From hype to healing: How healthcare leaders are embracing AI
For the new technology to truly make a difference in the industry, it must fully incorporate human elements and inputs.
Artificial intelligence is emerging as both a transformative force and a cultural flashpoint in healthcare. It has substantial potential to revolutionize clinical care, operations and health equity is substantial. However, challenges to adoption remain in gaining trust, defining governance and preparing people for rapid change.
At the American College of Health Data Management (ACHDM), a growing number of Fellows are not just exploring AI’s promise — they’re confronting its complexity. Their message is clear — the next wave of healthcare transformation must be led with transparency, curiosity and humanity.
As the industry transitions into an age fueled by AI, healthcare leaders acknowledge that it’s important to understand that AI will never replace the human elements of empathy, compassion or wisdom. They understand that’s crucial to have transparent conversations about how best to employ AI and create guardrails for its responsible use.
That blend of confidence and caution is increasingly common within healthcare. AI capabilities are maturing, but so does the recognition that success depends less on the technology itself and more on how leaders shape the narrative around it.
Leading with learning
For Andy Kinnear, a health data veteran with more than 30 years in the field, embracing AI began with curiosity and now continues with his own personal commitment to capitalizing on its potential. “My childhood, education and 30-plus year career have all been built on the relentless pursuit of knowledge,” Kinnear reflects. “I’m reading, listening, engaging and testing AI tech daily, so I can help others understand the opportunity and uphold ethical standards.”
Having a hands-on mindset isn’t just about engaging in more hype — it’s about building trust in the technology. In fact, a recent article on HealthDataManagement.com noted that “AI may not be a sentient co-worker, but it can still stir worry among humans,” and that healthcare organizations must acknowledge staff fears and ensure AI is introduced through practical, inclusive initiatives.
Kinnear echoes the need for education at every level. “I’m investing in my team’s learning and encouraging their continued development. They see me doing it, so they know it matters.”
Strategic, not speculative
While education lays the groundwork, implementation brings the vision to life. For Dr. Eric Weaver, a nationally recognized expert in value-based care, the stakes are even higher.
“AI will have an impact on healthcare delivery (that’s) more significant than anything we’ve seen in the past 50 years,” Weaver asserts. “I’m immersing myself in data science and AI through coursework, reading and conversations with technologists.” His focus is on driving AI adoption that supports strategic goals, particularly those aimed at improving population health outcomes.
Weaver has firsthand evidence of the gaps that AI can fill. “I’ve seen organizations reduce administrative burdens and clinical errors by using predictive models,” he says. But he warns that, “you must approach implementation with a clear sense of purpose.”
That perspective aligns with recent insights from Mayo Clinic Platform President John Halamka, MD, who told Health Data Management that AI will “revolutionize the care delivery model,” but only if “we get the governance and workflows right.”
Ground-level innovation
Across the country, other Fellows are putting AI into practice with both innovation and introspection.
For example, Grant Wood is organizing the Genomics AI Coalition, aimed at leveraging AI in precision medicine. “This is about catalyzing partnerships to scale innovation responsibly,” Wood says.
Meanwhile, Julia Rehman is driving practical applications inside a health system. “As AI-driven decision-making reshapes healthcare delivery, I’m developing risk stratification models to prioritize care,” she says. “But I’m also critically examining how we govern this technology, especially around algorithmic bias and equitable access.”
Rehman believes successful AI implementation requires strong operational readiness. “In our analytics transformation initiative, I led a cross-functional team to revamp our enterprise data strategy,” she recalls. “We aligned stakeholders, built new skills and embedded change management practices to ensure adoption.”
Her advice to peers? “Make sure data governance and workforce training aren’t afterthoughts — they’re prerequisites.”
Creating cultures of readiness
Preparing teams to thrive in an AI-rich environment is an equally urgent priority, Weaver indicates. “With generative AI models becoming more prevalent, I’m actively training my staff and engaging them in use case development,” he says. “That builds ownership and reduces fear.”
A significant key to cultural success is proactive communication. AI implementations in healthcare organizations can be shattered by rampant misinformation, ACHDM Fellows say. They underscore executive responsibility to facilitate those conversations.
There’s a clear consensus among the Fellows that AI’s impact on healthcare will be profound, but not automatic. The success of the technology will hinge on leaders’ ability to think critically, act strategically and lead with empathy.
As Rehman puts it, “Technology doesn’t transform systems, people do.”
The integration of AI into healthcare is not merely a technological shift, it’s a cultural transformation. The Fellows of the American College of Health Data Management are proving that success with AI doesn’t begin with code — it begins with leadership, trust and a deep sense of accountability.
Highlighting best practices
Here are some best practices that executives are using to guide their teams and organizations into the AI future.
Start with self-education and model it. “I’m reading, listening, engaging and testing AI tech daily so I can help others understand the opportunity,” says Andy Kinnear. Leaders who invest in their own understanding of AI are better equipped to lead others through the change.
Create a transparent dialogue about AI’s role. It’s important to have conversations that acknowledge AI will never replace the human elements of empathy, compassion, or wisdom. Clear, honest communication can preempt fear and build trust.
Align AI with strategic goals, and don’t just chase the trend. “I’m of the mindset that AI will have an impact more significant than anything we’ve seen in the past 50 years,” says Eric Weaver, who’s applying AI to value-based care. Let mission, not novelty, guide your implementation.
Integrate teams into development and governance. “With generative AI models becoming more prevalent, I’m actively training my staff and engaging them in use case development,” says Weaver. Involving people early fosters ownership and long-term adoption.
Build infrastructure to support AI operations and ethics. Herat Joshi is improving EHR integration and mentoring teams in AI governance. His approach underscores that innovation must rest on solid data, systems, and policies.
Balance innovation with integrity. “As AI reshapes healthcare delivery, we must examine how to govern it, especially around algorithmic bias and equitable access,” says Julia Rehman, who leads operational AI deployments. Her work is a reminder that ethics and effectiveness are not at odds — they’re interdependent.
Catalyze ecosystems, not just tools. Grant Wood’s Genomics AI Coalition illustrates that real progress comes from partnerships. Successful AI doesn’t live in a single product — it thrives in a connected, trusted network of collaborators.
Kyle Atkins is a Fellow of the American College of Health Data Management.