Most AI failures don’t begin at scale — they begin during pilots, when early success creates confidence without revealing the risks that only surface once AI is embedded in real workflows, real teams and real decisions under pressure.
AI initiatives fail because organizations focus on building and buying tools, while overlooking how AI actually behaves once it enters daily clinical and operational work. What looks controlled during pilots often creates new risks over time: unsafe decisions that go unchallenged, front-line concerns that stop surfacing, accountability gaps, and missed value that does not appear on dashboards until it is too late.
Most leaders believe they are governing AI through pilots, performance metrics and oversight committees. And yet problems still accumulate. Decisions get made faster, but with less scrutiny. Risk scales quietly while leadership believes things are under control.
Leading AI Adoption in Healthcare: AI Doesn’t Adopt Itself
Melinda Deholl
$29.99
Echelon Press
On Shelves and Online
132 Pages
