When a colleague says they have checked an AI-generated answer, what do you understand that to mean?
Perhaps they read it carefully and corrected the wording. Perhaps they compared it with the original information, noticed a missing condition and changed the conclusion. Those reviews could leave behind equally tidy documents. The work behind them is quite different.
Leaders need a way to see that difference. If people are expected to review AI-assisted work, we need to help them develop the judgement the task requires and give them an opportunity to demonstrate it.
A completed training session tells us someone has taken part. Confidence tells us how they feel. Neither, on its own, shows whether they can recognise a plausible answer that should not be used.
Start with the work the answer must serve
It helps to establish the requirements before reading the AI response. What will someone do with the result? Which conditions must remain true for it to be useful? What would make it unacceptable?
Consider a hypothetical comparison of two ways to organise a recurring task. The draft clearly explains both options and recommends the simpler one. Every statement about that option is accurate. However, the recommendation overlooks a requirement that the work must continue when a particular resource is unavailable.
Checking individual statements would leave that problem untouched. The reviewer needs to compare the answer with the full task, including the condition the answer left out.
That requires knowledge of the work. A person can be capable with an AI tool and still lack the experience to recognise a consequential omission in an unfamiliar role. Review responsibilities should reflect what people know and where they need help.
Follow the evidence beyond the sentence
A useful reviewer can show where an important claim came from and whether the source supports what the draft says.
An included reference gives them somewhere to start. They still need to open it, find the relevant information and check that it applies. A source may describe an earlier version of the process, include an exception or support a much narrower conclusion.
This is also where polished language deserves attention. Words such as “will” and “always” can turn a limited observation into a promise the evidence cannot support. A clear explanation can link accurate facts through an assumption that has never been checked.
Ask the reviewer to explain how the evidence supports the proposed conclusion. Where has interpretation entered the answer? Is an assumption reasonable for this task, and does the person receiving the work need to see it?
Asking the AI to explain itself again may help expose an issue, but another fluent response cannot settle it. The check still needs a reliable basis outside the generated answer.
Knowing the permitted use matters too. Someone might be able to verify the content while overlooking that the information should never have been entered into that tool. Review competence includes recognising the relevant boundaries and knowing when a concern needs to be raised.
Try a small coaching exercise
Choose a familiar, low-risk task and set aside half an hour with a colleague. Use fictional or appropriately approved material, with the original instructions and sources available. Keep the exercise separate from live work.
Prepare a short draft that is mostly useful. Deliberately leave out one important constraint and include a conclusion that the supplied evidence does not support. Tell the colleague that the draft needs review; there is no need to turn the exercise into a surprise test.
Ask them to mark what they would keep, change or query, and to point to the reason for each important decision. Give them the same access to information and help that they should have in the real task.
Watch where they begin. Do they establish what the work is for? Can they locate the evidence behind a claim? Do they notice what is absent as well as what is wrong? Their explanation should make the review understandable to another person.
Then introduce a harder case. For example, provide two fictional sources that disagree, with no clear indication of which takes precedence. A sound response may be to pause the affected conclusion and ask the appropriate person for clarification. Inventing certainty to finish the exercise would be a concern.
Include a sound example in later practice as well. Reviewers need to recognise when work meets the agreed standard, rather than feeling obliged to find a fault in every answer.
Make uncertainty safe to report
Discuss the exercise together. Focus on the decisions that mattered and one area to practise next. A single exercise gives a useful starting point; repeated observations across realistic tasks provide a stronger picture of what someone can handle independently.
Pay attention to the conditions around the person. If they cannot access the source, have no time to check it or do not know who can resolve a conflict, more training alone will leave the difficulty in place.
Leaders also influence what people feel able to say. If every pause is treated as a lack of confidence, a reviewer may be reluctant to identify uncertainty. Respond constructively when someone explains what they cannot verify and what help they need.
The standard should be achievable and suited to the consequences of the work. People need clear expectations about the checks required, the limits of their responsibility and when specialist judgement is necessary. Asking someone to guarantee that an answer contains no possible error creates a burden they cannot meet.
A useful next step is to sit alongside one colleague and review a piece of work together. Listen to how they reach their decisions. That conversation can reveal where they are ready to act and where a little more support would make the work safer.