How to review AI-assisted workplace deliverables

Start with the artifact's purpose

Before proofreading, identify the decision, action or understanding the artifact must support. The same sentence can be harmless in a brainstorm and material in a policy, forecast or customer commitment. Purpose determines which errors deserve the most attention.

NIST's AI Risk Management Framework makes context central to risk mapping and measurement. Applied to knowledge work, that means the reviewer should write down the audience, constraints, evidence standard and consequences before evaluating the draft.

A useful review question is not simply whether the output sounds plausible. Ask whether the artifact contains everything its recipient needs, whether it distinguishes facts from assumptions and whether its form makes the intended action clear.

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Match the checks to the format

Different artifacts fail in different ways. Spreadsheets require formula, unit and reconciliation checks. Research summaries require source fit and coverage checks. Recommendations require alternatives and trade-offs. External communications require factual, policy and commitment review.

For calculations, reproduce material figures from the underlying inputs and inspect periods, denominators, signs and rounding. For written synthesis, trace decision-changing claims to their sources and confirm that the source applies to the stated population, jurisdiction and date.

For recommendations, identify what evidence would reverse the conclusion and which uncertainty remains unresolved. For communications, confirm that corrections made during analysis also appear in the subject line, executive summary, tables, attachments and requested action.

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Preserve evidence lineage without unnecessary surveillance

Evidence lineage records where material inputs came from, how calculations were performed and which corrections changed the delivered result. It supports review and learning, but it should capture only what is necessary for the stated purpose and respect privacy and organizational boundaries.

A lightweight evidence record can list the source used, the calculation reproduced, the assumption tested and the final correction. This is more useful than retaining every intermediate token when the review question concerns a small set of material decisions.

NIST's ARIA programme emphasizes evaluation in realistic human-AI contexts. That perspective supports examining the delivered artifact together with relevant workflow evidence, while recognizing that neither the final document nor an event log alone reveals the entire reasoning process.

• Define the artifact's intended decision or action.
• Prioritize checks by materiality.
• Reproduce important calculations.
• Trace important claims to fitting sources.
• Confirm every correction reached the delivered version.

Sources: [2] [1]