Many organisations start AI with pilots and informal approvals. That works until AI becomes a shared capability: multiple teams, multiple vendors, more sensitive data, and higher expectations from boards and regulators. At that point, decisions need a home.
An AI governance council is a practical mechanism for decision-making. Done well, it accelerates delivery by removing ambiguity and standardising controls. Done poorly, it becomes a bottleneck.
Start with decision rights, not membership
The council exists to make a small set of repeatable decisions:
- Risk tiering. Which use cases are low/medium/high risk and what controls apply (see risk appetite).
- Approved patterns. Which architectures and guardrails are standard (see control tower and enterprise AI architecture).
- Data boundaries. What data can be used, where it can be processed, and how it is retained.
- Exceptions. When to allow deviations, and what evidence is required.
Once decision rights are clear, membership becomes obvious: the people who can approve, fund, or accept risk.
Keep the cadence lightweight and predictable
A common pattern is a weekly 30-minute triage plus a monthly deep-dive:
- Triage. New use cases, exceptions, and release approvals for higher-risk changes.
- Deep-dive. Trends, incidents, audit readiness, and strategic vendor decisions.
Predictability matters: teams should know when decisions will be made and what evidence is required.
Standardise the artefacts that make decisions fast
The council should require a small set of reusable artefacts:
- Use case one-pager. Purpose, users, data, and automation level.
- Risk and controls matrix. Controls by tier (see risk and controls and policy layering).
- Operational readiness. SLOs, incident response, telemetry (see SLOs and observability).
- Evidence pack. Evaluation results, red team outcomes, and change history (see compliance audits and governance artefacts).
Make outcomes measurable
Governance should improve delivery, not just reduce risk. Measure:
- Lead time from proposal to approval (by risk tier).
- Incident rate and severity trends.
- Reuse of standard patterns vs bespoke builds.
- Audit readiness: how quickly evidence can be produced.
The goal is a council that turns AI into a managed capability: faster decisions, clearer accountability, and fewer surprises.