Amid an era of exuberance around AI, John Halamka, MD, President, Mayo Clinic Platform, notes that the apparent “suddenness” of its appearance masks a long arc of work. “This is a overnight revolution, 50 years in the making,” he said, noting that today’s breakthroughs rest on decades of progress in compute, storage, and tooling—combined with a sharp cultural shift that followed the debut of consumer-facing chatbots in late 2022.

This interview was conducted as part of our recently published Special Report on AI 

A 50-Year ‘Overnight’ Shift

According to Halamka, three forces made the current moment possible: technology, policy, and culture. On the technology front, cheap teraflops and near-limitless storage have turned once-exotic experimentation into routine engineering. On policy, multi-stakeholder efforts—such as industry coalitions—now offer guidance on where AI should be used, how it should be validated, and what guardrails are necessary. On culture, he pointed to the mainstreaming of AI discussions across boardrooms and clinical leadership after late 2022, which brought new investment and urgency. The practical upshot, he said, is that products are rolling out quickly not because teams are rushing, but because much of the foundation was built over decades.

Executives Want Outcomes, Not Hype

He said the ask from hospital C-suites is not “give us AI,” but “help us solve business problems.” Thin margins, staffing challenges, and clinician burnout dominate agendas; technologies are judged by their ability to relieve those pressures. “Does a single one of them tell me they need AI? No.” Instead, leaders are looking for ways to improve documentation accuracy and reimbursement, reduce administrative burden so clinicians can work at the top of their license, and extend scarce specialist expertise via model-driven augmentation. He noted that ambient listening has become table stakes in many markets; systems without a credible program risk falling behind peers that are already reducing after-hours charting and improving note quality. The unifying theme, he added, is to map AI initiatives to a health system’s declared strategic objectives rather than letting novel tools dictate the agenda.

Governance, Validation and Risk

In Halamka’s view, governance for AI must be purpose-built for the realities of statistical systems deployed in heterogeneous populations. FDA clearance (for software as a medical device) is important, but insufficient on its own. Local verification—safety, fairness, appropriateness, and effectiveness on an organization’s patients—is essential. “There has to be some validation or qualification for your local population,” he said. Clinical adoption also requires change management; physicians are rarely persuaded by novelty alone. He emphasized proving that new tools save time, improve quality, or lift patient satisfaction.



The risk discussion extends beyond cybersecurity to operational and clinical consequences of model-driven decisions. He encourages leaders to work in communities of practice—industry consortia and trusted forums—to share evaluation methods, bias assessments, and post-deployment monitoring approaches. On the technology pattern front, he differentiated predictive systems (pattern-matching over large cohorts) from generative systems (language-based outputs with variable accuracy) and from agentic orchestration (where systems can take actions). He advised calibrating autonomy to risk: allow automation for low-stakes workflows, require human-in-the-loop for higher-stakes tasks, and avoid fully autonomous control in settings like device dosing.

Designing for Flexibility—and Scaling Beyond Pilots

He advises organizations to architect with modularity so they can swap components as vendors and techniques evolve.