Six failure modes I've seen across every AI deployment I've audited. Each one is a specific way human oversight silently degrades in production. Tap through them, then flip a card for what to look for in your own system.
A human sits in the review loop. Nobody checks whether they are actually looking. Compliance documentation says oversight exists. The error rate says otherwise.
Review volume grows with the AI's output. Human capacity does not. Quality degrades quietly over a shift, then over a quarter.
Sustainable
Add checkpoints everywhere and humans stop reading them. Too many gates produces the same failure as no gates: automatic approval.
Familiarity raises approval rates regardless of actual reliability. Trust grows on autopilot while accuracy stays flat.
Errors nobody catches do not disappear. They accumulate in logs nobody reads, until one surfaces as an incident.
You cannot govern what you cannot measure. Most organizations measure AI capability constantly. They measure nothing about oversight quality.