- Leadership and economic pressures are pushing CIOs to approve agentic AI before accountability, liability, and clinical governance approaches are settled.
- Healthcare AI governance is internally fragmented: Committees, risk owners, and clinical leaders rarely share one rubric for what means to advance a use case to the pilot stage.
- Platform position (EHR-native, EHR-adjacent, overlay, or internal) materially changes what controls the organization has and changes the approach to the use case evaluation process.
- Agentic AI adoption pressure is outpacing governance maturity, leaving CIOs without a defensible way to say yes, no, or not yet.
Our Advice
Critical Insight
- The unit of approval is not the use case or the vendor, but rather the specific agentic behavior, in a named workflow, on a named platform. Providers need to evaluate and approve use cases at this level of granularity.
- Workflow fit, downstream exposure, and containment are screening dimensions, helping determine which use cases don’t belong under consideration.
- Platform position determines risk and triage treatment. EHR-native, adjacent, overlay, and internal deployments each carry different audit, override, and rollback profiles, and each demands a different governance posture.
Impact and Result
- A shared understanding of agentic AI concepts, vocabulary, and practical fit for healthcare providers.
- A structured set of candidate use cases mapped to business capabilities and screened for structural fit, workflow fit, downstream exposure, containment before any priority scoring exercises is done.
- A scored, visually represented agentic AI portfolio that supports confident, defensible prioritization.