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AI isn’t failing because it lacks capability. It’s failing because it isn’t consistently reaching the moments where decisions are made.
Healthcare isn’t struggling to adopt AI. In fact, the industry has moved quickly from early experimentation to widespread investment in tools designed to improve clinical decision-making, reduce administrative burden, and drive financial performance. Many health systems and payers now have AI strategies, and many have already demonstrated that these technologies can work in controlled environments.
And yet, a familiar pattern continues to emerge. Pilot programs show promise and early results generate excitement, but when it comes time to scale, impact stalls. Adoption lags, outcomes plateau, and organizations are left wondering why tools that seemed so powerful in theory are not delivering in practice.
This challenge isn’t unique to healthcare. McKinsey research has shown that while AI adoption is accelerating rapidly, many organizations are still early in translating that momentum into scaled operational and financial impact, reinforcing that the gap between experimentation and execution remains a core barrier.
The answer is less about the sophistication of the technology and more about how it fits into the reality of healthcare operations. AI isn’t failing because it lacks capability. It’s failing because it isn’t consistently reaching the moments where decisions are made.