When AI is wrong (and when humans are wrong about being right)

A native speaker reviewing AI output sometimes flags issues that are not real — the AI is correct, and the reviewer's instinct is wrong. Both kinds of error happen and both are expensive. Knowing how to tell them apart is the discipline that protects both quality and team time.

When AI is wrong and the reviewer is right

When the reviewer is wrong and the AI is right

How to tell which is which

Three diagnostic questions when a reviewer flags AI output. First: is the issue grammatically wrong or just stylistically different? If just different, the AI is probably fine. Second: does the issue violate the brand-voice document specifically? If not in the doc, the reviewer is applying personal preference. Third: would a different native speaker also flag this issue? If you have access to a second reviewer, a 30-second second opinion often resolves the question.

The escalation process

When there is a genuine disagreement, the call goes to the brand-voice owner (usually the marketing director or the GM). They make the binary decision: AI is right, or the reviewer is right. The decision gets logged in the brand-voice document as a clarification ("we use X not Y; example: ...").

Over 90 days of this discipline, the brand-voice document becomes the arbiter and disagreements drop sharply. The team has a shared reference for what "correct" actually means at this property, and AI output gets evaluated against a standard rather than against any individual reviewer's preferences.