Public case studies are useful only when source evidence and architecture interpretation are not mixed.
9.1 Evidence and inclusion method
A case was included only when a first-party source named the organization, described a real deployment or operational workflow, and provided enough detail to establish what was used and what outcomes were reported.
9.2 Morgan Stanley — AI-assisted advisor knowledge
The source describes evaluations against real advisor use cases, expert feedback, controls, and zero data retention. It also reports that advisors spend less time searching and more time with clients.
Why the case matters
In a regulated knowledge workflow, adoption depends on reliable retrieval and evidence quality rather than fluent text alone.
9.3 Klarna — Customer-service AI assistant
The published story also reports work equivalent to 700 full-time agents, human-level customer-satisfaction scores, a 25% reduction in repeat inquiries, operation in 23 markets and more than 35 languages, and an estimated USD 40 million profit improvement for 2024.
Why the case matters
This is workflow automation, not only employee productivity. Appropriate metrics include repeat contact, resolution time, satisfaction, escalation, and financial outcome.
9.4 Moderna — Enterprise-wide AI adoption
The source names Dose ID as a clinical-data analysis pilot, Contract Companion for contract summaries, and Policy Bot for internal policy questions. It also reports an average of 120 ChatGPT Enterprise conversations per user per week during the stated period.
Why the case matters
A shared platform can support many functions, but regulated or safety-relevant workflows still need validation and accountable human review.
9.5 BBVA — AI adoption across a regulated bank
The 2026 story reports more than 70% active usage, training for 250 senior leaders, and alignment of security, legal, compliance, and technology. An internal assistant in Peru reportedly reduced average query handling from about 7.5 minutes to about one minute.
Why the case matters
At regulated scale, training, leadership participation, legal and security alignment, and internal communities are part of the operating architecture.
9.6 LSEG — Trusted data inside AI workflows
The source says LSEG is integrating trusted data directly into AI workflows and references a Model Context Protocol capability for customers to access precise, verifiable information. It emphasizes governance, legal, compliance, cybersecurity, and delivery controls.
Why the case matters
The differentiating enterprise asset is often the governed connection between models and trusted domain data.
9.7 NTT DATA Group — Incident analysis with Codex
The source says the CoE supports technical validation, use-case development, usage monitoring, knowledge resources, and adoption. Security guidance covers permitted data, connected systems, network traffic, sandbox mode, automation level, and human review.
Why the case matters
Delegated execution requires stricter controls than chat: environment, network, system access, autonomy level, and human review must be explicit.
9.8 Accenture — Enterprise-scale GitHub Copilot
The published story reports more successful builds and more pull requests per week, plus use for tests, legacy-code understanding, and technical debt. It also describes repository scanning with GitHub Advanced Security.
Why the case matters
Developer AI should be evaluated through build success, pull requests, tests, security findings, and delivery outcomes—not only suggestion acceptance.
9.9 Cross-case comparison
| Organization | Primary pattern | Boundary | Reported evidence |
|---|---|---|---|
| Morgan Stanley | Governed retrieval | Internal financial knowledge | Adoption, document access, evals |
| Klarna | Customer-service automation | Shopping and payments support | Volume, resolution time, repeat inquiries |
| Moderna | Federated enterprise platform | Clinical support and corporate functions | Adoption, custom GPTs, usage |
| BBVA | Governed enablement | Multiple banking functions | Users, active usage, time saved |
| LSEG | Trusted-data integration | Financial markets data | Release and delivery cycles |
| NTT DATA | Agentic task execution | Incident analysis | Task duration and user scale |
| Accenture | Embedded developer copilot | Software engineering | Controlled trial and delivery signals |
9.10 Architecture lessons
The strongest cases improve retrieval, service resolution, incident analysis, development, or delivery.
Evals, CoEs, legal and security alignment, and clear guidelines recur across deployments.
Trusted internal knowledge and proprietary data create enterprise-specific value.
Resolution time, repeat contacts, release cycles, task completion, and build success are more useful than token volume.
Agentic execution requires sandbox, network, access, and human-review rules.
Central controls can coexist with domain-led creation of assistants and workflows.
9.11 Evidence limitations
9.12 Chapter summary
The cases show several paths to enterprise AI value: governed knowledge retrieval, customer-service automation, enterprise platforms, trusted-data products, agentic incident analysis, and embedded development assistance.
What the strongest cases share is a real workflow, access to domain context, measurable outcomes, and an operating mechanism for evaluation and governance.