Measure What Matters: Picking AI Metrics That Mean Something

ISO 42001 asks you to monitor and measure your AI management system. Which raises the obvious question: measure what?

Most organizations start with what is easy: number of AI tools deployed, training completion rates, policies published. These are activity metrics. They tell you the machine is running. They do not tell you it is running well.

Better metrics measure outcomes and risks. How often did human reviewers override the AI's recommendation — and why? How long between a model update and its performance review? What share of high-risk use cases have current risk assessments? How many AI incidents were caught by monitoring versus reported by angry customers?

Here is my rule: only track a metric you would act on. If a number moved badly and nobody would do anything, it is decoration. Every metric should have an owner and a threshold that triggers a conversation.

Keep the set small. Five meaningful metrics, reviewed regularly, will teach you more than fifty vanity numbers nobody reads.

You get what you measure. So measure the things that would keep you up at night, and sleep better knowing someone is watching them.

I explain frameworks; I don't give legal counsel.