Practice area
AI governance
Build the governance layer that lets an organization adopt AI systems deliberately: who approves, who supervises, what is documented, and what is refused.
Scope
What the engagement covers.
- System inventory and use case classification by risk
- Approval and escalation pathways for new deployments
- Human supervision requirements by use case
- Vendor and model diligence criteria
- Disclosure obligations to clients, regulators, and workforce
Outputs
What you hold at the end.
- AI governance policy and supporting procedures
- Use case register with risk classification and supervision requirements
- Approval workflow with named accountable roles
- Training and competency baseline for affected teams
Frameworks
What the work is measured against.
Instruments are named rather than gestured at, because that is what makes a programme auditable.
- EU AI Act
- NIST AI Risk Management Framework
- ISO/IEC 42001
- OECD AI Principles
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