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At Rochester, Minn.-based Mayo Clinic, which operates campuses in Minnesota, Arizona and Florida as well as a network of regional clinics and hospitals, AI strategy starts with the people closest to the work. That approach has helped shape an AI portfolio now including Mayo Clinic 500 AI models in various stages of deployment, according to Richard Gray, MD, vice president of Mayo Clinic and CEO of its Arizona operations in Phoenix.
Lesson 1: Give Employees Room to Identify Use Cases
Mayo has largely taken a grassroots approach to AI, particularly during the earlier stages of its work, Dr. Gray said. The organization has encouraged clinicians and other employees to identify pain points in their work while expanding AI capabilities within individual departments. “We want to create space for our people, our leaders, who feel comfortable exploring those new ideas, and they do so,” Dr. Gray said.
Embedding AI Capabilities Closer to the Work
Mayo is also embedding AI capabilities within departments so ideas can be developed closer to the work. “We’re embedding and growing AI capabilities on the front lines within departments, so that they can bring those solutions forward and build a greater whole,” Dr. Gray said.
Lesson 2: Build AI Oversight Into Existing Governance
Mayo’s governance model combines AI-specific oversight with adaptations of processes the organization already uses for clinical care and research, Dr. Gray said. Many AI models move through a research protocol and Mayo’s institutional review board, which also reviews the safety of clinical trials. “We approach it in the same way that we approach bringing other evidence-based tools into our practice,” Dr. Gray said. “How can we be sure that they are evidence-based, that they have been tested, and that they are reliable?”
Adding a Dedicated AI Implementation Leader
As the volume of AI work grew, Mayo added more AI-specific oversight, appointing Micky Tripathi, PhD, former national coordinator for health information technology at HHS, as chief artificial intelligence implementation officer in 2025. Dr. Gray said Dr. Tripathi leads Mayo’s AI implementation and adoption team, which helps determine when evidence is strong enough for a model to enter practice, whether it can be deployed safely, who will own it once in use, and how its performance will be monitored over time.
Lesson 3: Build AI Skills Both Centrally and Within Departments
Mayo has taken a two-track approach to building the workforce needed to support its AI work, Dr. Gray said. The organization has recruited AI leaders and engineering talent at the enterprise level, including through Mayo Clinic Platform, its data and digital innovation ecosystem that connects clinical data, technology developers, researchers and healthcare providers. Individual departments and divisions are also hiring their own AI engineers and data scientists.
A Voluntary Reskilling Program With Strong Uptake
Mayo is also developing AI skills among employees already at the organization, incorporating AI capabilities into existing job descriptions and degree programs across its five schools, while a voluntary upskilling and reskilling program supported by philanthropy from the Harper Family Foundation allows employees to select training based on their work area. In 2025, 20,000 employees enrolled in the program. “So that rate of adoption and uptake, to me, is the test of whether we’re getting it right,” Dr. Gray said.
Lesson 4: Look for AI That Gives Clinicians Time Back
One AI model Dr. Gray pointed to as a successful use case supports radiation treatment planning for patients with head and neck cancer. The model was trained on treatment plans Mayo teams had developed from planning CT scans and can now automatically contour a plan at what Dr. Gray described as Mayo Clinic quality, reducing the work required from physicists and radiation oncologists by 75%.
Why This Example Illustrates a Genuine Win
“I think one of the reasons that’s an example of a win is because it took an issue of quality and consistency, and very high work effort, and converted it to something that was of great value to our physicists and radiation oncologists, who could then spend more time focusing on the patient,” Dr. Gray said.
Lesson 5: Avoid Relying on a Single AI Solution
One of Mayo’s lessons came from an AI project that did not move forward, Dr. Gray said. The organization was seeking a better way to review the extensive outside medical records brought by patients seeking second, third or fourth opinions, and two teams pursued different approaches: one focused on organizing and searching the records, the other pursued a generative AI approach summarizing a patient’s full record in natural language.
What Happened When One Approach Failed
The generative AI project ultimately ended after the health-specific frontier model underlying it did not perform as expected and the technology partner decided not to continue investing in the model, Dr. Gray said. The other project remained funded and is now being used to address the problem. “What we learned was, number one, not to have a single option, and certainly not to have vendor lock-in on a single underlying intelligence engine behind a product that we’re trying to use, because you will need that versatility,” Dr. Gray said.
What This Means for Health Systems Earlier in Their AI Journey
For health systems earlier in their AI work or operating with fewer resources, Dr. Gray said they do not need to replicate Mayo’s approach to get started. He pointed to ambient AI scribes as one area where hospitals can adopt commercially available technology without building their own AI infrastructure. “When we see these ambient listening technologies going into Mayo Clinic and other healthcare organizations, we see a restoration of that relationship and more joy coming back into medicine, and I don’t think you can put a price tag on that,” Dr. Gray said.
Why Mayo’s Scale Doesn’t Preclude Smaller-System Lessons
Given that Dr. Gray explicitly frames commercially available tools like ambient scribes as an accessible entry point, health systems without Mayo’s enterprise-level AI infrastructure or dedicated implementation leadership can still apply the underlying principles, grassroots problem identification, evidence-based evaluation, and workforce training, at a scale appropriate to their own resources.
What These Mayo Clinic 500 AI Models Lessons Mean Going Forward
With Mayo’s portfolio spanning more than 500 models built on a grassroots-plus-governance approach, this experience offers a concrete template for how large health systems can scale AI while maintaining rigorous oversight and avoiding the pitfalls of vendor lock-in. Given the strong voluntary enrollment in Mayo’s upskilling program, 20,000 employees in a single year, other health systems may find that investing in accessible, self-directed AI training drives more genuine adoption than mandatory top-down programs.
What to Watch Going Forward
As Mayo Clinic continues expanding its AI portfolio under Dr. Tripathi’s implementation leadership, industry observers will likely watch whether the organization’s dual emphasis on grassroots innovation and centralized governance continues scaling effectively as the number of deployed models grows further. Given Dr. Gray’s explicit lesson about avoiding single-vendor dependency, health systems building their own Mayo Clinic 500 AI models-style portfolios may increasingly prioritize maintaining flexibility across multiple AI vendors and underlying models rather than committing to one dominant platform.
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