Why AI Failures Are Rarely Model Problems?

When an AI-powered workflow fails in production, teams often blame accuracy, hallucinations, or data quality. In most audits, those are symptoms, not causes. The real failures happen at the boundary between decision, context, and authority. The model did exactly what it was allowed to do, with the context it was given, and without the authority it should have escalated to. A proper AI Audit asks where context is lost, where authority is unclear, and where the system is forced to decide when it shouldn’t. If your post-mortems always end with “we need a better model,” you’re treating governance failures as technical debt. Transformation begins when failure analysis shifts from models to decision architecture.

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