Artificial intelligence is rapidly shaping the next phase of healthcare transformation. Yet across hospitals and health systems, the results remain uneven. Predictive models routinely perform well in pilots but fail to deliver sustained clinical or operational impact. The difference between promise and performance no longer lies in algorithm design; it lies in how organizations act on what those algorithms predict.

Why healthcare organizations must treat verification as a core operational discipline, not a procedural checkbox. Through real-world case studies, we show how AI creates invisible failure modes, why LLMs invert the traditional learning curve, and what executives must do to ensure adoption delivers measurable value without exposing the enterprise to hidden liabilities.

The opportunity is clear, those who combine AI speed with domain rigor will thrive. Those who confuse plausibility for reliability will not.

Drawing parallels to precision medicine, we show how AI adoption must be customized system by system, with fast operational gains in areas like prior authorization and scheduling, and slower, carefully validated progress in clinical decision support. We highlight why hybrid neuro-symbolic approaches, orchestration of multiple tools, and organizational sovereignty are critical for sustainable transformation.