What It Takes to Lead Health Care Through Digital and AI Transformation

Health care is entering an unprecedented period of rapid, technology-enabled transformation. AI, digital medicine, new payment models, and evolving regulatory frameworks are reshaping how care is designed and delivered, often faster than traditional structures can adapt. The question now for health care leaders is: what capabilities do they need to spearhead a system that is changing this quickly?

“If you’re not getting up to speed on AI right now, you’re already behind,” explains William Gordon, MD, MBI, staff physician at Brigham and Women’s Hospital and faculty co-director of the Technology-Enabled Care Delivery: From Digital Medicine to AI program at Harvard Medical School. He points to projections such as Goldman Sachs’ estimate that AI infrastructure investment will exceed $700 billion in 2026, accelerating what is possible in care delivery at unparalleled speed. “This is not incremental change,” he stresses. “This is exponential change.”

The Realities Reshaping Care Delivery

As technology accelerates, it’s exposing deeper structural and operational realities that leaders must understand. Gordon emphasizes that the transformation underway is not simply about adopting new tools, but about changes in how care is organized, delivered, and governed. “We’re moving from a world where technology supports care to a world where technology shapes it,” he says. “That requires a different kind of leadership—one grounded in clinical, operational, and policy fluency.”

He describes four realities that will define the next decade of health care delivery:

1. New models of care are emerging faster than systems can support.

AI and digital medicine are advancing more quickly than the infrastructure designed to enable them. The result is a widening gap between what is technologically possible and what health systems are prepared to implement.

“We’re already seeing care models that didn’t exist five years ago,” Gordon says. This includes AI-supported triage, AI-augmented documentation, and AI-driven risk prediction. “The technology is not waiting for us to catch up. It’s moving whether we’re ready or not.”

The challenge for leaders is integrating these capabilities safely and responsibly to meet the needs that exist.

2. Clinicians and patients are adopting AI before organizations do.

More than 90 percent of U.S. physicians report using, or being interested in using, AI in their daily work, according to a Doximity study. Gordon calls this uptake “unprecedented in the context of every other new technology/therapy—almost nothing has become so widely used so quickly. Providers are using this already, whether their health system is allowing it or not,” he points out. “They are adopting tools that help them survive the day.”

Patients are doing the same. Bipartisan Policy Center data shows that among respondents who use digital tools, 60 percent had tried an app specifically geared to mental health, and close to half had used a general chatbot. A major draw to such digital mental health offerings is the ease of access and use. They also enable users to seek mental health support during off-hours, especially late at night or early in the morning, when traditional care is least accessible.

“People are turning to these tools because they need help in the moment,” Gordon says. “They aren’t waiting for the system to give them permission.”

This bottom-up adoption is reshaping expectations and forcing organizations to rethink how they integrate AI-enabled interactions into their systems.

3. A fragmented regulatory landscape raises the stakes.

While federal agencies continue to develop guidance and oversight approaches for health care AI, many operational requirements and restrictions are also emerging at the state level. In fact, nearly every state has introduced legislation, and more than half have passed AI-related health regulations, according to the National Conference of State Legislatures.

For organizations operating across multiple states, this creates a patchwork of requirements that complicates everything from workflow design to vendor selection.

“Care delivery is not bound by geography, but regulation often is,” notes Gordon. “That mismatch is going to define the next decade of AI-enabled care delivery.”

4. AI and digital fluency are now core leadership competencies.

Leaders don’t need to be engineers or data scientists, but they do need a working understanding of what AI tools can do and how to use them to add value in different ways. Gordon describes the emerging leadership profile as someone who can speak the technical language in a focused way. Fluency also means knowing what questions to ask, such as:

1. What data was this model trained on?
2. What are its limitations?
3. How does it behave with different populations?
4. What happens when it fails?“

AI is not magic,” he says. “It’s a tool. Leaders need to know when it helps and when it doesn’t.” Those who develop their aptitudes in these areas will likely be more successful overall.

Why Leaders Can’t Wait

While some leaders may want to take a step back to see where the field is heading, Gordon stresses that there’s no time to wait. Patients and clinicians are already using AI, competitors are deploying it, regulators are shaping it, and payers are testing digital-first models.

“This is not a moment for leaders to sit back,” he says. “This is a moment to lean in, learn quickly, and lead responsibly.”

Leadership Imperatives for the AI-Enabled Future

Based on these realities, Gordon outlines seven imperatives for leaders preparing their organizations for the next phase of AI-enabled care:

1. Start with real problems: AI should be used to solve an existing clinical or operational need, not to create a new one.
2. Build cross-functional fluency: Bridge technical, administrative, clinical, and policy domains so that all your teams will be on the same page.
3. Expect uneven adoption: Clinicians and patients will move faster than systems. You can help your organization to catch up in the most effective way.
4. Prioritize safety and governance: Guardrails must be in place before tools scale to ensure that they meet regulatory and ethical requirements.
5. Invest in workforce readiness: Teams need training, support, and clear guidance on how to use digital tools in their existing and future workflows.
6. Stay close to the evidence: The field is evolving quickly; therefore, leaders must keep pace and use the lessons learned to guide new efforts.
7. Create conditions for safe experimentation: Without psychological safety and tolerance for some failure, organizations cannot learn and advance their offerings.

The Opportunity Ahead

The good news is that many health care leaders now have access to the advanced tools needed to maximize care delivery in ways that weren’t possible before. Yet, the best tools alone won’t change systems. 

Gordon stresses that leaders still have to evaluate what’s worth adopting, understand how these technologies behave in clinical environments, avoid costly missteps, and guide their teams through responsible implementation. 

The leaders who build AI skills in all these areas will be well-positioned to help their organizations translate possibility into sustainable clinical impact.