KEY TAKEAWAYS
- AI can reduce administrative burden and may help address an important contributor to clinician burnout.
- Efficiency gains aren’t automatic. AI can also introduce cognitive load, verification work, workflow disruption, and uncertainty around changing roles.
- Measure the impact on people, not just productivity. Successful implementation should make work better for the people using the technology while supporting quality care and patient experience.
Healthcare leaders are being asked to do more with a workforce that is already under pressure. Staffing challenges, administrative demands, clinician burnout, and rising patient complexity are making it increasingly important to find ways to improve efficiency without adding to the strain on the people delivering care. In 2022, nearly half of U.S. health workers (46%) reported feeling burned out often or very often, up from 32% in 2018.
Artificial intelligence is emerging as one potential way to address a major source of that strain: administrative burden.
And there are already signs that it can help. In a 2025 American Medical Association survey, 57% of physicians identified reducing administrative burdens through automation as AI’s biggest opportunity.
Early research is also encouraging. A 2025 study across six healthcare systems found that burnout among ambulatory physicians and advanced practice practitioners using an ambient AI scribe declined from 51.9% to 38.8% after 30 days, alongside improvements in cognitive workload and after-hours documentation.
The potential is significant: less time spent documenting and managing repetitive administrative work could mean more time for patients, clinical judgment, and the work that brings people into healthcare in the first place. But realizing that potential isn’t automatic. As AI becomes part of everyday healthcare, leaders also need to ask a more fundamental question: Is AI actually making work better for the people using it?
AI Potential: Assessing Intention Vs. Impact
One of the most significant shifts AI brings is changing the clinician’s role from creating information to supervising it. This is where the difference between AI’s intended impact and its actual impact on the workforce becomes important.
Without thoughtful planning, AI implementation can unintentionally create challenges such as:
Additional cognitive load. Rather than simply completing documentation, clinicians may be interpreting AI recommendations, monitoring for errors, and deciding when to rely on or override the technology. This can create what researchers describe as verification burden: the responsibility clinicians retain for reviewing, editing, validating, and approving AI-generated documentation and recommendations. Even when AI creates a first draft, clinicians remain accountable for its accuracy, completeness, and clinical appropriateness. As AI increases the volume and speed of documentation, verification can also become a larger part of the clinician’s workload, potentially offsetting some of the efficiency gains.
Impact on clinical judgment. AI can change how clinicians gather information, evaluate recommendations, and make decisions, with the impact varying based on each clinician’s experience, comfort level, and ability to question or verify AI-generated outputs. AI can serve as a valuable consultant, but organizations need clear guidance around when clinicians should review, challenge, or reject AI recommendations and how accountability for clinical decisions is maintained.
Technology and change fatigue. AI rarely replaces existing workflows overnight. More often, it is layered onto existing EHRs and clinical processes, requiring new workflows, new habits, and ongoing adaptation. Without sufficient communication, training, and workflow redesign, technology intended to reduce burden can instead contribute to change fatigue.
Anxiety about evolving roles. AI also raises questions that technology alone cannot answer. Will clinicians be expected to see more patients because documentation is faster? How will performance be evaluated? What happens when a clinician disagrees with an AI recommendation? Addressing these concerns proactively can help build confidence and engagement during adoption.
Emerging “Shadow AI” use. A recent discussion of “shadow AI” in healthcare highlights an important gap: when approved tools aren’t available or don’t address the problems clinicians experience in their daily work, employees may turn to tools outside the organization’s systems and policies to reduce documentation and administrative burden. While this creates new concerns around privacy, accuracy, cybersecurity, accountability, and patient safety, is also may signal the workforce is looking for support.
How Healthcare Organizations Can Keep AI Focused on Reducing Burnout
The question isn’t whether AI will change healthcare. It already is. The more important question is whether organizations will shape that change in a way that reduces burden, supports the workforce, and protects the human side of care.
Three things can help.
1. Anticipate the Impact
Before implementing an AI tool, assess more than its technical capabilities or projected efficiency gains.
Consider how it will change the actual work: what tasks will disappear, what new tasks will emerge, who will be responsible for reviewing or correcting AI-generated work, and how the technology may affect workload, decision-making, collaboration, and patient interaction.
Preparing the workforce is part of implementation, not an afterthought. Clear expectations, training, workflow redesign, and communication can help employees understand not just how to use a new tool, but how it will change their work.
2. Listen to the People Doing the Work
There can be a significant gap between AI’s potential on paper and the experience of using it in practice. Ask clinicians, nurses, administrative teams, and other employees what’s working, what’s creating friction, what tasks are actually being reduced, and where new burdens are emerging. Continue listening after implementation, not just during the planning phase.
3. Support the Workforce Through Change
Technology adoption doesn’t eliminate the need for human connection. In many cases, it makes that connection more important. Organizations can support employees through education and training, peer connection, leadership support, and opportunities to share concerns and learn from one another. Well-being resources should remain accessible as employees navigate both workplace change and the demands of life outside of work.
An EAP can be one part of that broader support system, providing confidential counseling, manager consultation, leadership support, education, and trainings and organizational support for addressing compassion fatigue, AI anxiety, trauma resilience, burnout or other concerns.
Keeping Workforce Well-Being at the Center
Even when AI successfully reduces administrative burden, it cannot address every source of clinician stress. Burnout, compassion fatigue, and the emotional demands of caring for others remain significant challenges across healthcare. Technology may improve efficiency, but it cannot replace the empathy, clinical judgment, and human connection that define exceptional care.
That is why workforce well-being should remain part of how organizations evaluate AI: How is this affecting the well-being of the people using it? Is it reducing frustration and administrative burden, or creating new pressures? Are employees better supported in their work? And ultimately, how is that experience affecting the care and experience of the patients they serve?
The goal is not simply to use AI to do more. It is to use technology in ways that strengthen the people delivering care and, ultimately, create a better experience for both employees and patients.