Are You Measuring AI Accuracy… or AI Dependency?

Accuracy tells you how often the model is right. Dependency tells you what happens when it is wrong. In many organizations, AI quietly shifts from assistant to authority without anyone formally redesigning the decision structure. Teams stop double checking. Managers assume the dashboard is objective. Junior staff hesitate to challenge outputs because the system feels statistically superior. This is how overtrust forms, not through hype but through habit. An AI Transformation Partner must audit reliance patterns: override frequency, human review depth, decision reversal rates, and the speed at which people defer to automation. When dependency grows faster than control mechanisms, risk compounds invisibly. The goal is not to slow adoption but to engineer calibrated trust. If your governance model does not track behavioral drift alongside technical metrics, you are optimizing performance while outsourcing judgment.

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