When AI Goes Wrong, Nobody Knows Who to Blame. That Is the Real Governance Crisis. A financial services firm deploys an AI model for underwriting. It is accurate. It is fast. Six months later, a regulatory audit reveals the model has been systematically denying claims at a higher rate for certain zip codes. Not because anyone programmed it to. Because the training data had a historical bias nobody checked for. The model passed every internal test. Nobody audited what it was actually doing in production. This is not a hypothetical. Variants of this story are playing out across insurance, banking, healthcare, and government procurement right now. And the gap at the center of every one of them is the same: organizations are deploying AI without any structured system for evaluating whether it is working the way they think it is, and whether it is working fairly. That gap has a name. It is called the absence of AI governance. And it is becoming one of the most consequential operational risk...