Ask AI for the receipts. Then check them.

Speed is useful. Traceability makes it usable.

An AI-generated explanation can sound complete before the work is complete. Before a summary, reconciliation narrative, or process recommendation moves into a business decision, make its basis visible. Ask what information was supplied, which statements are supported by that information, and where the output goes beyond it. Use only tools and data approved by your organization.

Ask for three things: evidence, inference, gaps.

For each material conclusion, ask the tool to identify the supporting passage in the source material, the assumptions it made, and the information it still needs. Keep these categories separate. A plausible interpretation is not the same as an observed fact. A missing source is a reason to investigate, not a reason to accept confident wording.

Check the source yourself.

An AI tool can invent a citation, misread a table, or point to a real document that does not support its conclusion. Open the original source and confirm the passage, date, units, and surrounding context. Recalculate material figures using an appropriate system. Assign a person who understands the work to resolve discrepancies and approve the result.

Build the review into the workflow.

Decide what can be drafted with AI, what must be independently checked, and who owns the final output. Record exceptions and route unresolved issues to a human reviewer. Start with a narrow, repeatable use case so your team can evaluate quality alongside time saved. The staffing question is often as much about process ownership and review capacity as it is about the tool.

Turn the questions into a scope.

If this is on your desk, we can discuss the business need, the experience required, and the appropriate engagement model through Robert Half.