AI Governance
AI Governance Policy vs Operating Model
Updated 2026-07-07
AI policy states intent. AI operating models define how use cases are approved, controlled, monitored and escalated.
Regulated organizations often start with AI principles or a policy statement. That is a useful beginning, but it does not answer the operational questions that arise when teams want to deploy actual AI use cases.
An AI governance operating model defines intake, risk classification, approval forums, mandatory controls, monitoring expectations, incident handling and ownership across business, risk, data and technology teams.
The difference matters because AI risk lives in use cases. Without operating routines, a policy may look mature while actual adoption remains uneven, undocumented or hard to defend.
Key takeaways
- AI policy needs an operating model to become enforceable.
- Use-case intake is the practical starting point for responsible AI.
- Monitoring and review are part of governance, not postscript activity.