Four Years Later: Why Healthcare Leaders Need AI Competencies More Than Ever

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Reflecting on the Journey from 2021 to 2025

A few years ago in 2021, before generative AI dominated headlines and transformed workflows, I conducted research into healthcare executive competencies around decision-making as detailed in my article published in the Journal of Health Administration Education. At that time, research revealed a troubling gap: while artificial intelligence, machine learning, and deep learning were already making inroads in clinical settings, there was virtually no literature addressing the leadership competencies needed for enterprise-level AI adoption.

It's 2025 now, and I wanted to share where I think we are four years in—and why the need for competent, AI-literate healthcare leaders has become more urgent than ever.

The 2025 Healthcare AI Landscape: Beyond the Hype

The transformation has been remarkable. Today, 85% of healthcare leaders report that their organizations are either exploring or have already adopted generative AI capabilities, according to McKinsey's fourth quarter 2024 survey of 150 healthcare stakeholders. What was once speculative is now operational reality. AI has moved from niche clinical applications to becoming embedded in the very infrastructure of healthcare delivery.

Where AI Is Making Real Impact

In 2025, healthcare AI is no longer just about promising pilot projects—it's about measurable outcomes:

• Clinical Decision Support: AI tools are helping pathologists diagnose diseases like celiac disease in seconds rather than minutes, dramatically reducing backlogs, as demonstrated at the University of Cambridge. Stroke diagnosis software demonstrates twice the accuracy of human professionals in certain contexts, according to research from two UK universities involving 2,000 patients, and the timing precision could mean the difference between permanent disability and full recovery.

• Administrative Efficiency: Ambient listening technology—AI-powered voice recognition that documents patient encounters in real-time—has become the gateway application for many organizations. Healthcare leaders are choosing these solutions first because the ROI is clear: reduced clinician burnout, more face-to-face patient time, and measurable efficiency gains. According to Northwell Health's Data and AI Academy program results, 36% of participants reported time savings averaging six hours per week through AI-enabled workflow automation.

• Remote Monitoring and Predictive Analytics: The aging population and rising prevalence of chronic diseases have created massive healthcare datasets. AI now analyzes real-time data from wearable devices to identify patterns, enabling dynamic treatment adjustments and preventing adverse outcomes, particularly for patients in rural and underserved areas, according to Canada's 2025 Watch List on AI technologies in healthcare.

• Patient Safety: AI systems now work around the clock in the background, identifying care disconnects that human oversight might miss—from potential missed tests to drug diversion patterns that could harm patients. As Stacey Caywood, CEO of Wolters Kluwer Health, noted in their 2025 healthcare predictions, AI solutions are going deeper into live health data to identify issues that impact patient safety.

  
  

The Critical Problem: A Healthcare Leadership Competency Gap

Here's what concerns me most: while AI adoption has accelerated, the competency gap I identified in 2021 has widened rather than closed. Recent McKinsey research from January 2025 confirms that 46% of leaders identify skill gaps in their workforces as a significant barrier to AI adoption—and this includes the leaders themselves.

The Unique Demands of AI Leadership

Leading in the AI era requires what scholars now call a "multidimensional" approach across three essential capacities, as outlined in a 2024 review published in the Journal of Medical Internet Research on leadership for AI transformation:

Technical Capacity

• AI literacy to understand capabilities and limitations

• Subject matter knowledge to ask the right questions

• Data mining and statistical method comprehension

• Understanding of algorithms, their selection, and their biases

Adaptive Capacity

• Innovation mindset to identify AI opportunities

• Change leadership skills to navigate organizational transformation

• Ability to manage uncertainty and rapid technological evolution

• Skills to balance competing stakeholder priorities

Interpersonal Capacity

• Collaborative decision-making across multidisciplinary teams

• Communication skills to translate between technical and clinical domains

• Ethical reasoning to address privacy, bias, and equity concerns

• Empathy and wisdom that AI cannot replicate

This goes far beyond the traditional Healthcare Leadership Alliance competency domains established in 2008. The question isn't whether healthcare executives need new skills—it's whether we're developing them fast enough.

Why Competent Leadership Matters: The Stakes Are Higher

When I published my research in 2021, I warned that enterprise AI adoption demands "a shift beyond the traditional project-based technological adoption." That prediction has proven accurate, but the implications are more serious than I anticipated.

Four Critical Reasons Leadership Competency Is Non-Negotiable

1. The Speed of Innovation Demands Rapid, Informed Decisions

AI technologies evolve on a timeline measured in months, not years. As noted in the New England Journal of Medicine Catalyst, leaders who can move quickly while maintaining strategic rigor will gain significant competitive advantage. Those who cannot risk falling irreversibly behind as early adopters realize value from their investments.

2. The Complexity Requires Deep Understanding

As our 2021 research emphasized, asking appropriate questions about AI requires understanding the data mining process—from sampling and exploration through modeling and assessment. Leaders must comprehend how algorithms are trained, what biases they might contain, and when their recommendations should be questioned. Without this foundation, executives cannot effectively evaluate vendor claims, assess risk, or ensure appropriate deployment.

3. The Workforce Transformation Is Profound

AI isn't just changing what healthcare workers do—it's changing who does what. Organizations need to develop new roles like data scientists and AI integration specialists while reskilling existing staff. A 2025 survey by the American Medical Association found that 66% of physicians are already using health-AI tools—up from 38% in 2023—demonstrating how rapidly AI is becoming integral to clinical practice. Leaders lacking AI literacy cannot build effective talent strategies, create appropriate training programs, or retain the technical talent increasingly critical to competitive advantage.

4. The Ethical Implications Are Consequential

The social media industry's experience with biased algorithms perpetuating misinformation serves as a cautionary tale for healthcare. Leaders must understand how algorithms can disenfranchise populations through biased training data. They must navigate questions about data ownership, privacy protections, consent models, and liability when AI-facilitated errors occur. As highlighted in a 2025 survey by the OECD, 70% of the U.S. public expressed reluctance about AI being used for their diagnosis, underscoring the trust deficit that leaders must address. These aren't theoretical concerns—they're operational realities that demand competent leadership today.

From Awareness to Action: Building AI-Ready Healthcare Leaders

My recommendations for:

Healthcare Organizations

1. Assess Current Leadership Capabilities: Where are the gaps in AI literacy, technical understanding, and change management skills across your executive team?

   
   

2. Invest in Targeted Development: Prioritize education that goes beyond buzzwords to develop genuine competency in AI fundamentals, data ethics, and implementation strategy.

   
   

3. Build Multidisciplinary Teams: Bring together technical experts, clinical leaders, operational managers, and ethicists. The most successful AI implementations occur when diverse perspectives shape strategy from the beginning.

   
   

4. Create a Culture of Learning: AI will continue evolving. Organizations need ongoing education, not one-time training events.

Individual Leaders

• Commit to AI Literacy: You don't need to become a data scientist, but you must understand how AI systems work, their capabilities, limitations, and risks.

  
  

• Develop Your Translation Skills: The ability to bridge technical and clinical worlds—to help data scientists understand care delivery realities and help clinicians understand algorithmic possibilities—is increasingly valuable.

  
  

• Strengthen Ethical Reasoning: Study the ethical frameworks being developed for AI in healthcare. Understand principles like fairness, appropriateness, validity, effectiveness, and safety (the FAVES framework).

  
  

• Stay Connected to Innovation: Attend conferences, follow thought leaders, participate in communities of practice. The landscape changes too quickly for passive learning.

  
  

Looking Forward: The Leadership Imperative

Four years ago, I concluded my research by noting that "scientific inquiry into these areas will enable robust enterprise AI adoption planning." Today, that inquiry is well underway, and the evidence is clear: AI represents both tremendous opportunity and significant risk, and the difference between the two lies primarily in leadership competency.

The healthcare industry stands at a pivotal moment. According to the World Economic Forum, we have 4.5 billion people globally lacking access to essential healthcare services and a projected shortage of 11 million health workers by 2030. AI could help bridge these gaps—but only if leaders have the competencies to guide responsible, effective implementation.

The question isn't whether your organization will adopt AI. The question is whether your leaders will be competent enough to do so successfully.

The time to build those competencies is now.

About the Author: Dr. Richard Greenhill is a coach, consultant, and executive coach specializing in digital transformation and leadership development.

Want to discuss AI leadership development for your organization? visit us online at https://www.thebranded-strategy.com/ or email at engage@improvehealthllc.com

Referenced Research: Greenhill, R.G., Pearson, J.S., Schmidt, R.N., Stuart, D., & Rossettie, S. (2021). Exploring Healthcare Leadership Competencies for the Fourth Industrial Revolution: A Scoping Review of the Literature. Journal of Health Administration Education, 38(3), 695-708.

Sources and References

1. McKinsey & Company. (2025). "Generative AI in healthcare: Current trends and future outlook." Survey of 150 healthcare leaders, Q4 2024.

2. HealthTech Magazine. (2025). "An Overview of 2025 AI Trends in Healthcare." Insights on ambient listening and healthcare AI adoption.

3. Northwell Health Data and AI Academy. (2025). Program outcomes from 155 employee participants on AI workflow automation.

4. Canada's Drug Agency (CDA-AMC). (2025). "2025 Watch List: Artificial Intelligence in Health Care." Annual horizon scanning report on emerging AI technologies.

5. University of Cambridge & UK Universities. (2025). Research on AI stroke diagnosis accuracy involving 2,000 patients.

6. Wolters Kluwer Health. (2025). "25 for '25: Healthcare Technology Trends." Predictions report by CEO Stacey Caywood.

7. McKinsey & Company. (2025). "AI in the workplace: A report for 2025." Research on workforce skill gaps and AI adoption barriers.

8. Journal of Medical Internet Research. (2024). "Leadership for AI Transformation in Health Care Organization: Scoping Review." Analysis of leadership capacities for AI implementation.

9. New England Journal of Medicine Catalyst. (2024). "Health Care Leadership in the AI Era: A Seventh Test for the Decade Ahead." By Toby Cosgrove and Thomas H. Lee.

10. American Medical Association. (2025). Survey on physician AI adoption showing increase from 38% (2023) to 66% (2025).

11. OECD. (2025). "Artificial Intelligence and the Health Workforce: Perspectives from Medical Associations." Survey data on public trust in AI diagnosis.

12. World Economic Forum. (2025). "7 Ways AI is Transforming Healthcare." Report on global healthcare access gaps and AI potential.