The speed of AI development is unmatched. As the technology continues to offer more advanced use cases, healthcare leaders face difficult questions: How do you know that an AI capability is ready for use? And how do you ensure it remains reliable as your organization and the broader healthcare ecosystem change?

Answering these questions requires a new level of scrutiny. Healthcare organizations already govern technology through patient safety; software development; and security, regulatory, and contractual controls. These controls remain essential, but AI can behave in ways traditional software doesn’t: the same input can produce different outputs, and performance can shift even when no code has been changed. A capability may perform differently when introduced to a new specialty, patient population, or geography. The data feeding the AI can change. Payer rules can change.

AI governance gives leaders a way to manage that variability: deciding what good performance looks like before release, setting clear thresholds for what can ship, and continuing to watch performance afterward. That’s the difference between documenting how an organization intends to use AI responsibly and building governance into how AI is developed, released, and managed.

Governance in practice starts with deciding how you will judge an AI capability before you even begin to build. Consider evaluation across the following three areas:

How teams measure those areas depends on the capability. A predictive model’s output can be evaluated against what happened, while an AI-generated clinical summary has to be judged against a broader sample and requires an expert review against pre-defined criteria.

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