PRACTICAL FIELD GUIDE
AI Governance
How can organizations govern AI without stopping innovation?
The short answer
Effective AI governance creates clear lanes for experimentation, review, deployment, and escalation. It reduces uncertainty so teams can move faster inside known boundaries.
Four operating priorities
Identity and ownership for every AI system
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
Risk tiers with matching controls
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
Human oversight for consequential decisions
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
Monitoring, incident response, and retirement
Make this visible in the operating model, assign ownership, and connect it to evidence the organization can review.
A practical starting sequence
- Name the business outcome and the people affected.
- Map the information, systems, decisions, and permissions involved.
- Choose a contained use case with a measurable result.
- Set the review, escalation, and learning process before launch.
- Scale only after the evidence supports it.