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A governance guide for responding when an AI-enabled process behaves unexpectedly, creates risk, or produces a potentially harmful outcome.
AI governance works when responsibilities, risk decisions, inventories, review points, and escalation paths are clear enough to guide everyday work.
Moving an AI workflow into normal operations requires ownership, testing, controls, support, measurement, and a plan for change after launch.
Human oversight works best when it is designed around consequences, uncertainty, exceptions, and accountability rather than added as a vague promise after deployment.
Administrative AI can reduce repetitive work in healthcare, but operational automation should not be confused with clinical decision-making.