Your AI dashboard won’t show you the difference between “got it right” and “missed everything.”
Both show up as the exact same entry: Approved.
I reviewed 24 companies with AI-driven workflows. Every one of them leaned on human review to sell trust. A high approval rate is usually marketed as a great performing system.
Think about what happens inside a typical review queue. An AI drafts a clinical summary or extracts clauses from fifty contracts. A human reviewer sits in front of the screen and clicks “Approve.”
When the dashboard shows a 98% approval rate with zero edits, teams celebrate.
But entries approved with no edits are an ambiguous signal. It can mean the AI output was perfect. Or, it can mean the reviewer was on their fortieth document of the afternoon and anchored on the AI’s “usually right” output and confident formatting.
What’s missing is reviewer catch rate. Out of every 100 AI errors that appear on screen, how many does the human actually intercept?
You can’t be calculated from approval clicks alone. approval logs only tell you what the reviewer flagged. It doesn’t tell you what they missed.
Without a real catch rate on the dashboard, what’s the earliest warning sign you’ve seen that a human review began rubber-stamping AI output?