Executive Summary
Finance, regulated environment, several business areas, risk/compliance/IT involved
250-1,000 employees
Several business areas wanted to use generative AI faster, while risk, compliance and IT lacked one shared decision model.
Shadow LLM usage, sensitive data classes and unclear approval paths
Executive interviews, LLM use-case scoring, rules blueprint and approval grid for sensitive data and model usage.
A prioritized LLM portfolio, reliable approval paths and a management-ready frame for controlled piloting.
Approval matrix and LLM use-case scorecard
Observed before/after
Observed before/after
Shadow AI, wrong data usage and politically driven favorite projects
Use-case scoring, data classes, approval boundaries, responsibilities and short decision note
9 -> 3 prioritized initiatives and 100% defined approval paths
Project voiceRole: risk/compliance. Business, IT and control functions could finally discuss the same decision.
Technical architecture
Technical architecture
The architecture layer shows how sources, permissions, review gates and operating artifacts were separated before execution.
Use cases, data classes and knowledge sources are inventoried before tooling or model choice.
Approval grid, responsibilities and review points separate allowed, review-required and stopped usage.
Pilot scope, model usage and human review stay bounded by use case.
Executive memo and evidence log keep start, stop and rescope decisions traceable.
Technical proof
Data classes, roles, model usage and human review separated by use case
The material is designed so leadership, IT, risk and business owners can discuss the same decision with concrete owner, data and review assumptions.
Approvals before tooling or pilot expansion.
Transferability
- Governance model aligned across business, risk, compliance and IT
- Sensitivity classes for data and model usage operationalized
- Decision memo created for the next budget and pilot approval
This story fits when
This pattern fits when business teams want to use LLMs, but risk, compliance and IT need reliable approvals, data classes and ownership.
Review AI Consulting
