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IT departments used to be judged on whether they could deploy software on time. CIOs say that bar has moved as part of a broader CIO AI workforce shift: the job now is redesigning clinical workflows, owning outcomes alongside clinicians, and building governance into every AI implementation from day one rather than bolting it on afterward.
Why the CIO AI Workforce Shift Changes IT’s Core Mission
Becker’s asked CIOs two questions: how are health system IT departments changing in the era of AI, and what’s the boldest tech bet they’re making over the next two years? Their answers describe IT moving from a support function to a co-owner of clinical and operational results.
New Pressures Facing IT Teams
Garrett Olin, CIO of Shasta Community Health Center in Redding, California, said changes for IT in the AI era are around upskilling, supporting implementation, more complex security risk and increased vendor assessment and management, a set of demands that reflects the broader CIO AI workforce shift underway across health systems of all sizes.
From Software Deployment to Clinical Co-Ownership
Tony Damron, chief information officer and executive vice president of Pikeville Medical Center in Kentucky, said the biggest shift he’s seeing is that IT is no longer just a support function for AI; it’s becoming a co-owner of clinical and operational outcomes. “When we launched our Epic Ambient AI pilot with roughly 30 providers this summer, the conversation in our IT department wasn’t ‘how do we deploy this software,’ it was ‘how do we redesign workflows and measure impact on documentation burden and patient face-time,'” Damron said. “That’s a different kind of IT work than we were doing five years ago.”
A New Standard for Evaluating Any Technology
“The work today has become less about whose vendor name is stamped on it and more about how to keep patients and clinicians happy while implementing technology to drive the organization forward,” Damron said. AI is also raising the bar on how IT evaluates any new technology, even outside of AI projects specifically. When Pikeville recently evaluated staffing platforms, the presence of AI-driven optimization changed the ROI conversation entirely, shifting the focus from finding a scheduling tool to what predictive, adaptive systems could do for labor costs and nurse satisfaction that a static rules engine couldn’t.
Governance as a Core Part of the CIO AI Workforce Shift
Governance has become a core IT deliverable, not an afterthought, because at a rural, independent health system, there isn’t the luxury of large data science teams to catch problems after the fact. IT has to build that right out of the gate of any implementation, according to Damron, underscoring how the CIO AI workforce shift demands proactive rather than reactive oversight structures.
Agentic AI as the Next Frontier
The boldest tech bet over the next two years, Olin said, is greater use and integration of agentic AI to automate and augment processes across the organization, spanning clinical, revenue cycle, quality, finance, and strategy functions alike.
Balancing Optimism With Clinical Caution
While AI is taking the world by storm in many different ways, that doesn’t always make it the correct decision to implement in a clinical setting, Damron said. Health system executives are all cautiously optimistic about how AI agents can continue to help in healthcare while leaving the clinical decision-making in the hands of the providers, a balance central to how the CIO AI workforce shift is playing out responsibly.
Continued Investment in Automating Manual Tasks
“So, we will continue to make our biggest bets on using artificial intelligence in healthcare, especially to automate manual tasks and free more clinical staff up to do patient care and not as much screen time,” Damron said. “Simply put, we are still betting on AI in healthcare and I see that continuing well into our future.”
What This Means for Health System IT Departments
The CIO AI workforce shift described by both Olin and Damron suggests that health system IT departments are increasingly expected to demonstrate measurable clinical and operational impact rather than simply successful technical deployment. As agentic AI adoption grows and governance becomes embedded into every implementation from the outset, IT leaders at organizations of all sizes, from rural community health centers to regional medical centers, appear to be converging on a shared understanding: the value of AI investment will be judged by outcomes for patients and clinicians, not by which vendor’s name appears on the software.
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