorisation rules and monitoring. Rollout elsewhere is described as ongoing, not completed. This is unusually useful evidence because it explains several parts of the same operating workflow rather than presenting an application name alone. It is still a company account, not an audit of control effectiveness. [LuLu Retail Integrated Annual Report 2025, printed page 104](https://www.luluretail.com/media/pqobufrm/lulu-retail_iar2025_eng.pdf)\r\n\r\nThe analytical value lies in the relationship between a prediction and an action. A forecast has no operational effect until someone or something changes an order, allocation or stock position. A retailer therefore needs to know both whether predictions are useful and whether the process acts on them appropriately. A system can be accurate on average and still fail on the categories, stores or exceptional events that matter most.\r\n\r\nFor an executive interview, The proposed interview asks for one routine replenishment decision and one exception. The respondent should identify which inputs were available, what the recommendation was, which rule applied, whether a person intervened and what happened next. A redacted decision record would provide stronger evidence of actual responsibility than a general claim that the system is supervised.\r\n\r\nThis is also where autonomy should be defined carefully. Human approval and bounded automation can be a deliberate operating design. They should not be treated as a failed attempt at full autonomy. The question is whether authority is appropriate to the task and explicit enough to inspect. A system that makes a suggestion, one that prepares an order, and one that commits an order have different consequences.\r\n\r\n## A digital shopfront does not establish a digital organisation\r\n\r\nLuLu's October 2026 announcement describes AI shopping robots deployed in UAE stores, with navigation and promotional functions. It supports a specific customer-service implementation. It does not establish how widely shoppers use it, whether it improves conversion, or whether the broader enterprise delegates material decisions to AI. [LuLu robot announcement, 1 October 2026](https://www.luluretail.com/media/news/lulu-brings-ai-powered-robotic-shopping-experience-to-uae-stores/)\r\n\r\nAn earlier LuLu announcement describes a WhatsApp assistant implemented with Yellow.ai, connected to customer and order systems and supported by handoff to a live agent. The integration is an organisationally relevant detail: service answers can be linked to a real transaction rather than a static FAQ. The review does not reproduce the announcement's performance claims as independently verified results. [LuLu WhatsApp announcement, 28 February 2023](https://www.luluretail.com/media/news/lulu-hypermarket-launches-new-ai-powered-customer-experiences-on-whatsapp-in-collaboration-with-yellowai)\r\n\r\nFor either interface, the executive question is what happens when the system cannot complete the task. An effective escalation can matter more than an impressive demonstration. Mayden's proposed interview should examine failed requests, incorrect product guidance, accessibility, language coverage and the transfer of context to an employee. It should also ask whether the company monitors unresolved demand rather than merely counting interactions.\r\n\r\nMajid Al Futtaim's 2021 Carrefour City+ release describes AI-assisted item recognition and automatic charging at a named store in Dubai. This is evidence of a historical service launch with a defined customer journey. It is not evidence that the same configuration remains operational at the cutoff or that a wider network adopted it. [Majid Al Futtaim Carrefour City+ announcement, 6 September 2021](https://www.majidalfuttaim.com/en/media-centre/press-releases/detail/2021/09/carrefour-city-plus)\r\n\r\nHistorical evidence deserves a visible date. An index should neither discard every older implementation nor silently describe it as current. It should make continuity a separate question and seek a dated operating update in the next research cycle.\r\n\r\n## The back office changes the picture\r\n\r\nMajid Al Futtaim's 2024 sustainability report describes an AI pricing engine, AI analysis of customer feedback, AI-supported recruitment and a new AI Academy. The academy has separate learning paths for everyday users, technical specialists and senior leaders. Recruitment uses screening questions and job-fit scores. These passages broaden the observable organisational picture, but they do not establish unbiased hiring, autonomous pricing or company-wide proficiency. The source's other training metrics are not automatically AI Academy results. [Majid Al Futtaim Sustainability Report 2024, pages 70, 76 and 80–81](https://www.majidalfuttaim.com/docs/default-source/reports/maf-sus-pages-full_final.pdf?sfvrsn=7c1de58c_2)\r\n\r\nThese applications lead to different tests. A pricing recommendation should be assessed against commercial constraints and customer effects. A recruitment recommendation requires an account of the human decision and the handling of contested outcomes. Customer-feedback analysis needs a way to check whether important minority complaints disappear inside broad categories. Training needs evidence that people can apply the relevant judgement, not merely attend a course.\r\n\r\nThe common thread is the design of work. When an AI tool shortens an analysis task, the company still has to decide what happens to the time released and who owns the action that follows. Faster classification of complaints is valuable only if the organisation identifies and fixes the right problems. A benchmark should avoid converting a processing-speed claim into a claim about customer outcomes without the intermediate evidence.\r\n\r\nThere is also a knowledge question. A company may successfully classify incoming feedback without maintaining a reliable body of operational knowledge. To distinguish the two, The next company interview should examine how categories change, how corrections are retained and whether later users can inspect the source material behind a conclusion. The current public record is not enough to answer all of those questions.\r\n\r\n## Retail media needs the same operating test as every other service\r\n\r\nIn May 2025, Majid Al Futtaim described Advertima Audience AI deployed across UAE Carrefour hypermarkets, while supermarket expansion remained prospective. The release explains a sensor-to-message mechanism for retail media. The review recognises the operator's implemented service workflow. It does not treat the announcement as independent proof of advertising effectiveness or privacy safeguards. [Majid Al Futtaim–Advertima announcement, 13 May 2025](https://www.majidalfuttaim.com/en/media-centre/press-releases/detail/2025/05/majid-al-futtaim-s-precision-media-and-advertima-set-new-retail-media-benchmark-with-ai-rollout-in-carrefour-stores)\r\n\r\nAlshaya's Media Connect service page advertises AI-assisted media delivery using purchase data and intent signals. That is a specific offering, but the inspected page does not document a live campaign implementation. A separate AI-assisted source challenge excludes this catalogue claim from scoring: it does not establish deployment or a specific internal implementation commitment. The distinction is about the evidence available, not a claim that the service is inactive. [Alshaya Media Connect](https://www.alshaya.com/en/alshaya-media-connect/)\r\n\r\nConsistency is essential here. Selling an AI-labelled product is not the same as using AI to operate a service. A retailer's own media-delivery process can qualify if the source actually describes it in operation. A service catalogue alone leaves an implementation question. The same rule should apply irrespective of the prominence of the company or the attractiveness of the announcement.\r\n\r\nFor a deeper assessment, the relevant questions concern responsibility for targeting decisions, the inputs used, the interpretation of performance measures and the handling of unsuitable outputs. Those questions are proposed due diligence, not findings that either company has failed a requirement.\r\n\r\n## Recovering a source can change the evidence, without changing the standard\r\n\r\nAl Meera's older annual-report link did not initially provide a readable document. The research recovered the full report through the company's new annual-report page and its linked document host. The report describes smart carts launched in 2024, with login, scanning and purchase functions, and places them in an AI discussion. It also records the earlier opening of Al Meera Smart. The named vendor's 2022 announcement remains a separate, earlier project commitment. [Al Meera Annual Report 2024, printed pages 14–17](https://sacorporatewebprodqc001.blob.core.windows.net/public/documents/Al%20Meera%20AR%202024%2013%20APR%202025%20ENG%20FINAL%20(1).pdf); [Zippin project announcement](https://www.getzippin.com/blog/al-meera-partners-with-zippin-to-open-first-checkout-free-stores-in-qatar)\r\n\r\nThis case illustrates why access status belongs in a serious index. An indexed extract may identify a promising lead, but it is not equivalent to reading the source passage. Conversely, a broken old link should not permanently erase evidence that can be recovered from an official archive.\r\n\r\nThe company case still has boundaries. A launched shopping interface does not supply a rollout denominator, a controlled comparison or evidence about every part of the business. The report also cannot establish that every component described as smart uses AI. The retained claim is limited to the specific passage that links the cart workflow to AI.\r\n\r\n## Consumer production adds a different time horizon\r\n\r\nBaladna's 2025 report describes future AI-supported utility metering and AI-capable feed-management software, including intended integration with existing systems. Those are specific operational plans. They remain plans in this review because the passages do not establish completed AI deployment. The company's existing production, water or energy achievements must not be attributed to a future system. [Baladna Annual Report 2025, physical pages 39 and 41, printed pages 76–81](https://baladna.com/storage/uploads/iKUuHjMbSSYZfRre3VkWieoWq.pdf)\r\n\r\nA consumer-products company can have a sophisticated physical operation before an AI project enters service. The Index should not confuse general technological capability with evidence of AI-specific practice. It should also preserve the ability to record a later transition once a dated source establishes installation, routine use and a responsible operating team.\r\n\r\nMezzan provides a more bounded implemented example. Its 2025 sustainability report describes GenAI used to develop an ESG questionnaire with deployment targeting its top twenty high-spend suppliers. This supports GenAI-assisted drafting and a specified intended supplier cohort; it does not confirm twenty completed deployments or responses. It does not establish that AI autonomously scored suppliers or verified compliance. [Mezzan Sustainability Report 2025, physical page 53](https://www.mezzan.com/wp-content/uploads/2026/08/Mezzan-Sustainability-Report-2025-English-.pdf)\r\n\r\nThe distinction is valuable. Generating a questionnaire, interpreting responses and making a supplier decision are three different activities. A respondent should identify exactly where the AI contribution stops. The follow-up should establish what human review checked relevance, completeness and unintended ambiguity before the questionnaire was used.\r\n\r\n## Silence and irrelevant mentions need different labels\r\n\r\nJarir's examined annual-report language about AI-enabled products concerns merchandise, not an internal operating practice. eXtra's issuer notice provides a different kind of evidence: supervision of an Executive Management Committee for Data and AI during fiscal 2024. That supports a specific governance activity, without proving an implemented store or fulfilment application. [Jarir Annual Report 2025](https://wps-media.jarir.com/wp-content/uploads/2026/04/JB_AnnualReport_2025_EN.pdf); [eXtra issuer notice, 30 April 2025](https://www.saudiexchange.sa/wps/portal/saudiexchange/newsandreports/issuer-news/issuer-announcements/issuer-announcements-details/?anCat=1&anId=86829&cs=4003&locale=en)\r\n\r\nBMMI's latest report downloads presented access difficulties, while its accessible 2024 annual report did not yield a qualifying practice in the searched text. Trafco's examined annual-report material described technology without an explicit qualifying AI practice. Neither result justifies calling the company non-native or digitally immature.\r\n\r\nThe research also encountered Arabic news pages labelled as AI-generated summaries. Those labels describe the publisher's process, not the subject company's adoption. Similarly, a supplier portal marked as a testing environment cannot establish production deployment. These exclusions are not minor editorial choices; they prevent a keyword count from becoming a misleading organisational measure.\r\n\r\n## From an evidence page to a useful executive conversation\r\n\r\nThe proposed company interview should begin with a material workflow rather than an abstract self-rating. A retailer might choose replenishment or customer service; a consumer-products company might choose production planning. The interviewer should then ask for an additional workflow outside the flagship area to test whether practices are repeatable.\r\n\r\nFor each workflow, the discussion should cover nine dimensions: the role of AI in core work; role-specific employee capability; partner responsibilities; integration with systems and people; records that allow a decision to be reconstructed; rules for experiments; permitted actions and overrides; maintenance of knowledge; and applied AI governance. Respondents should be able to answer through a guided click-through or voice interview, but the underlying evidence request should be the same.\r\n\r\nA useful company report would separate three things: what was disclosed, what was substantiated in the assessment, and what remains unresolved. It would explain the next evidence needed for each conclusion and provide a small number of prioritised operating questions. It should not penalise a company for declining to disclose commercially sensitive details publicly; confidential participation and public evidence are different measurement channels.\r\n\r\nThis is a proposed assessment design, not a validated scale. Before claiming to benchmark organisations scientifically, Mayden needs to test question interpretation, scoring reliability, missing-data patterns and the relationship between measured dimensions and external outcomes. The public corpus provides useful material for that work, but it cannot substitute for it.\r\n\r\n## Reading the retail and consumer evidence exhibits\r\n\r\nThe heatmap shows nine dimensions. U means unknown; level 1 denotes a specific commitment or pilot, level 2 an implemented practice, and level 3 adds concrete operating detail. Each cell takes the highest supported, adjudicated level.\r\n\r\nPoints equal the supported total divided by 27 and multiplied by 100, rounded. The fixed denominator allows three points per dimension. Equal underlying totals share a position; records without qualifying evidence remain unranked. Unknowns add no documented points without proving absent capability. Positions describe this selected public corpus, not maturity, commercial success or national adoption.\r\n\r\nAnchor counts distinguish specific commitments or pilots, implemented practices, and implemented practices with operating detail. They do not count independent applications or companies: one replenishment workflow can support several dimensions. These are research-record counts, not estimates of operational AI coverage.\r\n\r\nSource dependence shows the largest points reduction when one source URL is removed. A sharp decrease identifies reliance on one disclosure, not loss of capability. A smaller decrease does not prove independent verification: URLs can repeat announcements. Historical cases, subsidiary boundaries and supplier accounts retain their claim-specific qualifications.\r\n\r\nThe enduring value of the Index will depend on disciplined updates. New evidence may strengthen a claim, reveal that a project stopped, or clarify that a widely promoted deployment had a narrower scope. Preserving the previous edition and recording the reason for each change will make those developments legible. That is how this research can become a useful continuing record of enterprise change while keeping its scientific claims proportional to the evidence.\r\n\r\n**Evidence and reproducibility.** The companion retail-evidence.json contains entity scopes, source relationships, passage locators, deployment stages and proposed evidence levels. Separate AI-assisted source challenge is retained separately and governs the released calculations. This edition uses public company and partner disclosures; it includes no private system access, respondent survey, statistical meta-analysis or validation of actual organisational maturity.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "healthcare-public-evidence", "title": "AI in Gulf healthcare: evidence at the point of care", "dek": "Clinical and administrative workflows, responsibility and the boundaries of deployment.", "kind": "Industry evidence review", "sector": "Healthcare", "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# Healthcare: the AI-native test is a change in care delivery\r\n\r\n*Mayden AI Native Index — exploratory public-evidence research. Cutoff: 10 October 2026. This is a purposeful sample of ten healthcare organisations, not a clinical-quality ranking or an estimate of national adoption.*\r\n\r\nThe meaningful question for a healthcare executive is whether AI changes how work gets done while preserving accountable clinical judgement. A hospital can acquire advanced equipment, sponsor conferences and announce technology partnerships without changing a routine workflow. Another can implement a narrow, well-supervised intervention that makes a real difference to documentation, screening or patient access. Those situations should not receive the same interpretation.\r\n\r\nOur reviewed material makes the distinction concrete. It contains operating accounts of AI-assisted imaging, clinical documentation, insurance administration and appointment support. It also contains pilots, future organisational plans, broad supplier promises and statements whose entity scope cannot be established. Mayden retains these differences in the evidence register. An attractive narrative is useful only if a reader can inspect the passage, understand whose operation it describes and see why the claim qualifies.\r\n\r\nThis edition examines Dr. Sulaiman Al Habib Medical Services Group and Dallah Healthcare in Saudi Arabia; M42 and PureHealth in the UAE; Hamad Medical Corporation and Primary Health Care Corporation in Qatar; Taiba Hospital and Kuwait Hospital in Kuwait; and Bahrain Specialist Hospital and Royal Bahrain Hospital in Bahrain. Selection was purposeful, with two organisations headquartered in each market. Qatar's two organisations are public providers and are labelled accordingly. PureHealth and M42 are integrated international groups. The sample therefore supports comparison of public evidence, not a contest between equivalent hospitals.\r\n\r\n## What the strongest disclosures explain\r\n\r\nHMG's 2025 report describes cardiac-imaging AI and an AI framework that preserves clinicians' final authority and includes fairness monitoring. These are separate questions: where AI assists care, and how its use is controlled. The report also identifies clinical documentation tools. A partnership described as established to support development and research remains a commitment where completed joint work is not demonstrated. Its evidence is narrower than its ambition. [HMG Annual Report 2025, PDF pages 39 and 51](https://hmg.com/ir/en/Lists/Reports_Publications/Attachments/111/HMG_Annual_Report_2025__English.pdf)\r\n\r\nDallah identifies Sahl ambient documentation, reporting use by more than 175 clinicians, and deployed Thakaa Med radiograph analysis. Its responsible-AI framework is described at a higher level than HMG's specific authority and monitoring account. A training paragraph sits in a forward-looking section and is treated as a planned commitment, not a completed workforce programme. This distinction survived a separate AI-assisted challenge of the initial research coding. [Dallah Annual Report 2025, PDF pages 24–26](https://www.dallahhealth.com/Library/Assets/Downloads/Dallah_Health_English_2025.pdf)\r\n\r\nM42 reports AIRIS-TB operating at 2,000 chest examinations daily. That issuer-reported throughput is separate from model evaluation. The original AIRIS-TB paper evaluates historical screening images; its estimated automation potential is not a measured reduction in routine clinical workload. Prospective workflow validation remains necessary. Executives should request both technical evaluation and evidence of what changed in care delivery. The GE HealthCare collaboration remains a forward-looking commitment. [M42 operating account](https://m42.ae/media-resources/news/m42-and-ge-healthcare-join-forces-to-advance-ai-enabled-patient-care/), [original study, accessible full text](https://pmc.ncbi.nlm.nih.gov/articles/PMC12241593/).\r\n\r\nPureHealth's report connects a shared AI infrastructure to particular operations. Daman's document processing is insurance administration; SEHA Clinics' retinal screening is clinical delivery; HHG's nurse scheduling and executive insights concern overseas operations. These cases belong in an explicitly consolidated group profile. They must not be silently converted into evidence that every UAE hospital uses the same systems. The physician-documentation pilot and proposed AI Office also retain their own statuses. [PureHealth Integrated Annual Report 2025, PDF pages 29, 31, 36 and 39–41](https://purehealth.ae/wp-content/uploads/2026/03/PureHealth_Integrated-Annual-Report_2025_EN.pdf)\r\n\r\nThese examples suggest a practical reporting standard: name the workflow, identify the operating entity, state whether use is live, describe the mechanism and explain the decision boundary. A list of technologies is less informative than a clear account of who does what differently. Executives can use that standard immediately, even before collecting additional quantitative measures.\r\n\r\n## Historical evidence and current claims need different treatment\r\n\r\nHMC provides unusually specific historical material. Its 2021–2022 precision-medicine report describes an imaging AI committee, internal educational sessions and integrated imaging trials. The committee has review and monitoring responsibilities. The Siemens and Koios applications are explicitly trials, even though integration into imaging systems is technically detailed. We retain the pilot cap and label the committee evidence historical. The document cannot confirm that the same arrangements remain active in October 2026. [HMC Precision Medicine Annual Report, PDF page 39](https://www.hamad.qa/EN/Hospitals-and-services/CPMGC/Annual-Report/Documents/Precision%20Medicine%20Annual%20Report%202021-2022.pdf)\r\n\r\nPHCC's March 2025 account says AI was integrated into its 2024 screening programme to assist image interpretation. That supports an implemented practice. It does not identify enough about the model, coverage or evaluation to support a more detailed operating anchor. Its annual report's conference discussion is not automatically an internal workforce-development programme. [PHCC screening account](https://www.phcc.gov.qa/media/news/2025/03/10/we-are-working-on-introducing-mandatory-annual-oral-screening-for-students---dr.-hamad-al-mudhahka), [PHCC Annual Report 2024](https://www.phcc.gov.qa/docs/default-source/default-document-library/phcc%27s-annual-report-2024-eng-final.pdf)\r\n\r\nAge matters because technology and organisations change. A programme may be replaced, expanded, discontinued or absorbed into another service. Historical evidence can still explain an organisation's development and inform follow-up questions. It should not acquire a false current date because a researcher accessed the webpage recently. Publication date, reporting period, observation date and research access date are distinct fields.\r\n\r\nFor a future recurring edition, The next research cycle should ask organisations to reconfirm older operating claims with dated implementation records. The correct question is not simply whether the old claim remains on the website. It is whether the operation, accountable owner, user population and control arrangement remain materially the same. Changes should create a documented update rather than quietly rewriting the historical record.\r\n\r\n## What the gaps actually mean\r\n\r\nThe Kuwait and Bahrain searches illustrate why an unknown result needs a visible explanation. Taiba's indexed news points to a doctor's AI-assisted endoscopy demonstration at an overseas conference. The full page could not be recovered through the reviewed access route. Neither an inaccessible passage nor an overseas demonstration establishes routine use at the Kuwait hospital. Kuwait Hospital's official identity and service inventory were checked, but the bounded search recovered no qualifying AI operating claim. This is a research limit, not a judgement about clinical capability. [Taiba source lead, excluded](https://www.taibahospital.com/taiba-hospital-proudly-recognizes-dr-asmaa-al-kandaris-achievement-in-a-global-gastrointestinal-endoscopy-conference/), [Kuwait Hospital](https://kuwaithospital.com.kw/)\r\n\r\nBahrain Specialist Hospital has a named IT director's testimonial on Voiceoc's site describing conversational AI in appointment and call support, including a reported share of traffic. This is direct implementation-participant evidence, but supplier-hosted and undated. General performance claims elsewhere on the supplier page are not hospital results. Royal Bahrain Hospital's MRI page includes a broader KIMSHEALTH FAQ mentioning AI diagnostics; that template does not identify the Bahrain hospital's own implementation. It remains excluded from local scoring. [BSH testimonial](https://customerexperience.voiceoc.com/), [Royal Bahrain Hospital FAQ](https://www.royalbahrainhospital.com/procedures/magnetic-resonance-imaging-mri)\r\n\r\nA public-evidence index will partly reflect disclosure capacity. Large listed groups produce extensive reports and have communications resources. Smaller private providers may operate effective systems without describing them publicly. Replacing quiet organisations with easier-to-score ones would make the cohort look more impressive while weakening its integrity. The better response is to preserve the selected entities, publish the search boundary and invite evidence through a standard verification process.\r\n\r\n## Nine operating questions for healthcare leaders\r\n\r\n**AI in core work.** Which recurring clinical or administrative workflow uses AI today? Identify the service, facility, operating owner and eligible population. Distinguish assistance from a system that completes a task. Describe the ordinary workflow after implementation, including what happens when the tool is unavailable. This makes the answer testable without requiring proprietary model code.\r\n\r\n**Workforce.** Which roles have changed, and how is competence checked? Training attendance alone does not establish effective use. Ask whether clinicians and administrative staff understand limitations, escalation and correction. Separate staff training from AI used to allocate staff: both concern work, but they are different organisational practices and need different evidence.\r\n\r\n**Ecosystem.** What does the organisation do with partners beyond signing an agreement? Useful evidence identifies implementation responsibilities, integration work, support arrangements and who controls changes. A partnership announcement can establish a specific commitment. Continued delivery requires a more substantive account, especially when a vendor's software is embedded in a consequential care workflow.\r\n\r\n**Interfaces.** Where does a patient or employee encounter AI, and how is the interaction connected to the underlying service? An appointment assistant should be assessed through completed service transactions, unresolved requests and human escalation. A documentation assistant should be assessed through review and correction in the record workflow. Conversation volume alone does not explain whether work was completed reliably.\r\n\r\n**Lineage.** Can an organisation reconstruct which information, model version and instruction produced an important output? Generic electronic records and cybersecurity controls do not answer this question. The interview should seek a concrete example of tracing an AI-assisted decision or document back through its inputs and changes, with privacy-preserving evidence suitable for review by a named human assessor.\r\n\r\n**Experimentation.** How are new tools evaluated before routine adoption and after material change? A pilot should have a stated question, target population, comparison approach, acceptance criteria and stopping rule. Detailed integration is not a substitute for evaluation. Equally, a completed evaluation should not be confused with proof of general clinical benefit across settings.\r\n\r\n**Autonomy.** What can the system do without a person's approval? This should be answered task by task, with explicit limits, override mechanisms and responsibility. Automatically drafting text differs from submitting a claim, changing a schedule or influencing a clinical action. The label “agentic” does not supply these missing details and should not determine an anchor.\r\n\r\n**Knowledge.** How does the organisation turn AI-assisted information into reusable operational learning? Examples could include evaluated retrieval, reviewed summaries or structured feedback into a clinical protocol. A dashboard described as AI-enabled establishes less than a documented learning loop. Ask who checks the content and how errors alter future practice.\r\n\r\n**Governance.** Who may approve, pause or retire a system, and what evidence informs that decision? Policies become more informative when they specify authority and operating routines. Ask for an example of a model change, an incident review or a rejected use case. This reveals whether oversight is part of ordinary management rather than a document created for external communication.\r\n\r\nThese questions are a proposed diagnostic instrument, not validated survey scales. The company study must test clarity with clinical, operational and technical respondents before treating answers as comparable measurement. Some concepts require multiple informants. A chief executive may know strategic ownership while a clinical lead knows exception handling and a technical owner knows model versioning. Disagreement can be useful evidence of an incomplete operating model.\r\n\r\n## Measurement that executives can trust\r\n\r\nThe public-evidence layer uses discrete anchors. A specific named commitment or pilot can establish level 1. An explicitly implemented internal practice can establish level 2. Implementation with a concrete mechanism, coverage, evaluation or authority can establish level 3, subject to a separate AI-assisted source challenge. Unknown means that the bounded search found no qualifying evidence. It does not mean that the organisation lacks the practice.\r\n\r\nThe aggregate must remain a summary of documentation across the nine dimensions. It is not the probability that an organisation is AI-native, a clinical-safety grade or a measure of patient benefit. Repeating the same case in several press releases must not increase its score. The maximum supported anchor within a dimension is retained, and the evidence reader should show the contributing claim rather than a mysterious total.\r\n\r\nOutcome claims need a separate ledger. A reported reduction in documentation time, annual document throughput, screening discrimination and appointment traffic are not interchangeable benefits. Each has a different denominator and study design. Before comparing results, record the baseline, time period, sample, exclusions and relevant operating conditions. Where those fields are missing, label the result as an attributed organisational claim. Do not average unlike percentages into a sector impact statistic.\r\n\r\nA stronger proprietary assessment would add submitted evidence to the public record while preserving provenance. Mayden could request a workflow map, redacted approval record, role-specific adoption figures and a description of evaluation and monitoring. The organisation should approve only the factual accuracy and disclosure permissions of its submitted material, not its eventual score. A separate reviewer should decide whether the evidence meets the published anchor.\r\n\r\nThe first pilot should test respondent burden as well as measurement quality. Start with an executive interview, route detailed questions to the appropriate owners and ask for a small number of useful artefacts. A long questionnaire filled with hypothetical best practice can reward confident language rather than real capability. A shorter interview that follows one implemented workflow end to end can uncover responsibility, exceptions and learning much more clearly.\r\n\r\n## The reporting opportunity\r\n\r\nThe media opportunity is the gap between visible applications and visible operating systems, illustrated through scoped cases and unanswered questions. Two unlike providers cannot establish a country winner. Unknown entries do not mean lagging capability, and international groups do not represent national health systems.\r\n\r\nThe heatmap shows documented evidence and unknowns. Points summarise that profile under the declared rules; they do not measure clinical quality or enterprise maturity. The evidence-anchor chart separates commitments and pilots from implementation and operating detail, counting claims rather than independent systems. Source-URL removal shows the effect of omitting the most influential retained URL: a large change signals concentrated documentation, not weak capability. None establishes national adoption or patient benefit.\r\n\r\nParticipants should receive an evidence profile, unanswered operating questions and prioritised verification gaps. Repeated editions can track commitments becoming operations, stopped pilots, improved controls and better-evidenced outcomes. Consistent definitions and traceable revisions make that comparison useful.\r\n\r\n**Review transparency.** Extraction and source challenge were AI-assisted, without human expert sign-off or independent assurance.\r\n" }, { "slug": "realestate-public-evidence", "title": "AI in Gulf real estate: beyond the smart-city promise", "dek": "Property, construction and asset operations across ten selected entities.", "kind": "Industry evidence review", "sector": "Real Estate & Construction", "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# Real Estate & Construction: intelligence must survive contact with operations\r\n\r\n*Mayden AI Native Index — exploratory public-evidence research. Cutoff: 10 October 2026. Ten purposefully selected developer and owner-operator groups across five GCC markets. This edition measures documented organisational practice, not investment quality, building safety or national AI maturity.*\r\n\r\nA developer becomes more interesting as an AI enterprise when intelligence changes a recurring decision: how a design is assessed, a construction discrepancy is detected, an asset is maintained or a service request is resolved. A smart-city label does not answer those questions. Neither does a digital twin, a technology partnership or an innovation award on its own. The evidence becomes useful when it identifies the operation, the responsible entity and the mechanism that connects information to action.\r\n\r\nThis sector presents an unusually difficult measurement problem. A developer may own hotels, operate a district, manage schools, invest in a cooling company and commission construction from contractors. AI used in any one of those activities cannot automatically be attributed to every other activity. A group profile can legitimately include several businesses, but it must preserve where each claim originates. Without that discipline, size and corporate complexity can masquerade as enterprise-wide adoption.\r\n\r\nThe purposeful cohort includes ROSHN Group and Red Sea Global in Saudi Arabia; Emaar Properties and Aldar Properties in the UAE; Qatari Diar and United Development Company in Qatar; Mabanee and Tamdeen Group in Kuwait; and Diyar Al Muharraq and Naseej in Bahrain. These are primarily developers and asset owner-operators, not a matched sample of general contractors. There are two headquarters per market, but no basis for estimating country adoption or ranking national ecosystems from this selection.\r\n\r\n## Five operating patterns worth examining\r\n\r\nRed Sea Global offers a specific example in environmental operations. Its marine director describes using CoralNet to analyse recurring coral surveys, training the model with human-labelled images and checking its performance against skilled analysts. That is more informative than a general claim about sustainable technology. It identifies a working process and an evaluation step. It remains a marine-monitoring case, not evidence that the entire development enterprise has become AI-native. [RSG implementation account](https://www.redseaglobal.com/en/media-center/blog/innovation-meets-preservation-technology-s-impact-on-red-sea-biodiversity-conservation/)\r\n\r\nEmaar's case is operationally narrow and historically older. Winnow's 2019 account describes AI-enabled food-waste recognition in hospitality kitchens, using images and scales to inform purchasing and production choices. The source discusses installation of waste technology across properties, but that total should not be read as a proven count of AI Vision installations. The record does not independently confirm continuous operation through October 2026. [Winnow's Emaar implementation account](https://www.winnowsolutions.com/resources/news/emaar-hospitality-group-first-to-bring-cutting-edge-technology-into-uae-kitchen)\r\n\r\nAldar's innovation report supplies several distinct internal mechanisms: specialist teams, security automation, construction-partner feedback and payment interfaces. It also labels an ESG chatbot beta and puts autonomous asset agents in a future section. Those distinctions matter as much as the positive cases. [Aldar Innovation Report 2025, pages 10, 14, 16–18, 29 and 37](https://app-aprp01saasa21mtp001.cms.optimizely.com/globalassets/aldar-innovation-report-2025---eng---final-09-april.pdf)\r\n\r\nQatari Diar's evidence is scoped to Lusail through its development subsidiary. ST Engineering's contract announcement describes a programme scheduled through 2027; a later, undated implementation account describes deployed city operations and an operator assistant. Both belong in the record. The later account supports documented implementation, while the contract prevents a careless claim that every contractual deliverable is complete. Supplier-reported deployment remains supplier-reported. [2024 contract](https://www.stengg.com/en/newsroom/news-releases/st-engineering-drive-ai-powered-smart-city-transformation), [Lusail operating account](https://www.stengg.com/en/innovation/artificial-intelligence/how-ai-is-powering-the-next-generation-of-smart-cities/)\r\n\r\nUDC's November 2025 announcement describes an AI assistant connected to facilities services and its resident platform. The useful evidence is the workflow description, not the awards. Its annual report separately describes AI initiatives at Qatar Cool, identifies a 51% stake and places the company in a section headed Major Associates. That related-investee case is excluded from UDC points: the majority stake alone does not establish controlled or consolidated operating scope. [UDC implementation account](https://udcqatar.com/Media-Room/Press-Releases/Middle-East-Enterprise-AI), [UDC Annual Report 2025, pages 66–67](https://udcqatar.com/-/media/28448e61b207417e9756f8027195f1a5.ashx)\r\n\r\nTogether, these cases suggest that AI can enter the development enterprise through many doors. Environmental monitoring, hospitality operations, construction delivery and resident services may develop at different speeds. A single narrative about “the AI-powered developer” conceals that variation. A dimension profile and a scoped case portfolio are more useful to an executive than a score without its supporting operating detail.\r\n\r\n## Commitments, products and missing evidence\r\n\r\nROSHN's hackathon provides evidence of a specific AI-inclusive innovation programme with named partners and opportunities for pilots. It does not establish that the submitted solutions became routine operating systems. An ecosystem commitment and a completed implementation deserve different anchors. The number of submissions or size of the prize pool should not become a proxy for organisational capability. [ROSHN Hackathon 2025 announcement](https://www.roshn.sa/news-and-events/press-releases/article-78)\r\n\r\nMabanee's 2025 annual report was recovered and screened, including its operational and governance material. The bounded search did not identify a qualifying AI claim in that document. Its sustainability-report register was accessible, but the linked 2025 sustainability PDF was not successfully recovered during the research window. Those are distinct observations: one document was inspected, another remains an access gap. Neither supports a conclusion that Mabanee lacks AI capability. [Mabanee Annual Report 2025](https://www.mabanee.com/storage/storageannual-reports/annual-report-2025-eng-2070077368.pdf), [sustainability report register](https://www.mabanee.com/en/sustainability/sustainability-reports)\r\n\r\nTamdeen's Planet Laser announcement describes AI-enhanced gameplay in an entertainment offering. The article does not establish a developer's internal AI operating process, and its opening language is mixed with a forthcoming launch date. Diyar Al Muharraq's summit sponsorship includes AI in the event agenda, while the implemented city measures described are smart meters, efficient lighting and solar installations. Those technologies should not be relabelled AI. [Tamdeen announcement](https://www.tamdeen.com/news/tamdeen-entertainment-unveils-planet-laser-by-fun-tiki-at-al-khiran-mall), [Diyar announcement](https://www.diyar.bh/en/press/97/diyar-al-muharraq-official-platinum-sponsor-for-the-bahrain-smart-cities-summit-2024)\r\n\r\nNaseej's 2025 strategy announcement discusses growth and innovation without a specific operating AI commitment. Search results for similarly named Saudi technology businesses are especially hazardous: they describe a different organisation. The Bahrain developer remains in the cohort with unknown dimensions where qualifying evidence was not recovered. [Naseej strategy announcement](https://www.naseej.bh/our-news/naseej-holds-annual-general-meeting-and-announces-new-strategy)\r\n\r\nKeeping these firms visible is part of a credible benchmark. A research team could easily make an index look stronger by replacing quieter firms with companies that publish more AI content. That would change the selection process after seeing the results. Mayden's better approach is to disclose the boundary, preserve the unknowns and offer a consistent route for organisations to supply further evidence.\r\n\r\n## The enterprise boundary is a measurement decision\r\n\r\nBefore an interview begins, the organisation should confirm exactly what is being assessed. Is it the listed parent, a consolidated group, a property-management subsidiary or one development? Which operating partners are within scope? Who owns the decision being described? These questions are not administrative details. They determine whether two observations concern the same kind of enterprise.\r\n\r\nFor a contractor-delivered project, the developer may benefit from AI without owning the capability. That can still be relevant ecosystem evidence if the arrangement changes the developer's work and the responsibilities are documented. It is less persuasive as evidence of an internal workforce or knowledge capability. An outsourced inspection service does not establish that the developer can reproduce, evaluate or govern its outputs independently.\r\n\r\nOwnership percentages also need careful interpretation. They help describe the economic relationship but do not by themselves establish operational control. Where a source labels an investee differently from the researcher's initial assumption, the discrepancy should remain visible for adjudication. Company-confirmed scope and financial consolidation notes can resolve it in a proprietary assessment. The researcher should not silently choose the interpretation that produces more points.\r\n\r\nHeadquarters country is similarly limited. A Kuwait-headquartered developer may operate assets elsewhere; a UAE group may hold international businesses. Country filters should identify headquarters, with operating geography shown separately. Aggregating the resulting profiles into national performance claims would require a different sample design, appropriate denominators and a clearly defined population.\r\n\r\n## Nine questions that reveal the operating model\r\n\r\n**AI in core work.** Which recurring development or asset decision has changed? Follow one workflow from input to decision and execution. A strong answer names the project or asset population, the responsible team and the system's actual contribution. It also explains what remains manual. Ask for a recent example rather than a list of software purchased.\r\n\r\n**Workforce.** Who performs work differently because of AI? Embedded specialists, trained business users and changed approval roles are different forms of evidence. Ask which roles can evaluate an output, challenge it and work safely when a tool fails. Training hours are useful only when the relevant population and expected competence are clear.\r\n\r\n**Ecosystem.** How do suppliers, contractors and internal teams learn together? A meaningful partnership account identifies data exchanges, integration responsibilities, acceptance criteria and a route for operational feedback. A technology vendor's capability statement is not evidence that the developer uses all of those capabilities. Implementation contracts can establish commitments; operating records establish delivery.\r\n\r\n**Interfaces.** What can employees, residents or contractors actually complete through an AI interaction? Separate answering a question from initiating a maintenance job, changing a booking or paying a charge. Track where the interaction transfers to a person and whether the transaction reaches the system of record. Attractive demonstrations should not substitute for ordinary service performance.\r\n\r\n**Lineage.** Can a project decision be reconstructed from its inputs, model version and approvals? In construction, this could involve knowing which design revision and site scan informed a discrepancy. In leasing, it could involve the information underlying a recommendation. A digital twin or central data platform does not automatically provide this traceability. Ask for an example that follows the complete chain.\r\n\r\n**Experimentation.** How does the firm decide whether to expand, revise or stop a use case? Define the comparison, affected process, acceptance criteria and consequences of error. A pilot that exposes limitations can be organisationally valuable even if it never scales. The public-evidence anchor nevertheless remains a pilot anchor until routine implementation is demonstrated.\r\n\r\n**Autonomy.** Which actions can a system take without approval? Automated detection, suggested responses and completed changes are distinct. Ask who may change permissions, how exceptional conditions are handled and whether the action can be reversed. The word “agent” is not a substitute for an authority map, particularly when the system affects access, payments or physical infrastructure.\r\n\r\n**Knowledge.** How do insights become reusable organisational learning? A useful answer connects analysis to a decision and then to observed results. It explains who checks the output and how corrections influence future work. An AI-generated report can improve access to information, but a learning system requires a feedback process that survives changes in staff and suppliers.\r\n\r\n**Governance.** Who approves deployment and who can stop it? General corporate governance, cybersecurity and ESG reporting are important contexts, but they are not automatically AI governance. Seek the operating owner, risk review, model-change approval and incident process. Evidence of one carefully governed workflow can be more revealing than an organisation-wide policy without implementation detail.\r\n\r\nThe questions should be tested with different respondents before being used as a validated instrument. Development directors, asset managers, technology leaders and risk owners see different parts of the operation. A structured interview should route questions to the people who can substantiate the answers. Divergent answers may reveal an unclear responsibility boundary rather than a respondent error.\r\n\r\n## Outcomes need a denominator and a counterfactual\r\n\r\nA source may report shorter design cycles, automated security hours, reduced waste or faster responses. Those measures have different meanings. Some describe throughput, some estimates of avoided effort, some predictions and some realised financial effects. The evidence register preserves the original category. A projected saving should never be displayed as an achieved result, and a technology's design capacity should never become its active deployment count.\r\n\r\nFor construction, a useful evaluation identifies the project phase, baseline method, inspected elements, detected issues and follow-through. If a system flags a potential defect, the resulting value depends on review, correction and avoided rework. Counting flags alone can reward noise. Comparing the same operating process before and after introduction is a start, but changes in project mix and delivery conditions still need consideration.\r\n\r\nFor facilities and resident services, transaction completion and exception handling are more informative than interaction volume alone. A busy assistant may be popular, confused or handling repeated unresolved requests. Track the unit of work, repeat contacts, human escalation and verified resolution. The right measures depend on the service and should be established before promotional claims are produced.\r\n\r\nFor environmental applications, better measurement is not identical to better environmental outcomes. An image-analysis tool can improve monitoring capacity without demonstrating an ecological improvement. The evidence chain must show how monitoring informed management and what happened afterwards. This distinction makes a report more useful: it separates a credible operational contribution from a much broader claim that has not yet been tested.\r\n\r\n## How the public benchmark should be presented\r\n\r\nThe released profile should retain the nine discrete anchors and show unknown explicitly. A named commitment or pilot supports level 1; an implemented internal practice supports level 2; an implemented practice with a concrete mechanism, coverage, evaluation or authority can support level 3 after a separate AI-assisted source challenge. Duplicate announcements do not accumulate points. The total is a summary of documented evidence, not a percentage of enterprise maturity.\r\n\r\nAn evidence reader should let an executive inspect the company boundary, operating entity, source date, access status and coding rationale. A full mirror must be labelled as a mirror, as with the recovered Aldar annual report. Failed original links should remain in the provenance record. An accessible homepage reached through a broken PDF link is not the missing annual report. [Aldar Annual Report 2025, readable mirror](https://financialfilings.com/filings/aldar-properties-pjsc/annual-report/2026/48374610/)\r\n\r\nRead the delivered exhibits as complementary views of the same bounded record. The heatmap locates documented themes and unknowns; the points chart summarises those anchors without measuring development quality or enterprise maturity. The evidence-anchor chart distinguishes commitments and pilots from implemented practices and operating detail. Its bars count claims, so one underlying system can contribute more than one record. The source-URL-removal chart tests concentration by omitting the most influential retained URL. A lower resulting total indicates dependence on that disclosure, not a measured decline in capability. Repeated accounts can share an origin, so remaining URLs do not establish independent corroboration.\r\n\r\nFor press, the defensible story is that documented intelligence appears in specific operating processes while evidence of enterprise-wide coordination remains uneven. That is a hypothesis generated by this purposeful cohort, not a representative sector prevalence estimate. Editors should be able to follow each illustrative claim to a readable source and understand why a visually impressive counterexample was excluded.\r\n\r\nFor participating companies, the next step is a focused verification exercise. Select one important workflow, identify its owner, collect a redacted operating map, clarify authority and document how success is evaluated. Then examine whether the same organisational routines are shared across other use cases. This can turn a public-evidence profile into a valuable conversation about the enterprise without pretending that a website search has already answered every question.\r\n\r\nA durable index will reward clearer evidence and better operating practice through consistent rules. It will also record stopped pilots, revised claims and genuine uncertainty. Over repeated editions, that history can become more valuable than a single ranking: it shows how firms move from a technology announcement to an accountable capability that continues to work across projects, assets and teams.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "state-of-ai-native-enterprise", "title": "The AI-native enterprise: an operating evidence atlas", "dek": "Eight sectors. Five markets. A closer examination of how AI enters recurring work.", "kind": "Measurement research", "sector": null, "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# The state of the AI-native enterprise\r\n## Follow the decision. Measure the operating change.\r\n\r\n*Mayden research perspective for the eight-sector evidence edition · October 2026*\r\n\r\nAn enterprise becomes meaningfully different when AI changes how work moves, who makes decisions and how the organisation learns from results. Buying access to a model can begin that change. A convincing demonstration can make it imaginable. Neither tells a board whether the business now operates differently.\r\n\r\nMayden proposes a practical definition: an AI-native enterprise repeatedly combines AI, people, data and decision authority inside its operating processes, with measurable results and a working capacity to correct failure. This is a research proposition to test, not a validated threshold that companies have already crossed.\r\n\r\nThe immediate executive question is concrete: **Which important decision works differently today, and what evidence would persuade someone outside the project team?** Answering it connects technology investment to business operations. It also exposes the difference between a useful isolated application and an organisational capability that survives a change of model, supplier or leadership.\r\n\r\nThis edition approaches that question across banking, telecommunications, energy and utilities, logistics and aviation, manufacturing and materials, retail and consumer businesses, healthcare, and real estate and construction. Its public evidence is a starting point for inquiry. It cannot observe every internal practice, and it does not estimate regional adoption or certify the most capable enterprises.\r\n\r\n## Exhibit 1 — Five questions between an AI demonstration and an enterprise result\r\n\r\n| Evidence layer | Executive question | Evidence that moves the discussion forward |\r\n|---|---|---|\r\n| Task capability | Can the system do the defined job? | Representative tests, quality criteria and failure cases |\r\n| Operating use | Does it handle real work? | Production dates, eligible workload and actual usage |\r\n| Decision authority | What may it decide or change? | Permissions, accountable owner and escalation rules |\r\n| Business consequence | Did the relevant outcome improve? | Baseline, comparison, full costs and observation period |\r\n| Organisational learning | Can the enterprise detect and correct deterioration? | Monitoring, incidents, changed procedures and retesting |\r\n\r\n*Mayden analytical framework. These are different evidence questions, not five measured maturity bands.*\r\n\r\nA board can use this sequence without waiting for a perfect enterprise score. Choose a workflow, ask for the evidence at each layer, and note the first unanswered question. That point often identifies the next useful investment: better data, a clearer owner, broader rollout, or a more credible evaluation.\r\n\r\n## 1. A capable model does not guarantee a capable workflow\r\n\r\nThe central unit of investigation should be a recurring business process with a clear beginning and end. “Our staff use AI” leaves too much unspecified. “The system proposes a replenishment order; a planner checks exceptions; an approved order updates inventory” describes work that can be inspected.\r\n\r\nResearch illustrates why this distinction matters. In a randomised study of 50 US physicians, chatbot access did not significantly improve the primary diagnostic-reasoning score on six clinical vignettes. The study concerns a bounded test setting, not patient outcomes, but it shows why performance of the combined workflow deserves its own evaluation. [Goh and colleagues, JAMA Network Open](https://doi.org/10.1001/jamanetworkopen.2024.40969).\r\n\r\nTask benchmarks answer another useful question. GDPval evaluates professional work products across selected occupations, while explicitly limiting claims about tacit context and interactive work. It offers ideas for realistic test materials; it does not measure whether a named enterprise has integrated AI successfully. [GDPval, version 1](https://arxiv.org/html/2510.04374v1).\r\n\r\nFor an executive, the operating test has four parts. Identify the eligible work, observe the human and system together, include the exceptions, and examine the final accepted output. An impressive draft that requires extensive repair is different from an accepted result. A fast decision that creates downstream rework may move cost rather than remove it.\r\n\r\nThe same logic applies to governance. A policy is useful evidence of intent. An operating control has a trigger, a responsible person and a consequence. The relevant question becomes: **Show one occasion when this control changed what the system or an employee did.**\r\n\r\n## 2. Eight sectors need different proof\r\n\r\nA common measurement backbone makes comparisons understandable. Sector-specific operating questions make them useful. A bank and an industrial operator may both need reliable data, accountable owners and trained staff; their material decisions and failure costs differ.\r\n\r\n## Exhibit 2 — Where to begin the operating interview\r\n\r\n| Sector | Start with this workflow | Ask for this denominator | Examine this exception |\r\n|---|---|---|---|\r\n| Banking | Fraud alert investigation or service resolution | Eligible alerts or cases, by type | Legitimate customer incorrectly blocked |\r\n| Telecommunications | Network fault detection and resolution | Covered sites, incidents or service lines | Escalation during a severe outage |\r\n| Energy and utilities | Equipment maintenance or process optimisation | Eligible assets and operating hours | Recommendation outside safe operating limits |\r\n| Logistics and aviation | Routing, disruption response or cargo handling | Eligible journeys, shipments or disruption cases | Late data or an infeasible alternative |\r\n| Manufacturing and materials | Defect inspection or production scheduling | Products, lines and inspected units | Unseen defect or changed material |\r\n| Retail and consumer | Forecasting, replenishment or returns | Eligible products, stores or transactions | Promotion, stockout or disputed refund |\r\n| Healthcare | Documentation, scheduling or clinical support | Encounters and sites within the approved scope | Missing information or disputed recommendation |\r\n| Real estate and construction | Design review, site monitoring or asset operations | Projects, buildings or inspected work packages | Conflicting design versions or unsafe site condition |\r\n\r\n*Illustrative evaluation agenda developed by Mayden; no row asserts a measured sector result or recommends autonomous clinical or safety decisions.*\r\n\r\nBegin with one process that matters economically or operationally, then ask whether the practice travels. A deployment at one flagship site may be valuable without being enterprise-wide. A parent group may announce a programme that reaches only one subsidiary. A technology service sold to customers may demonstrate commercial capability without showing the supplier's own internal transformation.\r\n\r\nThose distinctions should appear beside company findings. Otherwise, readers can mistake a broad corporate name for a broad deployment.\r\n\r\nIndustry surveys also require careful scope. RICS separates pilot activity from regular process use in its construction survey; its respondents are professionals, not a census of enterprises. Our local evidence collection needs similarly precise units. [RICS construction research](https://www.rics.org/news-insights/artificial-intelligence-in-construction-report).\r\n\r\n## 3. Measure the value that survives the whole workflow\r\n\r\nTime saved is an intermediate result. It can improve response speed, increase output, reduce strain or create room for better work. Translating it into financial value requires another observation: what happened to the released capacity?\r\n\r\nIn its Copilot evaluation, the UK Department for Work and Pensions reports user benefits alongside verification overhead. The evaluation relies on self-reported outcomes, nonrandom licence allocation and no pre-trial baseline. Its methodological candour is a useful model for separating a promising experience from a causal estimate. [DWP evaluation](https://www.gov.uk/government/publications/an-evaluation-of-dwps-microsoft-copilot-365-trial/an-evaluation-of-dwps-microsoft-365-copilot-trial).\r\n\r\nA Danish study linking adoption surveys to administrative records likewise distinguishes changes in work from recorded labour-market outcomes. Its March 2026 revision finds substantial task reorganisation alongside earnings and hours estimates near zero over the observed period. Different outcome layers can move at different speeds. [Humlum and Vestergaard](https://www.nber.org/system/files/working_papers/w33777/w33777.pdf).\r\n\r\n## Exhibit 3 — A value account with separate, auditable lines\r\n\r\n| Line in the account | What to record |\r\n|---|---|\r\n| Gross task effect | Time, quality, throughput or loss change on the defined task |\r\n| Additional work | Review, correction, escalation, integration and training effort |\r\n| Operating cost | Model use, software, infrastructure, support and monitoring |\r\n| Capacity use | More output, shorter queues, different work or reduced paid hours |\r\n| Business outcome | Margin, service, reliability or another chosen outcome |\r\n| Attribution limits | Demand changes, staffing changes and other concurrent initiatives |\r\n\r\n*Accounting structure, not a forecast. Do not add unlike units or convert every saved minute into cash.*\r\n\r\nThe chief financial officer and process owner should agree on the last two lines before a pilot starts. Choose one primary outcome and a small set of guardrails. Record the baseline and comparison design. Preserve unsuccessful runs. Where randomisation is practical, use it; where it is not, make the identification limits visible.\r\n\r\nA programme can be worth continuing before it generates a proven financial return. The investment decision becomes stronger when the organisation can say what it has learned, what remains uncertain and which next observation will resolve the uncertainty.\r\n\r\n## 4. Agentic systems make authority a measurement problem\r\n\r\nAn agent that drafts a recommendation and an agent that changes a customer record may use the same underlying model. Their operating implications differ. Executives need a map of permitted actions, dependencies and recovery routes.\r\n\r\nProvider telemetry reinforces the need to examine deployment context. Anthropic's original agent-use study distinguishes individual API tool calls from complete sessions and acknowledges that observed traffic can include evaluations. Its findings are evidence about one provider's activity, not a count of autonomous enterprises. [Anthropic research](https://www.anthropic.com/research/measuring-agent-autonomy).\r\n\r\nInteractive benchmarks offer another lesson. Tau-bench tests repeated task completion and notes that a correct final database state can still conceal a policy violation. Success therefore needs more than one indicator. [Tau-bench, version 1](https://arxiv.org/html/2406.12045v1).\r\n\r\n## Exhibit 4 — Put scrutiny at the point of consequence\r\n\r\n| Action | Evidence to request |\r\n|---|---|\r\n| Read or retrieve | Allowed sources, access boundaries and stale-data handling |\r\n| Recommend | Acceptance criteria, reviewer competence and rejection records |\r\n| Change a record | Authorisation, validation and an attributable change log |\r\n| Commit resources | Limits, approvals, reconciliation and exception ownership |\r\n| Affect a person or physical process | Domain-specific controls, intervention route and tested recovery |\r\n\r\n*Illustrative action categories; greater autonomy is not automatically a better operating design.*\r\n\r\nAsk the process owner to walk through one successful case and one failure. Where did the system stop? Who noticed? Could the affected state be restored? Did the team repair only the immediate result, or change the process that produced it?\r\n\r\nEvaluation itself needs inspection. The SABER authors identify annotation problems in an established agent benchmark. Their results table also includes cases where the proposed safeguard lowers performance, despite broader positive wording in the narrative. The lesson for Mayden is to retain exceptions and check the underlying comparison. [SABER, version 1, sections 5–8](https://arxiv.org/html/2512.07850v1).\r\n\r\n## 5. Workforce evidence should describe changed work\r\n\r\nTraining participation is an input. It does not establish whether someone can judge a difficult output, handle an exception or redesign a process. A workforce assessment should distinguish access, practice, competence and responsibility.\r\n\r\nA useful interview asks an operator to describe the last task completed with AI, show how the output was checked and explain what happens when the answer is uncertain. A manager should be able to describe how roles or workload changed. A learning lead should be able to show how the training responds to observed mistakes.\r\n\r\nThe OECD's original employer and worker surveys demonstrate the value of sector-specific wording and more than one organisational perspective. They also predate mass generative-AI adoption, so their reported attitudes are historical evidence rather than current regional norms. [OECD workplace study](https://doi.org/10.1787/ea0a0fe1-en).\r\n\r\nFor Mayden's future GCC fieldwork, bilingual interviews should test whether terms such as deployment, approval and active use mean the same thing to respondents. The English label alone is not a stable measure. Interviewers should offer equivalent examples, allow “I do not know”, and record whether the respondent speaks for a team, site, business unit or group.\r\n\r\nThe report back to a company should identify a next action its managers can own: an exception-handling exercise, a better usage denominator, a revised approval route or a repeatable evaluation. That makes participation valuable before the broader benchmark has statistical norms.\r\n\r\n## 6. Read the public index as an evidence map\r\n\r\nMayden's public edition records the strength of located documentation across nine dimensions. The highest accepted anchor in each dimension contributes to a fixed-denominator score. Unknown evidence contributes no documented points, while remaining explicitly unknown. The result is a comparison of documented evidence in the research corpus.\r\n\r\nIt is not a percentage of enterprise transformation. A score twice as large does not mean twice the capability. A sophisticated private company may disclose little. A highly communicative group may provide detailed examples from a narrow operating area. Both possibilities matter when reading a ranking.\r\n\r\n## Exhibit 5 — Three kinds of coverage that must stay separate\r\n\r\n| Coverage | What it answers | What it cannot establish |\r\n|---|---|---|\r\n| Search coverage | Which routes, languages and document types were checked? | That all relevant information was found |\r\n| Evidence coverage | In how many dimensions was qualifying documentation located? | That the practices cover the whole organisation |\r\n| Operating coverage | What share of the eligible business uses the practice? | Its quality or causal business impact |\r\n\r\n*Mayden measurement distinction. A source can support one coverage statement without supporting the others.*\r\n\r\nReaders should be able to open a company result and follow the score to its contributing claim, source and review decision. If a source is a supplier case study, that origin should remain visible. Several pages repeating one announcement do not become several independent confirmations.\r\n\r\nSensitivity views help readers see dependence on choices: remove commitment-only evidence, cap detailed anchors, or vary one dimension's weight. These are alternative assumptions applied to the same corpus. They are not confidence intervals, and stability across them cannot validate the underlying construct.\r\n\r\n## 7. The next research step is a company-level learning loop\r\n\r\nThe public edition should lead into voluntary, evidence-backed company research. The two products should retain separate identities: public documentation provides an external view; participating organisations can supply operational evidence unavailable to an outside reader.\r\n\r\nStart the interview with one material workflow and follow it deeply. Then broaden to the organisational arrangements supporting it: sponsorship, data access, skills, decision rights, evaluation and improvement. Ask for examples before asking respondents to rate themselves.\r\n\r\nA useful company report has three parts. First, show the evidence supplied and its scope. Second, identify the important unanswered questions. Third, propose a small set of operating experiments with owners, measures and review dates. Keep sensitive operational information out of the public comparison unless the company has authorised its use.\r\n\r\n## Exhibit 6 — The executive challenge for the next ninety days\r\n\r\n| Period | Decision to make | Concrete output |\r\n|---|---|---|\r\n| First month | Which workflow is important and measurable? | Scope, owner, baseline and eligible-work denominator |\r\n| Second month | What should change, and what could fail? | Bounded trial, authority map and recorded exceptions |\r\n| Third month | What evidence justifies continuation or scale? | Outcome account, deployment decision and next evaluation |\r\n\r\n*Proposed operating agenda, not a claim about completed Mayden fieldwork.*\r\n\r\nA credible AI-native benchmark will be built through repeated observation, challenge and correction. Its value to an executive is the quality of the questions it makes unavoidable: what changed, where it changed, who remains accountable, and how the business knows the change worked.\r\n\r\n### Research note\r\n\r\nThis report is an original Mayden interpretation informed by a scoped, AI-assisted evidence review. It combines distinct study designs to develop questions, not to pool effect sizes. International experiments, surveys, provider telemetry and simulations are labelled at their point of use. They do not supply GCC adoption estimates or validate Mayden's index. Company comparisons belong to the accompanying frozen release and retain their documented scope, eligibility and source limitations.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" }, { "slug": "research-depth", "title": "How much should you trust an AI index?", "dek": "Evidence quality, disclosure bias and what changes when scoring assumptions change.", "kind": "Measurement research", "sector": null, "arSummary": "دراسة للأدلة المنشورة وحدود التقييم. التقرير التفصيلي متاح بالإنجليزية.", "body": "# What would make an AI-native benchmark scientifically credible?\r\n\r\nResearch development note · 10 October 2026\r\n\r\nMayden's ambition is to establish a durable benchmark for how enterprises turn AI into useful, accountable work. The current public corpus contains eighty selected records across eight sectors and five headquarters markets: Saudi Arabia, UAE, Qatar, Kuwait and Bahrain. The frozen first edition (PE-2026.10-v1), with twenty banking and telecom records, remains a historical worked example below. It measures located public evidence. The next stage must establish what the evidence can tell us about an organisation, where it remains incomplete and how a company could demonstrate progress.\r\n\r\nA useful benchmark gives executives better questions before it gives them a position. Which workflows have changed? Who uses the systems? What happens when they fail? What decisions may they execute? What evidence shows that the change helped? Public announcements can suggest answers. They rarely supply the complete operating record.\r\n\r\n## Start with the object being measured\r\n\r\nThree objects need separate treatment: an enterprise's actual operating capability, what its people report about that capability, and what outsiders can locate in public documents. They are related but not interchangeable. A quiet company can have strong capabilities. An articulate company can describe a narrow deployment in impressive detail.\r\n\r\nThe public index is a designed composite of disclosed evidence. It is not yet a validated psychological or organisational scale. Measurement research distinguishes indicators that define a construct from indicators that reflect an underlying trait; the appropriate tests depend on that choice. High internal consistency would not, by itself, establish that the nine themes measure AI-native enterprise capability. [Bollen and Lennox](https://doi.org/10.1037/0033-2909.110.2.305)\r\n\r\nThe programme therefore separates two linked instruments. The public edition maps documented practices and their evidential limits. A confidential company study investigates operating capability using interviews, artefacts, multiple respondents and, where appropriate, usage records. Neither instrument should silently stand in for the other.\r\n\r\n## Keep the number interpretable\r\n\r\nThe current public calculation takes the highest eligible evidence anchor in each of nine themes and divides the documented points by 27. Expressing the result on a 0–100 scale makes the arithmetic readable. It does not turn it into a percentage of AI nativeness.\r\n\r\nFixed anchors have advantages: another company's new announcement does not mechanically rescale everybody else, and repeated announcements within a theme do not add points. But a maximum has limitations. One detailed practice can fill a theme, even when enterprise-wide coverage is unknown. The arithmetic also treats the difference between a commitment and an implementation as equal to the difference between an implementation and a more detailed implementation account. That is a design assumption.\r\n\r\n**Historical worked example: PE-2026.10-v1, twenty records.** A frozen-data sensitivity exercise illustrates the consequence. Across the twenty frozen v1 records, removing commitment-only points while retaining the denominator changes five sector positions. Capping the higher implementation anchor at two points, with the fixed denominator, changes nine. Eighteen checks that individually halve or double one theme's weight change between one and eight positions each. These are deterministic alternative assumptions, including changes in ties. They are not confidence intervals or estimates of the probability that a company belongs in a position.\r\n\r\n**Current-corpus comparison: frozen PE-2026.10-v3, eighty records.** Repeating the same deterministic checks changes 16 sector positions when level-1 points are omitted and 20 when detailed anchors are capped at two. The eighteen individual weight changes alter between 1 and 23 positions each. This calculation includes changed ties and unranked states; it compares each scenario with its own frozen baseline. These counts describe method dependence in the expanded corpus, not company improvement since v1. The fourth-edition editorial revision retains the company coding and baseline scores.\r\n\r\nThe practical implication is to foreground the nine-theme profile, raw points and evidence coverage, with positions as a secondary view. Methodology research also shows that nominal weights and statistical importance need not coincide when indicators vary and correlate. Equal weights are a transparent starting choice, not a discovery that each theme contributes equally to performance. [Becker and colleagues](https://doi.org/10.1016/j.ecolind.2017.03.056)\r\n\r\n## Separate three kinds of coverage\r\n\r\nAn executive should be able to distinguish research coverage, evidence coverage and operational coverage.\r\n\r\nResearch coverage describes work completed against a declared search plan. It includes source types checked, languages searched, reporting periods considered and documents retrieved. Evidence coverage describes how many themes have qualifying claims. Operational coverage describes the proportion of relevant people, workflows, transactions or decisions affected by a practice.\r\n\r\nOnly the last speaks directly to penetration inside the firm. A report discussing all nine themes does not prove enterprise-wide use. Conversely, an inaccessible annual report is a research limitation, not evidence that an operating practice is absent.\r\n\r\nThe public research-input ledger publishes recorded search routes and access gaps. A future standardised search matrix should add planned-versus-completed routes alongside each company profile. The matrix would make failed retrievals, unresolved scope questions and under-searched themes visible. It would report completion against the plan without calling that an estimate of the percentage of all relevant evidence found. PRISMA-S offers useful reporting disciplines for documenting searches; adopting those disciplines does not make this corporate review an exhaustive systematic review. [PRISMA-S](https://doi.org/10.1186/s13643-020-01542-z)\r\n\r\n## Trace every conclusion to its support\r\n\r\nA credible claim needs more than a link. Readers should see the relevant page or passage, the corporate entity it concerns, the operating period, whether the practice is a plan or in use, and what remains unestablished. A group announcement should not automatically become evidence for every subsidiary. A vendor's customer story remains a commercially connected account. A mirrored annual report is another route to the same document, not another independent observation.\r\n\r\nThe evidence ledger records these distinctions and links each transformation: fetched source, extracted passage, atomic claim, review decision and published contribution. W3C's provenance model provides a useful vocabulary for entities, activities, agents and derivations. Traceability helps assessment; it does not certify truth. [W3C PROV-DM](https://www.w3.org/TR/prov-dm/)\r\n\r\nThe public interface exposes contributing claims beside theme results. That view should include qualifying, contradictory and excluded material, with the reason for each decision. “Company-reported” and “independently corroborated” should be explicit labels, never an unexplained confidence percentage.\r\n\r\n## Measure work, people and boundaries together\r\n\r\nRecent empirical research supports studying AI at the level of tasks, roles and operating conditions. It does not establish one universal enterprise productivity multiplier.\r\n\r\nA 2026 observational preprint links ChatGPT Enterprise account and usage records with worker roles and company information. Its author abstract describes differences in adoption speed, breadth and purpose. The selected customers of one product cannot represent all enterprise AI, and observed usage does not demonstrate causal business impact. The study nevertheless motivates a useful distinction between available accounts, active use, tasks performed and roles reached. [Chatterji and colleagues, version 1](https://arxiv.org/abs/2608.12236v1)\r\n\r\nAnother preprint describes randomized field experiments across seven online-retail workflows, with effects varying by application, including some with no effect. Its author abstract supports a workflow-specific evaluation question, not a transferable enterprise-wide return estimate. The inspected version concerns one platform and sales outcomes. [Fang and colleagues, version 6](https://arxiv.org/abs/2510.12049v6)\r\n\r\nFor Mayden, the corresponding interview should ask for a concrete example, a relevant denominator and an observable boundary. “We rolled out AI” becomes: which process, to whom, over what period, how frequently, with which review or fallback, compared with what baseline? The answers should remain separate from unrelated marketing claims or future forecasts.\r\n\r\n## Test the questionnaire before making it authoritative\r\n\r\nA company instrument must be understandable to the people answering it. That requires more than expert approval of attractive terminology. An executive, an operations manager and a technology owner may interpret “autonomy”, “deployment” or “governance” differently.\r\n\r\nContent-validity guidance in health measurement distinguishes relevance, comprehensiveness and comprehensibility, including direct testing with intended respondents. These principles can inform organisational questionnaire development, while the clinical thresholds and grading rules require adaptation. [COSMIN content-validity manual](https://www.cosmin.nl/wp-content/uploads/COSMIN-methodology-for-content-validity-user-manual-v1.pdf)\r\n\r\nThe next company pilot should begin with English and Arabic cognitive interviews in the UAE and Saudi Arabia, across more than one sector and respondent role. Participants should explain how they interpret each question and how they chose an answer. Unknown, no and not applicable need distinct response options. A limited feasibility pilot can reveal misunderstood questions, onerous evidence requests and missing topics. It cannot establish population norms.\r\n\r\nSubsequent validation should examine respondent agreement, stability over an appropriate interval, relationships with independently measured operating practices and differences across language, sector and company scope. The required sample depends on those analyses and the desired precision. There is no defensible universal pilot size that makes the instrument scientific.\r\n\r\n## Preserve comparability as AI changes\r\n\r\nA durable index needs stable definitions and an honest account of revisions. The U.S. Census Bureau's 2026 business-AI analysis explains that its survey question broadened in November 2025 from production-related use to use in any business function. Such changes demonstrate why a trend can reflect question design as well as business behaviour. U.S. survey estimates are not GCC adoption norms. [U.S. Census Bureau](https://www.census.gov/library/stories/2026/05/ai-use-businesses.html)\r\n\r\nThe programme needs a stable core and version emerging-practice modules separately. Each update should distinguish newly located evidence, corrected coding, changed organisational scope, revised methodology and documented operational change. Historical releases should remain reproducible. A better search next year must not be described as proof that the company improved.\r\n\r\nThis development note is a scoped methodological review with selected empirical examples. It is not a systematic literature review, new meta-analysis or validation of Mayden's instrument. Reporting guidance such as PRISMA provides useful transparency standards; using the name is not a substitute for executing and documenting the corresponding research design. [PRISMA 2020](https://doi.org/10.1136/bmj.n71)\r\n\r\nThe near-term publication can be valuable on these terms: a transparent account of what selected companies disclose, where evidence is strong enough to inspect, and which important questions remain unanswered. A stronger enterprise benchmark will follow from the quality of those questions, the evidence collected against them and the discipline with which Mayden separates observation from inference.\r\n\r\n**Review transparency.** Extraction and source challenge in this edition were AI-assisted. Separate AI readers do not constitute human expert sign-off or independent assurance. Company claims, research findings and Mayden interpretations retain their distinct scope.\r\n" } ], "sourceCount": 206, "sourceCountDefinition": "Unique URLs in source registers and literature, including excluded and unscored records and original/mirror URLs.", "evidenceCount": 272, "eligibleEvidenceCount": 224, "methodology": "# Public Evidence Edition: methodology\r\nVersion PE-2026.10-v4 · Research cut-off: 10 October 2026\r\n\r\n## What this edition measures\r\nThe AI Native Index is Mayden's research programme on how AI changes enterprise operations. This public edition measures the **breadth and specificity of documented public evidence** in a deliberately selected company cohort. It is exploratory. It is not a validated measure of actual AI-native capability, an audit, a certification or a representative estimate of Gulf adoption.\r\n\r\nMayden's working definition is an enterprise that repeatedly converts AI capability into useful, accountable work across a defined organisational scope. The definition and nine-theme framework remain hypotheses for empirical validation.\r\n\r\n## Cohort and geographic scope\r\nThe review includes eighty selected entity records: ten each in Banking, Telecommunications, Energy & Utilities, Aviation & Logistics, Manufacturing & Materials, Retail & Consumer, Healthcare, and Real Estate & Construction. Each selected sector cohort has two headquarters records in Saudi Arabia, the UAE, Qatar, Kuwait and Bahrain. Energy includes operating subsidiaries and public utility corporations; aviation/logistics combines distinct business models. The added sectors also mix operating roles: manufacturing spans materials and processing, retail includes food production, healthcare includes provider groups and public services, and real estate includes destination development and asset operations. These are explicit editorial cohorts, not homogeneous samples. Selection is purposeful, based on relevance to a launch study and identifiable public corporate disclosures. It is not random sampling, a complete industry census, or a selection established by a market-share threshold. Oman is not included; this is not full GCC coverage.\r\n\r\nThe assessment unit is stated separately for each company. The corpus mixes international groups, local operators and subsidiary practices. Headquarters labels do not show where every practice operates. stc group overlaps with stc Kuwait and stc Bahrain; e& has an interest in Mobily. No country average, national league table, GCC prevalence estimate or pooled company average is produced. Sector positions compare this selected corpus only; mixed scopes limit comparability.\r\n\r\n## Research process and source access\r\nA bounded desktop review used targeted corporate-domain searches and annual, sustainability, governance and implementation reports, predominantly in English with limited Arabic discovery. Researchers sought evidence across all nine themes, followed relevant primary sources and recorded search boundaries. The search was not exhaustive or a preregistered systematic review. Different publication practices, language access and retrieval failures create disclosure bias.\r\n\r\nThe underlying research archive retains company records, claim-level coding, URLs, publication dates where available, access dates, locators, implementation stages, source limitations, search notes and adjudications. Published reports cover mixed periods, including historical and 2025 disclosures available by the cut-off. They do not represent one common financial-year observation window. Undated source records remain undated.\r\n\r\nCorporate reports establish **what a company reports**, not independent verification of its operations. A readable company-authored report on a third-party mirror can qualify, with provenance and lack of issuer hash matching disclosed. Search-result snippets and indexed-only excerpts are retained as leads but excluded from scoring unless the exact claim is independently readable in an eligible full-source page or document. Vendor announcements and memoranda are not proof of a customer's deployment.\r\n\r\n## Nine dimensions\r\nThe framework examines AI in core workflows; workforce and skills; ecosystem integration; interfaces; data lineage; experimentation; autonomy; organisational knowledge; and governance. Each asks about an operating practice, not spending, technology brand names or the number of announcements.\r\n\r\nA single practice can inform more than one theme only where the claim explicitly supports each construct. Repeated claims do not add points within a theme. This first framework may contain correlated dimensions; we have not demonstrated discriminant validity or an optimal weighting scheme.\r\n\r\n## Evidence anchors\r\n| Level | Meaning | Minimum requirement |\r\n|---|---|---|\r\n| U | Unknown in this review | No accessible qualifying claim. This does not mean absent capability. |\r\n| 1 | Specific commitment or pilot | A named practice and concrete action, pilot or implementation commitment. Generic ambition earns no points. |\r\n| 2 | Implemented practice | An explicit statement that a relevant AI practice is in use in the assessed operating scope. |\r\n| 3 | Implemented practice with operating detail | Level 2 plus a specific mechanism, coverage detail, evaluation process or decision authority. This is evidence specificity, not proven enterprise-wide excellence. |\r\n\r\nCustomer AI products, infrastructure capacity for customers and general digitalisation do not by themselves demonstrate internal AI-native operating practices. AI used to deliver the operator's own service may qualify where the operating mechanism and entity scope are explicit. Generic privacy or data policies cannot automatically establish AI governance.\r\n\r\n## Normalisation and positions\r\nFor each company, take the highest eligible anchor in each of the nine dimensions. Assign 1, 2 or 3 documented points. U contributes no documented points to the calculation while remaining **unknown** in the displayed profile.\r\n\r\n**Public evidence points = 100 × (sum of the nine documented theme levels) ÷ 27.**\r\n\r\nThe display rounds to a whole point. In the downloadable JSON, a null theme value means U (unknown), not zero capability. The denominator is fixed; it does not shrink when evidence is missing. No min–max rescaling, percentile transformation or z-score is applied. A score of 50 does not mean “50% AI-native”. The profile and the number of themes with qualifying evidence must appear alongside the total.\r\n\r\nThe anchors are ordinal judgments. Summing them assumes equal numerical steps that have not been empirically calibrated. A maximum captures the strongest located disclosure; one narrow implementation can fill a theme without establishing firm-wide coverage. Each theme has equal nominal weight. This is a transparent initial design choice, not an empirically validated estimate of importance. Positions use unrounded totals within each ten-entity sector cohort. Equal totals share a position; the next position reflects the number of records above it. A record with no qualifying evidence is unranked, rather than labelled the least capable. Search and market filters do not change published positions.\r\n\r\nThe fixed normalisation enables reproducible arithmetic, but does not solve differences in disclosure, scope or source availability. A one-point coding change moves the normalised score by approximately 3.7 points. Close positions are therefore especially fragile. The release does not establish statistically significant differences between companies and does not publish confidence intervals unsupported by a sampling model.\r\n\r\n## Outcomes stay separate\r\nReported time savings, financial returns, customer metrics and forecasts are recorded separately from capability evidence. Attribution, baseline, period and denominator must accompany interpretation. A company's reported percentage is not a causal estimate merely because it appears in an annual report. We do not average heterogeneous outcome percentages or turn them into capability points.\r\n\r\n## Review and reproducibility\r\nAI research agents extracted and challenged source claims. A separate AI review pass checked proposed level-3 anchors and selected lower-level, access and exclusion decisions. Review changes are retained in the archive. This is not human expert sign-off, independent assurance or a completed inter-rater reliability study. A second AI reader can share errors with the first.\r\n\r\nThe public JSON release contains the company profiles, eligible and excluded claims, sources attached to claims, literature references, report texts and this method. A reproducible builder and a SQLite research database are retained by Mayden on D:. The archive hashes the research records. Some expansion documents are retained privately for review; the public bundle does not redistribute those source documents. The separate collector fingerprints only its explicitly attempted URLs, not every source in the corpus. Source pages may subsequently change.\r\n\r\n## How the research synthesis was assembled\r\nThe accompanying research series comprises eleven visual reports, including eight sector reports and three cross-sector reports. Its research register contains 34 selected references spanning empirical studies, organisational measurement research and methodological frameworks; these are not 34 comparable experiments or a complete literature census. The synthesis is a scoped rapid evidence review. Its evidence table records design, population, sample, setting, findings, limits and measurement implications. It is not a new statistical meta-analysis, systematic literature review or pooled effect estimate. Studies of a writing task, a contact centre and a software repository cannot establish one enterprise-wide productivity effect.\r\n\r\n## From disclosure research to a validated company study\r\nPublic evidence and proprietary company assessment must remain separate instruments. The existing nine-question self-check is qualitative and private to the browser. The downloadable 27-question instrument is a draft for cognitive testing, not a validated survey or automated certification.\r\n\r\nThe next development gates are: expert content review; English and Arabic cognitive interviews; a recruited UAE/Saudi cross-sector pilot; independent coding of blinded evidence; test–retest and respondent-role checks; construct and outcome validation; and sensitivity analysis of missingness, weights and aggregation. Sample sizes should follow the measurement model and required precision, rather than a convenient participant target. A small pilot can test comprehension and feasibility; it cannot establish general population norms.\r\n\r\nThe company study should collect multiple perspectives where feasible, distinguish “no”, “unknown” and “not applicable”, and request corroborating operating evidence. Results should separate capabilities, outcomes and evidence confidence. Participation consent, confidentiality, retention and permitted aggregation must be agreed before collecting confidential records or voice interviews.\r\n\r\n## Corrections, editions and independence\r\nMayden owns its original framework, code, compiled research records and editorial analysis subject to applicable rights; it does not own the underlying public facts or third-party reports. Companies may submit corrections or additional sources through the contact page. Contributions enter the same evidence protocol. Payment, sponsorship and advisory work must not purchase points or a position.\r\n\r\nThis release is a dated snapshot. Substantive changes require a versioned correction log and regenerated dataset. Future trend reports must distinguish changed evidence availability, changed company scope, instrument changes and actual operating change. A stable core questionnaire and a separately versioned emerging-practice module will support longitudinal work.\r\n\r\nThe AI Native Index™ name is used as a brand; no claim of registered trademark status or completed clearance is made. A validated enterprise benchmark remains the programme's objective, not a result established by this public evidence series.\r\n\r\n## Methodological foundations\r\nThe design draws on the [OECD/JRC Handbook on Constructing Composite Indicators](https://doi.org/10.1787/9789264043466-en), the [NIST AI Risk Management Framework](https://www.nist.gov/itl/ai-risk-management-framework), and [Boateng and colleagues' scale-development primer](https://doi.org/10.3389/fpubh.2018.00149). These sources inform design choices; none validates Mayden's instrument.\r\n\r\n\r\n\r\n\r\n## What changed in the second edition\r\nThe first edition remains downloadable with its original bytes. This edition expands the corpus and publishes score-contribution tables, deciding claim IDs, review decisions, search records and deterministic robustness tests. The change log separates new companies from corrections to existing coding. A changed number between editions is not evidence that a company changed its operations.\r\n\r\nEligibility now enforces explicit extraction exclusions, rejects invalid or future publication dates (including future months), and prevents a source-level access recovery from validating unrelated claims. New reviewed claims must bind to the exact serialized claim record; adverse access decisions and unresolved or conflicting reviews cannot silently promote an anchor. New implemented anchors require a deployed or scaled status. A detailed pilot remains a pilot. Older adjudications are identified as legacy records rather than retroactively described as hash-bound reviews.\r\n\r\n## What changed in the third edition\r\nThe third edition adds forty selected entity records across Manufacturing & Materials, Retail & Consumer, Healthcare, and Real Estate & Construction. It adds nine primary research references and a flagship report that separates task performance, workflow adoption, decision authority, business consequences and organisational learning. Earlier editions retain their versioned records; the larger corpus is not a new observation of regional adoption prevalence.\r\n\r\nThe construction remains the same nine-theme maximum-anchor method, fixed 27 denominator and twenty-one declared specifications. New sector packets receive claim-bound AI challenge of all proposed level-3 claims and a purposive sample of lower anchors and exclusions. Review can correct an implementation-stage label while preserving the exact original claim and its hash. A future commitment cannot receive an implemented anchor merely because its raw extraction called it deployed.\r\n\r\nA separate replay of the prior forty-company snapshot reproduced its point totals and examined dependence on individual source URLs. It is an audit of that snapshot, not evidence of temporal change or independent corroboration. The new flagship's conceptual exhibits are operating frameworks and questions; they are not survey distributions or estimates from the selected company corpus. Any plotted corpus quantities must identify the edition and denominator from which they were computed.\r\n\r\n## Inspect an individual calculation\r\nFor every company the interface lists each theme's maximum, its contribution out of 100 and all eligible claims tied at that maximum. The underlying records retain the proposed anchor, accepted anchor, exclusion reasons, publisher, source type, locator and review rationale. These records permit an external reader to reproduce the arithmetic and challenge the coding. They do not turn a publisher's account into verified operational fact.\r\n\r\nClaim fingerprints bind structured research records, not the truth of a statement. Source fingerprints bind retrieved bytes; different byte hashes may reflect web templates, navigation or other unrelated page changes. Source URL counts are not counts of independent observations. A vendor customer story, an issuer release and their republications may share one origin. No numerical probability of truth or independent-assurance claim is assigned.\r\n\r\n## Twenty-one declared specification checks\r\nThe baseline is accompanied by twenty alternative specifications: omit all level-1 points; cap level 3 at 2 while retaining the fixed 27 denominator; and, separately for each of nine themes, halve or double its weight while all other weights stay 1. Weighted scores divide by three times the sum of weights. All alternatives recalculate tied positions within the unchanged sector cohort, using unrounded values. The published company profile exposes the resulting score and position and the range across the declared specifications.\r\n\r\nThese are deterministic assumption checks, not confidence intervals, probabilities, significance tests or proof that one specification is correct. Capping anchors at2 deliberately lowers the attainable score to 66.7 on the existing scale; it does not redefine the scale to 18. Weight changes test local design dependence, not empirically estimated importance. An all-U result remains unranked in every specification.\r\n\r\nFor each company a separate check removes one qualifying source URL at a time and recalculates the profile from surviving claims. The largest drop exposes dependence on a particular located document. This removes each retained claim’s primary URL; required corroborating sources are not yet removed as dependencies. This is weaker than removing an entire source family: mirrored or republished copies can remain. It does not imply that a long annual report is less trustworthy. Reconcile source families and operating cases before interpreting this as corroboration sensitivity.\r\n\r\n## Research coverage is not operational coverage\r\nThe public research-input file includes the recorded queries or search topics, source IDs, dates, languages where recorded and access gaps. These logs describe performed work. They are not a search-recall estimate, a complete list of all relevant documents or proof that all nine themes were searched equally. Theme coverage counts themes with qualifying claims. Operational coverage asks how many relevant employees, workflows or decisions actually use a practice; that denominator is often undisclosed. The three concepts must not be substituted for each other.\r\n\r\n## The source collector: a tested beginning\r\nThe collector is a bounded, manually invoked public-source checker. It uses an approved HTTPS host and URL register, a declared user agent, request limits and pacing, robots access checks, conditional requests where supported, byte and extracted-text hashes, and dated retrieval receipts. It distinguishes first observations, unchanged content, rendering-only changes, content changes and access failures. HTML keyword matches and discovered links are research leads only. PDF fetching in this prototype fingerprints bytes; it does not extract or validate PDF claims.\r\n\r\nRetrieved text is treated as untrusted source material. The collector cannot accept a claim, assign an anchor, modify the released dataset or publish a ranking. It has no credentials for confidential company systems and no paid-data connector. Restricted material would require permissioned access and a separate agreement. This is not an official government crawler, an external certification or continuous monitoring. Run receipts state precisely which URLs were attempted, and automated access can fail even when a separate manual research route succeeds.\r\n\r\nIts HTTP request deadline and request budget bound collection after DNS resolution; DNS remains subject to the host operating system resolver. Supported content formats, publisher access rules, language coverage, dynamic pages, changing page templates and PDF extraction remain engineering limits. The current prototype records hashes and bounded discovery metadata rather than a complete immutable archive of the web. A production service would require persistent job scheduling, source-family management, extractor validation, review queues, storage/retention controls and operating ownership.\r\n\r\n\r\n## What changed in the fourth edition\r\nThe fourth edition follows two separately tasked AI challenge teams and a panel adjudication. The consulting-style briefs do not imply affiliation with BCG or McKinsey. This is an AI-assisted challenge of research and publication choices, not independent human review or validation of enterprise capability. The release retains the eighty company records, nine-theme anchors and baseline score construction. Editorial and diagnostic additions do not establish a change inside any company.\r\n\r\nEvery report now identifies the AI-assisted nature of source review locally. Internal production instructions have been removed from report prose. The healthcare discussion links to the original AIRIS-TB study and keeps issuer-reported throughput, retrospective model evaluation and prospective workflow effects separate. The methodological report labels its twenty-record v1 example as historical and adds a separate eighty-record v3 sensitivity comparison. Neither is a time-series measure of adoption.\r\n\r\nA worked calibration set covers all nine themes using actual retained claim IDs and decisions. It includes positive cases, interpretation boundaries and exclusions; where the frozen corpus lacks a particular category, the absence is stated rather than filled with an invented claim. These are teaching examples, not a blinded reliability study. The company-instrument validation protocol specifies human content review, bilingual comprehension work, independent coding, multi-informant checks and outcome validation as future gates. None is claimed complete.\r\n\r\n## Evidence dates and continuity\r\nPublication date, observation period, retrieval date and confirmation of continuing use answer different questions. The frozen v3 corpus contains 224 eligible claims: 79 have no recorded publication date and 28 have a recorded date before 2025. An undated record may describe a dated reporting period; it is not automatically obsolete. A 2026 release label does not make historical implementation current.\r\n\r\nA companion dated-source check retains eligible claims whose recorded publication date begins in 2025 or later and recomputes the profile against the same denominator. This is a sensitivity rule about recorded source dates, not evidence that older or undated practices stopped. The baseline remains unchanged. Evidence from an older source can still be useful, while continuity requires a separate dated operating confirmation. Do not substitute this date filter for a current-maturity score.\r\n\r\n## A bounded source-family and operating-case pilot\r\nThe provenance pilot maps two Emirates NBD PCA claims to one issuer account and one operating case. They concern different dimensions but are not independent confirmations. Removing the declared two-claim family or case changes that company's calculated evidence points from 92.5926 to 70.3704 before whole-number display rounding. Both checks give the same result because the declared member sets are identical.\r\n\r\nThis limited mapping has been checked against the frozen v3 record and its arithmetic. It is not a reconciled source-family graph for all companies. Required corroborating-source dependencies remain unadjudicated in this pilot and are represented as unknown, not inferred. The existing whole-corpus removal check remains a single-primary-URL test. Mirrored publications, common assertion origins, case overlap and required dependencies must be resolved before claiming comprehensive corroboration sensitivity.\r\n", "collection": { "uniqueSources": 40, "retrievedSources": 19, "runs": 4, "lastRun": "2026-10-10T01:55:29.370889+00:00", "latestStates": { "retrieved": 17, "not-modified": 2, "robots-blocked": 14, "error": 5, "http-error": 2 }, "scope": "Bounded manually invoked runs over registered source URLs. No completeness, continuous monitoring or factual-verification claim." }, "outcomes": [ { "id": "bank-out-enbd-trinity", "companyId": "emirates-nbd", "sourceId": "enbd-ar25", "metric": "Contact-centre sales performance improvement", "reportedValue": "approximately 40%", "period": "2025", "baseline": "Not specified in retained passage", "population": "High-volume contact-centre processes under Project Trinity; exact denominator not disclosed", "attribution": "Report says GenAI contributed; not causal isolation", "independentVerification": false, "eligibleForCapabilityScore": false }, { "companyId": "mobily", "sourceId": "T-S04", "metric": "Issue identification and analysis speed", "value": "approximately 90% faster", "period": "2025", "attribution": "Mobily annual-report self-report", "limitations": "Indexed passage; comparator, sample, dates and net-cost denominator not disclosed; not used in capability score." }, { "companyId": "zain-group", "scope": "Zain Kuwait FME Mate", "sourceId": "T-S15", "metric": "Fault-handling efficiency", "value": ">20% improvement in approximately 90% of cases", "period": "2025 early results", "attribution": "Zain annual-report self-report", "limitations": "Number and definition of cases, comparator and independent validation absent; expected 2,250 avoided annual downtime hours is forecast, not realized outcome." }, { "companyId": "beyon", "scope": "Batelco Bahrain", "sourceId": "T-S20", "metric": "Digital support transactions share", "value": "30% of total interactions", "period": "2025", "attribution": "Beyon annual-report self-report", "limitations": "Digital channel share is not AI resolution rate or avoided calls." }, { "id": "ENE-O01", "companyId": "aramco", "sourceId": "ENE-S01", "metric": "Fadhili pilot resource use", "reportedValue": "10–15% lower amine/steam; approximately5% lower power", "referencePeriod": "October2024–April2025", "baseline": "Not fully specified", "attribution": "Company-reported; detailed evaluation ongoing", "independentVerification": false, "scoreImpact": false }, { "id": "ENE-O02", "companyId": "adnoc", "sourceId": "ENE-S04", "metric": "Neuron5 pilot potential", "reportedValue": "50% fewer unplanned shutdowns;20% longer maintenance intervals", "referencePeriod": "2024", "baseline": "Not specified", "attribution": "Potential, not achieved enterprise outcome", "independentVerification": false, "scoreImpact": false }, { "id": "ENE-O03", "companyId": "dewa", "sourceId": "ENE-S07", "metric": "Rammas inquiries", "reportedValue": ">1.6million in2025", "referencePeriod": "2025", "baseline": "Count, not improvement", "attribution": "Company-reported; not unique users, accuracy or resolutions", "independentVerification": false, "scoreImpact": false }, { "companyId": "saudia-group", "sourceId": "L-S04", "quantity": "350 instructors; 250 facilities", "meaning": "Training-system resource context, not independently measured AI productivity.", "denominatorLimitations": "No count of actual AI scheduling users or counterfactual. Platform implementation combines multiple technologies.", "usedInScore": false }, { "companyId": "emirates-group", "sourceId": "L-S03", "quantity": "More than 2,000 IT staff by end-August 2026", "meaning": "Reported AI-gateway onboarding, used only as specificity of deployment coverage.", "denominatorLimitations": "No group workforce adoption rate, active-use rate or causal productivity estimate.", "usedInScore": false }, { "companyId": "dp-world", "sourceId": "L-S07", "quantity": "350,000 unproductive moves eliminated annually; 20% improvement in truck servicing time", "meaning": "Issuer-reported BoxBay operational claims, retained separately from AI maturity.", "denominatorLimitations": "Baseline, attribution and independent verification unavailable; not generalized to all DP World or all AI systems.", "usedInScore": false }, { "companyId": "jazeera-airways", "sourceId": "L-S13", "quantity": "Third position in SkyBreathe fuel-efficiency programme", "meaning": "Company-reported supplier-programme comparison, not a Mayden company ranking.", "denominatorLimitations": "Comparison denominator, methodology and AI causal contribution not established.", "usedInScore": false }, { "companyId": "gulf-air-group", "sourceId": "L-S19", "quantity": "75 staff trained in Power BI; 3,800 employees in group context", "meaning": "General analytics training and organizational context, not AI-specific participation.", "denominatorLimitations": "Must not be represented as 75 Copilot users or 3,800 AI users.", "usedInScore": false }, { "evidenceId": "HLT-006", "metric": "Documentation time reduction", "value": "40%", "attribution": "Dallah annual report2025", "limitations": "No baseline design, comparison group or time window; not an independently causal effect." }, { "evidenceId": "HLT-012", "metric": "Daily AI screening examinations", "value": "2,000", "attribution": "M42 July2025 statement", "limitations": "Reported operating throughput;200 conventional comparator has unclear staffing/time comparability." }, { "evidenceId": "HLT-017", "metric": "Documents processed annually", "value": "over60million", "attribution": "PureHealth2025 Daman account", "limitations": "Annualised wording; no sampled accuracy/error or denominator method disclosed." }, { "evidenceId": "HLT-031", "metric": "Traffic handled by conversational AI", "value": "close to20%", "attribution": "Named BSH director on Voiceoc site", "limitations": "Undated testimonial; denominator and measurement method unspecified." }, { "evidenceId": "HLT-013", "metric": "Retrospective validation AUROC", "value": "98.5%", "attribution": "M42 August2025 study announcement", "limitations": "Company summary of study; predictive discrimination is not health benefit or automation completion." }, { "evidenceId": "REA-004", "metric": "CoralNet training images", "value": "1,500", "attribution": "RSG marine director account", "limitations": "Training size, not independent accuracy or biodiversity benefit." }, { "evidenceId": "REA-011", "metric": "Automated security operating hours", "value": "34,000+", "attribution": "Aldar InnovationReport2025PDF16", "limitations": "Issuer estimate; baseline, attribution model and evaluation interval absent." }, { "evidenceId": "REA-017", "metric": "AI customer conversations", "value": "215,000", "attribution": "Aldar2025annual report", "limitations": "Conversation volume is not completed tasks or measured quality." }, { "evidenceId": "REA-020", "metric": "Lusail integration scope", "value": "80sites;10,000+video and200+operational systems", "attribution": "STEngineering", "limitations": "Supplier-reported integration, not users, deployments per company or productivity effect." } ], "sensitivity": { "meaning": "Deterministic specification checks, not confidence intervals or probabilities. All comparisons use the same sector cohort. Removing a URL does not remove every republication of that underlying claim.", "scenarios": [ { "id": "baseline", "label": "Published coding", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "implemented-only", "label": "Remove level-1 commitments and pilots", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 2 }, { "id": "detail-cap", "label": "Cap level-3 detail at level 2 (fixed denominator)", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 2, "minimum": 1 }, { "id": "ai-core-0.5", "label": "ai-core weight ×0.5", "weights": { "ai-core": 0.5, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "ai-core-2", "label": "ai-core weight ×2", "weights": { "ai-core": 2, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "workforce-0.5", "label": "workforce weight ×0.5", "weights": { "ai-core": 1, "workforce": 0.5, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "workforce-2", "label": "workforce weight ×2", "weights": { "ai-core": 1, "workforce": 2, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "ecosystem-0.5", "label": "ecosystem weight ×0.5", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 0.5, "interfaces": 1, "lineage": 1, 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"experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "lineage-2", "label": "lineage weight ×2", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 2, "experimentation": 1, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "experimentation-0.5", "label": "experimentation weight ×0.5", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 0.5, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "experimentation-2", "label": "experimentation weight ×2", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 2, "autonomy": 1, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "autonomy-0.5", "label": "autonomy weight ×0.5", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 0.5, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "autonomy-2", "label": "autonomy weight ×2", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 2, "knowledge": 1, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "knowledge-0.5", "label": "knowledge weight ×0.5", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 0.5, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "knowledge-2", "label": "knowledge weight ×2", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, "experimentation": 1, "autonomy": 1, "knowledge": 2, "governance": 1 }, "cap": 3, "minimum": 1 }, { "id": "governance-0.5", "label": "governance weight ×0.5", "weights": { "ai-core": 1, "workforce": 1, "ecosystem": 1, "interfaces": 1, "lineage": 1, 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