AI can generate a plausible-looking wrong answer faster than a human can produce a correct one. That single fact changes what “review” has to mean in your business.
The failure mode is subtle. Most organizations treat AI-generated output the same way they treat human-generated output: a colleague reviews it, catches obvious errors, waves it through. That process works for humans because humans have consistent failure patterns — they typo, they get tired, they occasionally misread a number. Reviewers develop instincts for those errors.
AI errors are different. AI produces fluent, confident, well-structured wrong answers. It gets facts wrong while sounding certain. It makes up sources that don’t exist. It sometimes calculates correctly and sometimes doesn’t, with no visible tell about which time is which. Human reviewers, trained on human errors, miss AI errors at a much higher rate than they realize.
The organizations that handle this well change their review discipline in specific ways.
First, review is required, not optional. A common failure pattern is “we let AI-generated content ship because it looked right.” That’s not review; that’s abdication. Every piece of AI output that reaches a customer, a financial system, or a decision-maker gets reviewed by a human who is empowered to reject it.
Second, review is done by someone accountable for the outcome. Not the person who made the AI request — they have motivated reasoning. A different set of eyes, ideally someone whose job depends on the output being correct.
Third, review looks for specific AI failure patterns, not general “does this look OK.” Specifically: are the numbers real? Are the sources real? Is the tone right for this audience? Did the AI answer the question that was asked, or a subtly different one it substituted?
Fourth, when review catches an error, someone documents it. Not to punish anyone — to build a shared understanding of where this particular AI system fails. That documentation is worth more than the vendor’s promise about accuracy.
I’ve watched teams that started with “review is a formality” evolve, over six months of near-misses, into “review is the reason we can use AI at all.” The teams that skipped the review discipline usually caught their first serious error the hard way — publicly.
The right question to ask about your business is not “are we using AI.” That answer is almost certainly yes, whether officially or not. The right question is “do we review AI output with the discipline the technology demands?” Question 7 of the diagnostic asks this. Answer honestly.