What Healthcare’s AI Readiness Gap Reveals About The Next Phase Of Adoption

Mark Thomas, Chief Technology Officer, MRO.

​AI is becoming more embedded in healthcare every day. As health systems explore ways to apply AI across a growing range of functions, the right foundation becomes more important.​

This is especially critical in healthcare quality. Quality operations rely on clinical context, accurate data and careful interpretation. If AI is being used to abstract clinical data, support quality reporting or measure performance, it needs to meet a high bar for accuracy, transparency and accountability.​

To understand how healthcare organizations are approaching AI adoption in quality, my company recently surveyed hospital and health system quality leaders about their current use of AI in quality operations. The results point to a gap between AI enthusiasm and organizational readiness. Nearly nine in 10 respondents (88%) are “interested in using AI in healthcare quality operations,” yet only 23% consider their organizations “very ready to adopt it.”​ This finding is consistent with a 2025 survey from Nordic and Modern Healthcare, which reported a large disconnect between healthcare executives’ confidence in their organization’s AI readiness and how prepared those organizations actually were.

It’s clear that while interest in AI is widespread, many organizations still need to put the policies, processes and infrastructure in place to create the foundation AI needs for success.

AI Has Moved Beyond Curiosity

Our survey found that a quarter of respondents have already implemented AI in quality operations, and another 30% are piloting, testing or evaluating AI solutions.​

That balance matters most as organizations consider expanding AI beyond pilots into broader quality operations. Respondents identified compliance concerns, trust in AI accuracy, costs and fragmented systems as their biggest barriers to AI adoption in quality.​