Healthcare organizations are still in the early stages of defining executive leadership for artificial intelligence, with many choosing to build governance frameworks before deciding whether to create a dedicated chief AI officer role, according to Jeffrey Sturman, managing partner of the IT and digital leadership practice at executive search firm WittKieffer.
"What we're seeing a lot in the market is this is really still very foundational. It's very early in the evolution of what AI and what leaders in AI need to be focused in on," Sturman said in a video interview with ISMG. "Many of the organizations we're talking to, many of the CEOs, the boards and our leadership, CIOs and the like are saying, 'We need AI governance, and let's start there.'"
Before expanding the C-suite, health systems must identify leaders who can establish governance, align AI initiatives with strategic goals, and guide responsible adoption across the enterprise. The decision to appoint a chief AI officer or distribute those responsibilities among existing roles - such as the CIO, chief medical information officer, or chief data officer - depends on each organization's strategic priorities, maturity, and operational needs.
Governance first, titles second
Large academic medical centers are more likely to invest in dedicated AI leadership. But many healthcare organizations are pausing to build governance structures before creating new executive positions. Sturman said the conversation often starts with a simple question: where does governance sit so that AI can improve patient care delivery, operational efficiency, and financial performance over time.
"Let's talk about where governance plays so that we're making the best decisions about how AI can come in and influence patient care delivery, influence efficiency and operations, influence the bottom line even over time," he said.
The push for governance reflects the dual pressure health systems face. They need to adopt AI quickly enough to remain competitive while managing risks around patient safety, data privacy, and regulatory compliance. Without clear oversight, organizations risk fragmented adoption that creates more problems than it solves.
What makes an effective AI leader in healthcare
Sturman outlined the traits that separate successful AI leaders from those who struggle. Clinical or healthcare operational expertise is essential - this is not a pure technology role. Leaders must understand how care delivery works before they can apply AI to it. Strong communication skills and genuine curiosity matter just as much, particularly when educating executive teams and boards who may not understand the technology's limits or potential.
The role also demands the ability to evaluate rapidly evolving technologies without getting swept up in vendor hype. Sturman said the most effective leaders can separate what works today from what might work years from now, and they communicate that distinction clearly to stakeholders who control budgets and strategy.
Lessons from other industries
Healthcare can learn from how banking, finance, and manufacturing have adopted AI, Sturman said. Those industries faced similar governance questions years ago and developed frameworks for risk management, compliance, and ROI measurement that health systems can adapt rather than build from scratch.
The financial services sector, for example, has established models for auditing AI-driven decisions - a capability that translates directly to clinical decision support tools. Manufacturing's experience with AI-driven process optimization offers parallels for hospital operations and supply chain management. These cross-industry lessons can accelerate healthcare's AI maturity without repeating mistakes others have already made.
Why this matters for healthcare professionals
For clinicians, IT leaders, and operations managers, the governance-first approach means AI adoption will move at a deliberate pace - not slow, but structured. Professionals who understand both clinical workflows and AI capabilities will be positioned for emerging leadership roles, whether or not those roles carry a chief AI officer title. The organizations that invest in governance now are building the foundation for AI tools that actually integrate into daily work rather than creating another layer of technology that teams have to work around.
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