Start with decisions, not documents
An operating model should show who may propose an AI use case, who assesses it, who accepts risk and who monitors it in operation. Policy matters, but clear decision rights are what make governance usable.
Use proportionate risk tiers
Not every use case needs the same review. Classify AI by information sensitivity, consequence, autonomy and affected stakeholders, then match approval, testing and oversight to that risk.
Make human oversight specific
Define what a reviewer must check, what evidence they receive, when they may override an output and when escalation is mandatory. A human in the workflow is not meaningful oversight unless the role is practical.
Connect governance to delivery
Architecture, procurement, privacy, security, testing, adoption and benefits should use the same ownership model. Governance weakens when it sits outside the teams selecting and implementing technology.
Create a repeatable operating rhythm
Maintain an AI register, review incidents and performance, reassess material changes and report significant risks to accountable leaders. The goal is disciplined learning, not one-time approval.
Discuss AI Governance
Turn AI principles into proportionate controls, accountable decisions and a practical implementation roadmap.