The debate over AI and inequality has largely focused on people—who will have access to the technology, who will benefit from it and who risks being left behind. But inside healthcare, another AI divide is already taking shape. This one is between the hospitals that have the money, talent and infrastructure to put AI to work and those that don't.

The numbers are striking. According to federal data on hospital AI adoption, 71% of U.S. hospitals used predictive AI integrated with their electronic health records in 2024. But adoption reached 96% among large hospitals compared with just 59% among small hospitals. Among hospitals belonging to multi-hospital systems, 86% used predictive AI. Among independent hospitals, it was only 37%.

That gap matters because AI is quickly becoming part of how hospitals survive, not simply an innovation project. The same federal data show some of the fastest-growing hospital AI applications are decidedly practical, including billing and scheduling. These are exactly the kinds of labor-intensive functions where financially stressed hospitals need to find efficiencies.

Yet those hospitals may be the least equipped to make the investment.

Hospitals remain extraordinarily dependent on people to operate. According to the American Hospital Association, workforce spending accounted for about 60% of hospital expenses in 2025, exceeding $1 trillion. Total hospital expenses grew 7.5% that year, more than twice the growth in hospital prices.

This creates a dangerous catch-22. The hospitals with the greatest need to use technology to redesign labor-intensive operations frequently have the least capacity to invest in doing so.

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