Throw AI at the Wall and See What Sticks

Many healthcare organizations initially adopted AI with a “see what sticks” mentality, implementing tools without strategy or clear outcomes. Well, the experimental phase is ending. Executives now emphasize intentional, disciplined AI adoption guided by governance, clear ROI metrics, and defined timelines. The focus has shifted from hype to accountability: only scaling pilots that demonstrate measurable impact. Leaders are becoming more financially prudent, recognizing that “free” pilots carry hidden costs and risks. AI success now depends on the quality and appropriate use of data, transparent evaluation, and trust between vendors and providers. Transparency in how models are trained, monitored, and validated is essential to building that trust. Health systems expect vendors to provide explainability, audit trails, and ongoing performance validation, ensuring AI tools are not just accurate but safe, ethical, and aligned with patient care.