In conversations with healthcare executives nationwide, I’m hearing a consistent theme: their organizations are investing heavily in AI to address capacity constraints and improve care quality, but getting clinical teams to use these tools effectively remains one of their biggest hurdles. Not the technology itself—the workforce’s readiness to leverage it.

We wanted to understand if this was systemic. So, we developed the Covista Care Capacity Monitor—a national study fielded by Gallup across all 50 states, surveying more than 1,300 clinicians and 160 healthcare executives.

What the Data Reveals

The adoption is real: 71% of healthcare organizations now use AI for clinical documentation, 54% for EHR interactions and 46% for diagnostics. Three-quarters of executives say AI has a positive impact on care quality. And 65% say AI can help address staffing shortages at least somewhat. The technology is working.

The hiring bar is shifting: 64% of healthcare executives say AI fluency now factors into recruiting decisions for physicians—57% for NPs and PAs, 49% for RNs. Nearly 60% also report that most of their existing clinical workforce needs upskilling or reskilling in these same technologies.

The priority gap tells its own story: 74% of clinicians say staffing is the problem, and only 34% prioritize technology improvements. Yet 63% say they want AI training. Those things aren’t in conflict—but right now, most clinicians aren’t seeing them as connected.

The Real Priority: Care Quality

When we asked healthcare executives about their top priorities for 2026, achieving adequate staffing ranked first, cited by 87% as a major priority.

The reason? Staffing shortages are negatively affecting care quality. 73% of healthcare executives and 76% of clinicians say staffing shortages negatively affect their ability to deliver high-quality care. Half of executives say shortages have reduced their capacity to serve patients.

From my vantage point preparing healthcare’s future workforce, these challenges are more connected than they may appear. Health systems have already made the AI investment—and are planning more. The workforce readiness investment is what unlocks it—getting that technology to do what it was built to do: relieve pressure on stretched teams and improve the quality of care they can deliver.

Most executives already sense this—the overwhelming majority see AI as part of the solution, increasing productivity and offloading the time-consuming administrative work that nurses, doctors and other clinicians deal with today. But the single biggest blocker right now is human capital: professionals who are fluent in these tools, can use them responsibly and help lead change within their health systems.

Our healthcare partners tell us that when clinicians are trained to use AI effectively, they can focus more attention on the aspects of care that require human judgment and connection. AI handles documentation, manages knowledge overload and surfaces what needs attention. Clinicians do what only clinicians can: exercise judgment, build trust and care for patients. The implementations that work are ones where staff understand what the technology is doing—and what it isn’t. Opacity breeds resistance. Transparency and training remove it.

Without readiness, the opposite happens. Experienced clinicians adopt new AI tools while managing full patient loads with minimal training time, creating stress and resistance. Newly hired staff need extensive AI onboarding, pulling experienced clinicians away from patient care.

The result? Underutilized technology. Frustrated staff. And care quality challenges remain unsolved.

What’s Actually Required

Getting there requires curriculum change that moves with urgency—for the next generation of healthcare professionals and the workforce already in place.

Our collaborations with Hippocratic AI, Google Cloud and Hello AI by GE Healthcare serve students pursuing healthcare careers and the health systems and healthcare providers whose staff need upskilling now. We’re developing AI credentials that run alongside traditional curriculum—building fundamental understanding of how AI works, when to trust it, when to question it and how to integrate it safely into clinical judgment.

The most effective approaches require sustained partnerships between healthcare and education providers. Health systems that clearly articulate what “AI workforce-ready” means for their environment give education institutions specific targets. Working together on competency development ensures graduates arrive with skills that match real-world workflows—and creates pathways for current staff to develop capabilities without leaving practice.

The Path Forward

Executives see AI’s value for care quality, but they’re underinvesting in workforce readiness. That gap explains why implementations stall.

The question isn’t whether to focus on staffing or AI adoption or care quality. It’s recognizing these are connected. You can’t hire fast enough to fill the gap. But you can prepare your current and future workforce to leverage AI in ways that help them deliver higher-quality care even while teams are stretched.

The organizations that realize the greatest returns on their AI investments will be the ones that build clinical trust in the technology in advance—through deliberate, sustained workforce preparation.

Michael Betz is Chief Digital Officer of Covista, America’s largest healthcare educator serving more than 97,000 students, and President of Walden University, one of Covista’s five institutions.

The Covista Care Capacity Monitor combines survey data from 1,347 clinicians and 167 healthcare executives with labor market analysis from U.S. Census, Bureau of Labor Statistics, Lightcast and IPEDS. Explore the platform at covista.com/research.

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