Measuring AI Governance: What Metrics Should You Track?

Why This Matters

Track four key metrics: training completion rate (aim for 100%), incident count (should trend down), policy compliance rate (spot-check quarterly), and inventory coverage (all tools listed).

Under the EU AI Act, having documented AI governance demonstrates that your business takes AI compliance seriously. If regulators or clients ask how you manage AI use, pointing to established practices is far better than starting from scratch.

Essential Metrics

Training completion: what percentage of AI users completed training? Should be 100%. Incident count and trend: how many per quarter? Are they decreasing over time? Policy compliance: are people following the policy? Quarterly spot checks. Inventory coverage: are all tools documented? Periodic team surveys.

More important than absolute numbers is the trend — are things improving?

Advanced Metrics

As governance matures, add: time to resolve incidents (getting faster?), vendor assessment completion, risk register currency, employee confidence surveys, and customer satisfaction with AI-assisted services.

These give a more nuanced picture but aren't needed until basics are covered.

Using Metrics for Improvement

Review quarterly. Look for patterns. If training completion is low, investigate why. If incidents are increasing, dig into types. Use what you learn to adjust training, policies, and processes. Metrics are only valuable if they drive action.

This article is for informational purposes only and does not constitute legal advice. Regulatory requirements change frequently — verify current rules with official sources. Built by Sawai Gyoseishoshi Office, Hiroshima, Japan.