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.

Navigating the AI Era: Craftsmanship in Healthcare Leadership

AI functions in ways that feel threatening, mostly because we, as humans, are creatures of habit. This is true in our personal lives and our careers. Surviving in a future with AI will require us to examine the unique intersections of our experience, education, and industry. Routine will become irrelevant, and value will shift to the unique contributions we bring to our roles and the care we provide to patients.

The medieval guild system thrived on something AI cannot replicate: the accumulated wisdom of hands-on experience combined with contextual judgment. As machine learning absorbs routine tasks in healthcare, we're witnessing a fundamental shift in what creates value. The question isn't whether AI will transform healthcare—it already is. The question in 2025 becomes, how will healthcare professionals cultivate craftsmanship that machines cannot touch?

Lesson 1: Build at the Intersections

The nurse who understands both clinical workflows and data architecture sees implementation challenges invisible to others. The physician who combines patient care experience with quality improvement methodology can translate AI insights into actionable protocols. The administrator who bridges operations with change management can anticipate resistance before it derails adoption.

Healthcare's complexity demands professionals who exist at intersections. Stop building expertise in isolated silos. Start deliberately creating unique combinations of knowledge that only you possess.

Lesson 2: Context Is Your Competitive Advantage

AI excels at pattern recognition across thousands of cases. However, it cannot read the room when a family is struggling with end-of-life decisions. It cannot recognize when a patient's cultural background requires a different approach to informed consent. It cannot adapt care plans when social determinants override clinical best practices.

Your ability to synthesize medical evidence, patient preferences, family dynamics, and resource constraints in real-time—that's craftsmanship. Cultivate it deliberately. Document it. Teach it.

Lesson 3: Redefine Professional Development

Traditional career progression in healthcare has rewarded depth in a single discipline. That model is eroding. The future belongs to those who build T-shaped expertise—deep knowledge in one area, connected to a broad understanding across multiple domains.

Ask yourself: What am I learning outside my primary discipline? How am I connecting disparate areas of healthcare? Where are the gaps between specialties that only someone with my unique combination of experiences could fill?

Lesson 4: Lead the Integration, Don't Resist the Tool

AI will not replace clinicians who use AI. But clinicians who use AI effectively may replace those who don't. The craftsmanship lies not in competing with the algorithm but in knowing when to trust it, when to question it, and how to integrate its insights with human judgment. This requires a new form of clinical wisdom—one that understands both the power and the limitations of machine learning in healthcare contexts.

The Path Forward

We can no longer build careers by mastering routines deeply. The routine is becoming automated. Value is shifting to the unique intersections we occupy—the combinations of clinical insight, operational understanding, technological fluency, and human judgment that cannot be replicated by any algorithm or any other individual.

What unusual intersections are you building in your career? What craft are you developing that exists nowhere else? The future of healthcare belongs not to those who do routine work faster, but to the craftspeople who bring wisdom born from irreplaceable combinations of experience to the most complex human challenges in patient care.

In conclusion, we must embrace the changes AI brings while enhancing our unique skills. The ability to navigate this new landscape confidently will define our success. Let's not just adapt—let's thrive.

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AI Won't Replace Healthcare Workers—It Will Unleash Them

Futuristic humanoid robot equipped with advanced AI capabilities.

Why Human Skills Matter More Than Ever in AI-Driven Healthcare

The fear is understandable: Will AI take my job? But in healthcare, we're asking the wrong question.

The right question is: What becomes possible when AI handles what machines do best, freeing us to do what only humans can?

From Task Completion to Human Connection

Healthcare workers didn't enter this field to spend hours on documentation, prior authorizations, or searching through fragmented systems for patient information. They came to heal, to comfort, to solve complex problems, and to be present during life's most vulnerable moments.

AI excels at pattern recognition, data processing, and administrative heavy lifting. It can draft clinical notes, flag potential medication interactions, predict patient deterioration, and streamline scheduling. What it cannot do is hold a patient's hand during a difficult diagnosis, navigate the nuanced conversations about end-of-life care, or read the unspoken anxiety in a family member's eyes.

The Future Belongs to Uniquely Human Skills

As AI absorbs routine cognitive tasks, the healthcare workforce will shift toward skills that are irreplaceable:

Clinical judgment in ambiguity. AI provides data; humans provide wisdom. When lab results conflict with clinical presentation, when guidelines don't fit the patient in front of you—that's where human expertise shines.

Empathy and emotional intelligence. The ability to connect, to truly listen, to make patients feel seen and heard—no algorithm can replicate the therapeutic value of genuine human presence.

Complex problem-solving. Healthcare is messy, contextual, and deeply personal. Navigating social determinants of health, family dynamics, and ethical dilemmas requires human discernment.

Leadership and change management. As AI transforms workflows, we need leaders who can guide teams through change, build trust, and cultivate cultures that embrace innovation while honoring human dignity.

Healthcare Workers: Thriving Means Evolving

Healthcare professionals who will thrive aren't those who resist AI—they're the ones who learn to partner with it. They'll use AI to amplify their impact: seeing more patients because documentation takes minutes instead of hours, catching complications earlier because predictive analytics flag risks, and having the mental bandwidth for the difficult conversations that truly matter.

The workforce of the future won't be replaced by AI. They'll be liberated by it—finally able to practice at the top of their license, to bring their full humanity to their work, and to focus on what drew them to healthcare in the first place.

The question isn't whether AI will change healthcare work. It's whether we'll prepare healthcare workers the ability to leverage AI so their unique human value can finally take center stage.

How are you rebranding for this future phase of AI impact?