Organizations do not need an overly complex governance structure before they can use AI responsibly. They do need a minimum set of mechanisms that make use cases visible, assign decisions, apply proportionate controls, and identify material change after deployment.
Create a use-case inventory
The organization needs to know where AI is being considered or used, who owns each use case, which data and vendors are involved, what decisions or actions it influences, and whether external users are affected.
Establish a proportionate risk tier
Not every AI use requires the same review. Risk tiering should consider impact, autonomy, data sensitivity, user population, decision criticality, legal or contractual obligations, explainability, and the ability to detect or reverse harm.
Define approval and accountability
Each tier should have clear approval authorities, required evidence, control expectations, exceptions, and escalation paths. Business ownership should remain explicit even where technology teams or vendors provide the model.
Set minimum control requirements
Common requirements include permitted data, access control, vendor review, testing, human oversight, logging, user disclosure, output validation, security, incident handling, and change management.
Monitor the deployed use case
Governance continues after approval. Performance, incidents, complaints, material model or vendor changes, control exceptions, and changes to data or purpose should trigger review.
What to carry into the next decision.
- Inventory AI use cases before attempting enterprise-wide control design.
- Apply risk tiers so review effort matches potential impact.
- Keep business ownership and approval authority explicit.
- Define monitoring and material-change triggers before deployment.
