Artificial intelligence has moved quickly from a technology that healthcare organizations are watching to one they are actively evaluating, testing and deploying. But as the number of potential applications grows, a more important leadership challenge is emerging: how do healthcare organizations move from experimenting with AI to creating meaningful, measurable value from it?
That question surfaced throughout a recent Nashville Health Care Council Next-Gen discussion led by cohort mentor Chair of the AI Practice and Partner at Bradley Arant Boult Cummings LLP, A.J. Bahou, with President at Lumeris, John Fryer. Using Lumeris’ experience deploying AI across primary care and value-based care as a real-world lens, the conversation moved beyond what AI can do to the organizational decisions required to make it work.
Several themes emerged that are increasingly relevant for healthcare leaders navigating AI adoption.
The proliferation of AI tools can make it tempting to start with the technology: Where could we deploy AI? What can this platform automate? What new capability could we add?
A more productive starting point may be the problem itself.
Lumeris’ use of AI illustrates this distinction. Rather than treating AI as a standalone technology, the organization uses it across the patient journey to bring together clinical, payer and other information, identify potential next actions and enable more proactive engagement between patients and care teams.
The broader lesson is that AI creates the most value when it is connected to a clearly defined organizational objective.
That requires leaders to ask questions such as:
In some cases, the answer may be to add AI to an existing workflow. In others, the more important question may be whether the workflow itself should continue to operate as it does today.
One of the clearest takeaways from the discussion was the importance of understanding the process before introducing the technology.
As Fryer put it, “AI without change management is basically nothing.”
AI layered onto a poorly designed process does not necessarily solve the underlying problem. It can simply create a technology-enabled version of the same inefficiency.
That means AI implementation begins well before deployment. Leaders need to understand how work happens today, where friction exists, what data informs decisions and where a technology intervention could create meaningful improvement.
It also changes the nature of change management itself.
Traditional transformations often have a relatively defined implementation period: design the change, deploy it, train people and move toward a new steady state. AI can be different. The technology itself may continue to evolve after implementation, creating a more fluid environment in which workflows, education and expectations must evolve alongside it.
For leaders, that means adoption cannot be treated as a one-time milestone. It becomes an ongoing organizational capability.
AI creates no inherent value simply because it is deployed.
As organizations evaluate potential use cases, the more important question is what evidence should justify expanding them.
The discussion explored how AI can be evaluated across clinical, operational and financial outcomes; from patient engagement and preventive care to unplanned admissions and the economics of delivering care.
That broader lens matters because a successful AI implementation is about more than whether the technology technically works.
Leaders should be asking:
Those questions help distinguish meaningful innovation from experimentation for experimentation's sake.
They also create a more disciplined approach to investment. As AI opportunities multiply, organizations will need to become increasingly selective about which problems are worth solving, which solutions deserve further investment and what evidence is necessary before scaling them.
Technology alone does not create adoption.
When AI touches clinical workflows, the people doing the work need to be part of the process. The goal is not simply to convince clinicians and operators that a new technology is useful, but to create enough understanding and curiosity that they can begin identifying opportunities themselves.
That can shift the conversation from: “Why are we changing this?” to “What could we do differently if this capability were available?”
But that openness needs to be balanced with strategic discipline. AI can generate an almost endless list of possible use cases. Organizations still have to determine where to focus.
For emerging leaders, this creates an important leadership tension: How do you create room for experimentation without allowing every new possibility to become a priority?
The answer may require leaders to become increasingly comfortable setting boundaries around innovation while still encouraging curiosity within them.
Another important consideration is recognizing that AI capabilities will rarely come from a single source.
Lumeris’ experience demonstrates the importance of working across technology, clinical content, research and other specialized capabilities rather than attempting to build every component internally.
That makes the AI conversation about more than traditional build-versus-buy decisions.
Leaders increasingly need to determine:
The ability to identify a partner is only the beginning. Integrating technologies, managing relationships and aligning different organizations around a shared objective are capabilities in their own right.
As the pace of AI development accelerates, that partnership judgment may become just as important as the ability to evaluate the technology itself.
Perhaps the most interesting question is not how AI can make today's healthcare processes more efficient, but what healthcare could look like if those processes were redesigned around the patient from the beginning.
AI creates opportunities to engage patients between traditional healthcare encounters, surface information that might otherwise be missed and connect different parts of the healthcare system more continuously.
That possibility points toward a different model of healthcare — one that is less dependent on episodic interactions and more capable of responding to what is happening in a patient's life between those encounters.
At the same time, consumers are becoming increasingly comfortable using AI themselves to find information, navigate questions and make decisions about their health. Healthcare organizations will need to consider how their own digital experiences respond to those changing expectations.
The leadership question therefore becomes larger than: where can we use AI?
It becomes: what would this experience look like if we designed it around the patient first, and then considered where AI could make that experience possible?
For the next generation of healthcare leaders, understanding AI will require more than knowing what the technology can do.
It will require the judgment to identify problems worth solving, understand the processes and data behind them, evaluate meaningful outcomes, bring people along, build effective partnerships and remain disciplined as new possibilities emerge.
That may ultimately be one of the most important leadership skills in an AI-enabled healthcare environment: knowing what questions to ask before deciding what technology to use.
AI will continue to evolve. The tools available six months from now may look different from those available today. But the leadership fundamentals remain remarkably consistent: understand the problem, focus on value, bring people into the process and keep innovation connected to the needs of patients, providers and the organizations responsible for delivering care.
For emerging healthcare leaders, developing that judgment now will be critical to not simply adopting what comes next, but leading it thoughtfully.
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