AI governance becomes practical when it has owners, policies, reviews, and metrics. Without an operating model, governance turns into scattered advice that nobody owns.
Operating Model Map
| Layer | Owner | Output |
|---|---|---|
| Business value | Business lead | Use case portfolio and ROI |
| Data and security | IT/security lead | Access rules and approved tools |
| Legal/compliance | Risk owner | Policy and review requirements |
| Operations | Workflow owner | Process changes and escalation |
| Measurement | Analytics owner | Usage, quality, risk, value metrics |
Review Rhythm
| Cadence | Review |
|---|---|
| Weekly | Pilot issues and blockers |
| Monthly | Usage, quality, risk events |
| Quarterly | Access, vendors, policy updates |
| Annually | Governance model and strategic bets |
Minimum Policy Set
- Approved AI tools.
- Prohibited data inputs.
- Human-review requirements.
- Source and citation rules.
- Incident reporting path.
- Vendor review checklist.
How to Use This
Use this as a starting model for leadership meetings or AI steering groups. Keep it lightweight at first.
Bottom Line
Good AI governance is not a document. It is a repeatable operating rhythm with named owners and visible metrics.