Organizations have invested $30–40 billion in AI. According to MIT (2025), 95% produced no measurable return. Only 5% made it into daily workflows.

If you're reading this, you're trying not to become that statistic—or you already are.

The problem isn't bad technology.

Most AI failures don't begin at scale. They begin during pilots, when early success builds confidence without revealing risks that surface once AI enters real workflows and decisions under pressure.

AI initiatives fail because organizations focus on building and buying tools while overlooking how AI behaves in daily clinical and operational work. What looks controlled during pilots can create new risks: unsafe decisions that go unchallenged, front-line concerns that stop surfacing, accountability gaps, and missed value that doesn't appear on dashboards until it's too late.

Leaders believe they are governing AI through pilots, metrics, and oversight committees—yet problems accumulate. Decisions move faster, scrutiny weakens, and risk scales while leadership assumes control.

This book explains why.

Leading AI Adoption in Healthcare shows why AI initiatives that look successful on paper fail in practice—and what leaders must do differently to prevent unintended consequences before they surface as patient safety events, lawsuits, or workforce turnover.

Drawing on healthcare leadership and operational science, the book introduces practical frameworks for governing AI where consequences land: in workflows, team decisions, and moments where human judgment matters.

Written for executives with real accountability and limited time, this concise guide focuses on decisions that protect patients, retain staff, and preserve trust—without jargon or hype.

This book is not about slowing innovation. It is about making adoption real—so AI delivers durable performance and ROI.

If you are responsible for AI outcomes and risk, this book will change what you pay attention to—and what you require before you scale.