The examiner question every manager should learn to ask
Banking supervision taught me to ask for the evidence behind a particular result. A documented process was useful, but I also needed to see how it applied to the transaction under review. I want managers to bring that habit to AI answers: choose a claim, inspect what supports it and decide whether it is sufficient for the action being considered. Training should give them practice doing that with their own work.
Managers do not need to become model engineers. They need a repeatable way to ask what an answer covers, where it came from, and whether it is sufficient for the decision in front of them. Train the habit, not the tool, so the judgment survives the next interface change or model upgrade.
Start with the decision, then check scope
Before anyone looks at the answer, ask the manager to state the action it would support. Contacting a list of accounts, changing a forecast and approving spend require very different evidence. Have participants write one sentence: "If this is correct, I will..." That sentence exposes questions that were interesting but never connected to a decision.
Then apply three checks to every exercise: who or what is included, which time boundary applies, and what the key measure means. "Revenue rose" is incomplete until you know whether it means bookings, invoiced sales, recognized revenue or collected cash. In credit and lending, "delinquent" can depend on days past due, charge-off treatment and whether modified loans are excluded. Managers should be able to say what remains unknown in plain language without reading a query.
Trace one number back to evidence
Pick a small part of the answer and follow it to a trusted source with the owner of that data. If the system reports overdue invoices, look at the aging date, excluded statuses and credit treatment with someone from finance. Do not ask trainees to validate every row. Teach them to find the point where an assumption could change the decision.
Include answers that should not be accepted: a refusal because the period was missing, a request outside the person's access, a question that genuinely needs clarification. A refusal that names the gap moves work forward. Rephrasing until the system returns something is the habit to break. Managers must learn the difference between resolving honest ambiguity and pressuring a tool toward the conclusion they wanted.
Practice disagreement without blame
Stage an exercise where the AI answer conflicts with the team's long-standing spreadsheet. Neither wins by default. Compare definitions, extraction dates, filters and rounding first. In my experience across data warehouse modernization programs, the old report is often encoding a convention nobody wrote down. Surfacing it is a gift to data governance, not an embarrassment.
Record the resolution as a reusable note owned by the business team. The learning outcome is a method for resolving disagreement, not a slogan to trust the technology or trust the incumbent report.
Match review effort to consequences
Give managers three decisions with different stakes: drafting a meeting agenda, prioritizing a review list, and authorizing a customer-facing commitment. Ask them to name the minimum reviewer and evidence for each. A reversible internal draft may need a glance. An external commitment still runs through existing approval authority.
This is where I disagree with rollout plans built around one uniform AI literacy course. Lending managers, treasury staff and healthcare revenue cycle leaders need practice with different decisions and consequences. Choose exercises from their own work and have the relevant process owner review them. Keep the authority boundary explicit: an answer cannot grant permission to take an action the manager could not otherwise approve.
Assess the skill on an unseen case
End with a new case, not a replay of the demonstration. Ask each manager to identify two assumptions, one evidence check and a safe next action, scored against a simple rubric agreed beforehand. Repeat a few weeks later using a real workflow with sensitive details removed.
If managers can explain their review in their own words, the training worked. That is also the standard an audit committee or examiner will eventually apply. A tailored enterprise AI workshop can build these exercises around the decisions your leadership team actually makes.