#Quality Control

AI output review often fails not because reviewers are careless, but because no one owns the definition of acceptable quality. Learn how unclear standards create inconsistent approvals, hidden risk, and weak accountability.

Many teams add human review to AI workflows and assume that is enough. In practice, review often fails when nobody defines what good output looks like, who approves exceptions, and how decisions should be measured.

AI output review often fails for a simple reason: teams ask people to judge answers without defining what good looks like. Here is why missing standards create inconsistent reviews, rework, and security risk, and how to fix it.