Useful AI makes its evidence, assumptions and exceptions easy to inspect.
We think the most expensive AI output can be the one that looks finished too early. The sentences read well. The chart looks ready for the board. Then someone asks where a number came from, and the person who was supposed to save time starts rebuilding the work. As a leader, you inherit that uncertainty at precisely the moment you need to make a decision.
We would put the reviewer in the brief from the beginning. What will they need to see to accept the work? Where should an unresolved question appear? Who can decide whether an exception is material? Those choices shape the product as much as the prompt does.
Design for the reviewer
A useful deliverable should help its reviewer. The figures should be traceable to identifiable inputs. Adjustments should be visible. Assumptions should be separated from observations. Exceptions should be collected where they can be resolved. These are design choices that can be made before the first report is generated.
Take an illustrative variance commentary. A sentence saying that costs increased because of supplier inflation may sound reasonable. The underlying records might show a higher price, a change in quantity, a different supplier or a timing difference. A reviewable output would show the movement and the evidence supporting each possible explanation. It would leave the conclusion open where the data does not settle it.
We would put the reviewer in the brief from the beginning.
This approach changes the brief given to AI. Instead of asking only for a polished answer, ask for the support required to assess it. What source rows explain the figure? Which mapping was applied? Which assumption is doing the most work? What information would change the conclusion? The answer becomes more useful when its limits are visible.
Give review an owner
Our standard is that an honest gap should be easier to find than a confident guess. We want the workflow to tell your team when the source does not support the explanation. That gives a responsible person somewhere useful to apply judgment, rather than asking them to distrust every sentence equally.
The right depth of review depends on the consequence of error. A draft meeting outline and a payment recommendation do not deserve identical controls. A sensible process considers what could happen if an error passes through, whether the action can be reversed and how quickly a problem would be noticed. That judgment should influence the workflow from the start.
Teams can measure review rather than merely complain about it. Record where reviewers spend time, which exceptions recur and what causes an output to be rejected. Those observations create a practical improvement agenda. Better input definitions or a clearer reconciliation may save more time than changing the model.
This is an optimistic way to approach AI. It assumes the technology can contribute while recognising that professional judgment deserves a well-designed interface. People become more willing to use a workflow when it helps them fulfil their responsibility. Trust grows through repeated, inspectable experience.
Before you approve the next AI project, ask who will accept its output and what they will need in front of them. We would welcome that challenge on our own work. If the answer is simply that someone will check it, the review still needs designing.
Examples in this essay are illustrative, rather than accounts of client engagements.