Name the decision
An AI program becomes easier to evaluate when its first question is concrete: which decision, recommendation, or action should improve? Write down who makes that decision today, which facts they use, and what happens when they are wrong.
A customer support answer, a sales lead qualification, and a clinical recommendation carry different consequences. Their owners, evidence, and approval points should reflect those differences.
Set the boundary
Choose one use case and a version of the system. Identify data sources, model routes, tools, affected people, and the highest effect the system may have. State which actions require a person before they happen.
The boundary keeps an assessment from turning into a vague judgment about all AI used by an organization.
Measure what changes
Capture a baseline for work time, error, review effort, and exceptions. Test ordinary and difficult examples. Record what was observed and what remains unknown.
A decision to expand should follow measured performance and an understood exception path. A compelling demonstration is a starting point for that evidence, not a substitute for it.