AI governance is now a foundational requirement. Good governance ensures organizations invest in tools that align with their organizational priorities and manage the clinical and ethical implications in a safe and transparent way.

The need for governance has grown since the beginning of the AI boom. As AI shifts from isolated pilots into core clinical and operational workflows, the scale of impact and potential for risk has expanded. Yet, many health systems still lack robust governance frameworks. Even organizations with established governance programs can struggle to adapt over time as AI risks, regulations, and tools evolve.

AI governance becomes a strategic advantage when implemented well and adapted over time. Strong governance creates consistent pathways for evaluating, approving, and monitoring AI tools. Today, that consistency is essential as models are updated more frequently, more vendors offering AI enter the market, and clinical teams leveraging AI outputs to a greater extent. Robust and disciplined governance reduces uncertainty and is necessary for promising initiatives to sustainably scale across the enterprise.

Apply the four principles below to build a future-ready AI governance framework.