Why AI In Healthcare Fails Without People-Centered Strategy

The real challenge isn’t technology—it’s people.

AI is reshaping healthcare for all stakeholders – whether you’re a hospital administrator evaluating new systems, a physician looking to improve patient care, an investor assessing opportunities, or a patient seeking better outcomes. But the reality is that most organizations are failing to get value from their AI efforts… for now.

The adoption of AI in drug development, patient care, operations, and health management has created a lot of activity but limited value. The biggest barrier to real impact isn’t the technology itself, but in how leaders are approaching the use and integration of AI tools. Too many are asking the wrong question by looking to “develop an AI strategy.” But, AI, like other tools, should not have its own strategy. Instead, what healthcare leaders need to figure out is “How can AI help us accelerate and achieve our organizational aspirations?”

AI is a Tool, Not a Strategy

A stethoscope, an MRI machine, or electronic health records only create value when they serve a clear purpose: better diagnoses, faster treatment, improved patient outcomes, or more efficient operations.

AI is another tool, and it works the same way. Whether your goal is getting treatments to patients faster, reducing administrative burden on clinicians, improving care coordination, or making healthcare more affordable, AI helps accelerate progress on these existing priorities. Adopting AI just for the sake of AI, without connecting it to what your organization is trying to accomplish, leads to disconnected tools resulting in lots of motion and very little progress.

AI Adoption Is Really About People

AI is unique from past technology, as its usefulness is contingent on constant and evolving human decision-making. When hospitals switched to electronic health records and upgraded billing systems, staff had to put effort into learning the new system. Once they did, however, the processes they followed and the actions and behaviors they needed to get the most out of the system were consistent. But AI tools, especially generative AI now being used for medical documentation, care planning, and patient communications, work differently. Two clinicians with the same AI tool can get vastly different results based on how they use it. The value of AI is inherently tied to how people use it and this has implications.

For healthcare providers: A physician using AI to review patient histories might uncover critical insights another misses. Not because the technology is different, but because of how they engage with it.
For administrators: Getting staff to adopt AI effectively means helping them understand why it matters to patient care and operational goals, versus just mandating its use.
For health consumers: As AI becomes embedded in telehealth, diagnostics, and personalized treatment plans, your experience will depend on how well your care team integrates these tools.

The Real Barriers: Fear and Resistance

While many are beginning to embrace it, AI still makes people nervous. Headlines about job losses and automation create anxiety, especially among healthcare workers already stretched thin. If the focus on AI is on efficiency improvement and if leaders don’t put the effort into painting the picture of how it can help free up time to devote to the mission of delivering better patient care, resistance is inevitable.

The promise of AI in healthcare is significant, tangible, and exciting. Examples of immediate use cases include:

For clinicians: Reduce time spent on paperwork so they can focus on patients
For administrators: Streamline operations and improve resource allocation
For patients: Get faster, more personalized, and more effective care
For investors and insurers: Drive better outcomes while controlling costs

But if the expected benefits aren’t clearly communicated, AI adoption will stall—or worse, create new problems instead of solving existing ones.

What Works: Top-Down Vision Meets Bottom-Up Innovation

For a fast-evolving tools like AI, successful integration into the organization requires two separate approaches:

Deliberate and Strategic: Executives and administrators need to connect AI initiatives to the organization’s core mission. Are you trying to reduce patient wait times? Improve chronic disease management? Accelerate clinical trials? Starting from the desired outcomes and asking the question of “how can AI enable them?” will help identify the most strategically important use cases. Having a set of criteria by which to judge which challenges/opportunities are best addressed with AI can be helpful. Current AI tools are particularly well suited to areas where pattern recognition, data searching, knowledge access, or analysis are important.

Emergent and Experimental: AI is still a very new technology, and not all the use cases are obvious. Identifying where AI can have the biggest impact will require experimentation. The people closest to the work—physicians, nurses, lab technicians, care coordinators, medical affairs teams—know where the real bottlenecks and opportunities are in their processes. Creating space and mechanisms for them to experiment with AI and surface the best ideas not only helps with support and buy-in for AI use but also develops innovative solutions to existing challenges. This can start by involving employees in pilots. For example, a hospital system trying to reduce readmissions shouldn’t just hand clinicians an AI prediction tool. They should involve care teams in testing it, refining how it fits into discharge planning, and identifying what helps them actually keep patients healthy after they leave.

Reducing Anxiety and Building Momentum

To encourage employees to embrace AI and participate in the change, leaders can employ some tried and tested approaches:

Create safe spaces for experimentation: Encourage teams to initially test AI tools in low-stakes environments like reviewing literature, drafting routine documentation, or analyzing trends in patient data. This helps them see the benefits without taking on the risk of impactful mistakes.

Celebrate early wins: When someone uses AI to solve a problem or improve a process, share that story. Recognition and success build momentum.

Remove barriers: Traditional healthcare management systems are built for consistency and compliance, but they can slow innovation. Leaders need to streamline approvals, create cross-functional teams, and empower more people to find solutions.

Address job security concerns directly: Be transparent about how AI will change roles and emphasize the overall organizational goals. The focus should stay on utilizing AI to improve work and progress against the mission. If efficiency gains and elimination of jobs is part of the business strategy, then leaders should state that as well. In every case we have encountered, the fear and anxiety lessen with clarity – even when some of the news is not good.

Making AI Work for Healthcare Organizations

AI offers enormous potential to improve patient outcomes, accelerate medical breakthroughs, reduce costs, and make healthcare more accessible. Realizing that potential requires a different approach from past technological implementations. Avoid starting with, “What’s our AI strategy?” Instead, start with, “What are we trying to achieve, and how can AI help us get there faster or better?” When leaders and practitioners engage with AI tools as partners without the fear of replacement, the combination of human expertise and AI capabilities can deliver much better care.

AI transformation in healthcare is fundamentally a people challenge, not just a technology implementation. The most successful organizations will connect AI initiatives to specific outcomes, engage employee as partners in the change, and build cultures that encourage experimentation and change. The organizations that treat AI transformation as an opportunity to operate differently will be able to achieve real value from AI investments, and more importantly, deliver better outcomes for patients.