Objectives: What 'Good AI' Means at Your Company

"We want to use AI responsibly." Lovely sentence. Useless objective.

ISO 42001 asks you to set AI objectives: specific, measurable targets for what your AI management system should achieve. In plain English, you have to define what "good" looks like, in numbers and deadlines, and then check whether you got there.

Vague intentions cannot be managed. Objectives can. Compare:

Vague: "We care about AI fairness." Objective: "By Q3, all customer-facing models complete a bias assessment before deployment, and results are reviewed by the AI owner."

Vague: "We take AI risk seriously." Objective: "100% of high-risk AI use cases have a documented risk assessment, reviewed annually."

See the difference? An objective names what, names when, and can be checked. That checkability is the whole point. A management system runs on evidence, and objectives manufacture evidence.

Keep them few. Three to five objectives beat twenty. Pick the ones that matter most to your risks, write them down, assign an owner and a deadline, and review them like you mean it.

If you cannot measure it, you cannot manage it. So decide what good looks like, then go build it.

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