AI metrics for executives should cover value, cost, adoption, time to value, quality, delivery, capability, proficiency, incidents, and strategic outcomes.

Measure AI transformation success through outcomes, adoption, capability, controlled risk, repeatable delivery, workforce proficiency, and portfolio discipline.

Measure AI productivity gains across the full workflow before and after adoption, including quality, errors, rework, human review, and capacity use.

Build an AI business case with a problem, baseline, workflow change, benefits, full costs, risks, alternatives, dependencies, adoption, stages, and measures.

Set an AI investment budget using opportunity, use-case economics, readiness, risk, size, and the cost of data, technology, skills, controls, and change.

Identify high-value AI use cases by finding material problems and testing measurable value, feasibility, information readiness, adoption, ownership, and risk.

Measure AI business value through an owned outcome, credible baseline, adoption evidence, full cost, operational performance, quality, and risk.

Prioritize AI investments using strategic fit, problem size, value, feasibility, data, adoption, time to value, risk, scale, and capability created.