How can companies implement AI governance frameworks?
You don’t need a 60‑page policy. Start small, center on uses, and scale controls with risk.
When this is required
Handling sensitive data, regulated industries, or customer‑facing AI outputs.
Selling to enterprises that request governance evidence.
Preparation checklist
One‑pager AI policy (scope, roles, no‑go data, approvals).
Draft use‑case register template (title, owner, data, model/tool, risk tier, controls).
Decide “high‑risk” triggers (e.g., profiling, legal effects, minors’ data).
Step‑by‑step process
Inventory uses: Collect where AI is already used (engineering, support, marketing).
Risk‑tier them: Low = internal productivity; High = customer‑impacting decisions.
Assign controls:
Low: training + basic logging.
Medium: domain review + sampling of outputs.
High: human‑in‑the‑loop, evaluation metrics, sign‑off.
Approve vendors/models: Add a simple intake with privacy/security checks.
Evidence: Centralize logs, reviews, sign‑offs; schedule a quarterly review.
Communicate: Train staff; publish a short external statement.
Documentation & approvals
Register entries (one per use).
Exceptions (who approved, why, expiry date).
Annual policy review; quarterly register sweep.
Mitigations & follow‑up
If issues surface, capture them, fix fast, and update the register and training.
Templates & tools
Use a spreadsheet or ticket type in your existing work tracker; avoid heavy software at first.
Next steps
Pilot with two high‑impact uses; expand to the rest.