To overcome resistance to AI in critical fields like healthcare, position it first as a supplement, not a replacement. By providing AI-generated summaries that still require clinical review, organizations can demonstrate value and build trust, making clinicians see AI as a tool that frees them for high-value work.
For high-stakes decisions like utilization management, validate an AI model by having it run alongside the existing human process. The AI renders a decision in parallel with the medical director, allowing the organization to confirm alignment and build confidence before “shifting left” to autonomous workflows.
The push towards agentic AI in healthcare isn't just about efficiency. It's a direct response to compounding crises: an aging population with more chronic illnesses, severe clinician burnout, and tightening regulatory SLAs. These factors make traditional, human-centric care management unscalable.
Proving the ROI of clinical AI can take years if based solely on patient outcomes. Instead, focus on early, measurable operational wins that are known proxies for better care. Track metrics like increased clinician capacity and higher patient engagement rates to prove the system's value and build momentum.