AI Governance Operating Model
Most organizations don't need more AI tools. They need a way to make AI decisions that is repeatable, documented, and defensible.
An AI governance operating model answers the questions that keep showing up anyway: Who decides? Based on what criteria? What must be documented? How do we approve use cases without blocking the business? How do we monitor drift and vendor risk over time?
What's Included
1) Decision Flow (intake → review → approve → monitor)
A practical pipeline for AI use cases:
- What qualifies as low/medium/high risk
- What must be reviewed and by whom
- What approval and rejection look like
2) Roles & Responsibilities (RACI)
- Business owner accountability
- IT/security responsibilities
- Legal/Compliance review touchpoints
- Optional: an "AI Council" model that is practical and flexible
3) Control Set (minimum viable product)
- Acceptable use and data handling rules
- Vendor/tool evaluation criteria
- Human review and escalation expectations
- Logging/evidence guidance
4) Documentation and Evidence
Defensibility.
5) Adoption and Change Guidance (optional)
Training cadence, comms, and measurements so adoption sticks.
What You Get
- AI governance charter + RACI
- Use-case intake template + risk triage rubric
- Policy stack outline (practical, right-sized)
- Vendor evaluation scorecard (optional add-on)
- Measurement plan for adoption
When You Need This
- You're moving beyond pilots into broad adoption
- You have multiple tools/vendors, and no consistent decision flow
- Legal/Compliance/Privacy wants earlier visibility and to understand implications of tech decisions
- You're seeing "shadow AI" because the approved path is unclear
Next Steps
Starting from zero? AI Discovery & Risk Scan
Copilot rollout specifically? M365 Copilot Readiness
Want ongoing support? We can scope a lightweight governance retainer after the operating model is in place.