AI can sound finished before the work is actually finished.
A response can be grammatically strong, well structured and convincing while still containing a wrong number, missing fact, unsupported assumption or unsuitable recommendation.
That is why workplace AI should usually be treated as an intermediate output: something that helps move the task forward, but still needs the level of human checking appropriate to the situation.
The better the output looks, the easier it can be to overlook hidden errors. Review the evidence, not just the presentation.
Use a repeatable review instead of relying on a quick glance.
Facts
Check names, dates, quantities, references, calculations and other factual details against the source information.
Completeness
Confirm that important context, requirements, exceptions and unresolved points have not been omitted.
Assumptions
Identify anything AI has inferred or invented that is not directly supported by the available evidence.
Logic
Check whether the conclusion actually follows from the facts and whether any important reasoning step is missing.
Communication
Review tone, wording, audience, clarity and whether the result could be misunderstood.
Authority
Confirm whether the output is only a draft or recommendation, and who has responsibility for the final decision or action.
Not every AI output needs the same amount of checking.
Review effort should increase with the consequence of being wrong. A low-risk draft and a decision affecting money or operations should not be treated the same way.
Low-risk internal drafts
Simple rewriting, brainstorming, formatting and non-critical internal content.
Quick human check for clarity, obvious errors and suitability before use.
Operational and external work
Reports, customer or supplier communication, analysis summaries and recommendations.
Verify key facts, source information, assumptions, format and required actions.
Higher-consequence work
Important financial, safety, legal, employment or operational decisions.
Use qualified human expertise, organisational controls and formal approval where required.
A person may verify that an AI-generated document is accurate without having authority to approve the business action described in that document.
Some signals should trigger a closer review.
When AI makes a statement that matters, ask yourself: “What evidence would I need to confirm this without the AI?”
Review an AI-generated stock discrepancy summary.
Imagine a warehouse system shows 2,160 units while two physical counts confirm 2,131 units. The verified difference is 29 units. AI is asked to draft a management summary from the investigation notes.
Verify the quantities
System stock is 2,160 units and two physical counts confirm 2,131 units. The verified difference is 29 units.
Separate completed checks from assumptions
Receiving, outbound movements and adjustment records were reviewed. The information available does not yet confirm the root cause.
Check what the model added
If the draft says the shortage was caused by a missed dispatch posting, remove or rewrite that statement unless the records prove it.
Review the management summary
Make sure the report clearly distinguishes verified facts, investigation completed, unresolved issues and recommended next checks.
Keep the final decision with the authorised person
Any inventory adjustment, escalation or corrective action should follow the organisation's approval process.
“The 29-unit shortage was caused by an outbound transaction that was not posted correctly.”
“A verified shortage of 29 units remains unresolved after review of the available receiving, outbound and adjustment records. Further investigation is required before confirming the root cause.”
The second version keeps verified facts separate from an unconfirmed explanation. That distinction is one of the most important parts of reviewing AI-generated operational content.
Before using the output, answer six questions.
If an important statement cannot be verified and the consequence of being wrong is meaningful, do not treat the output as final. Get the missing evidence, expertise or approval first.
Quality comes from the combination of AI speed and human control.
AI can reduce drafting and information-handling effort, but the person reviewing the output is still responsible for checking whether it is accurate, appropriate and ready for the next step.
Use AI for the defined task.
Check against the evidence.
Remove errors and assumptions.
Use the right human authority.