This week, members of the System team attended PlatforMed, the Mayo Clinic Platform’s annual conference on bringing platform thinking to healthcare. We deeply appreciated the opportunity to engage with topics at the frontiers of care and research, engage with collaborators and innovators, and bring back insights to share.    

This year’s theme — “From Pilots to Power: Operating Healthcare as a Platform in the Age of Agentic AI” — summarized a refrain we hear from informatics leaders across the spectrum of healthcare and life sciences. In each session, we listened to executives, researchers, policymakers, and technology leaders discuss in depth and with candor the challenges presented by current models, as well as the opportunities presented by advances in data, AI, and automation.

Here’s what stood out most from what we heard.

Data architecture as a clinical imperative

During his keynote address, Mayo Clinic President and CEO Dr. Gianrico Farrugia elegantly restated his core imperative that “data architecture is at the center of care delivery,” underlining his remarks earlier this year at the World Economic Forum — that existing data structures constrain innovation and are proving to be wholly inadequate to meet the needs of humans and AI to improve patient care and outcomes. This is the systemic problem that our Clinical Graph was built to alleviate; we too believe in transforming health through this lens.  

Cheristi Cognetta Rieke, Vice Chair of Mayo Clinic’s Department of Nursing Transformation, further emphasized the need to reshape the paradigm of what AI solutions are meant to provide to front-line caregivers, saying: "Instead of giving time back, AI helps you stay ahead and correctly anticipate patient needs." In other words, proactive care depends on how information is structured and connected, not simply what models can do.

The maturation arc: from agents to infrastructure

In a conversation about agentic AI in healthcare, Dr. Adam Landman from Brown University Health and Micky Tripathi, Chief Artificial Intelligence Implementation Officer at Mayo Clinic, weighed in on the past, present, and future of the space. Consensus was that if the theme of last year was generative AI, agentic AI would be the theme of 2026. As for the next wave? Data architecture and AI governance.

This aligns not only with what we heard about care delivery, but with analyst findings on how agentic AI technologies and practices are evolving across sectors. Per Gartner, only 17% of organizations have deployed AI agents to date, yet more than 60% expect to do so within the next two years. Given this delta between intention and execution, the conversation is shifting from capabilities alone to governance, cost, and the infrastructural foundations that enable agents at scale, from resident architecture to semantic layers and knowledge graphs. 

The execution gap and the call for context

Among the most interesting discussions were those unpacking bottlenecks to progress and performance. Panelists often noted that their organizations and the sector at large don't lack for scientific knowledge or cutting-edge technology, but are missing the operational and architectural connective tissue — the necessary layer that dissolves healthcare silos through assembling context and connection — to deploy them in concert for the most innovative and effective outcomes.‍

Farhana Alarakhiya, Chief Data Innovation Officer at Aga Khan University, underscored the need to advance the conversation from focusing solely on innovation, insights, and scale: instead, the next generation of AI solutions must move towards integration, drive action, and ultimately derive the critical context needed in order to provide meaningful impact.

Across the robust agenda, what was perhaps clearest is this: in a moment marked by rapid technical evolution and a consistent discrepancy between promise and performance, organizations making investments in contextual infrastructure and resilient, evolution-ready governance are best poised to assure excellence in care and success in transformation.

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