Healthcare leaders are treating artificial intelligence as an enterprise-wide operating model rather than a discrete technology initiative, a shift that is reshaping how organizations approach data sharing. The CMS Interoperability and Prior Authorization Final Rule (CMS-0057-F) is now accelerating this change by forcing payers and providers to share data in ways that voluntary standards never achieved.
Sathiyan Kutty, chief AI officer at Emids, said the transformation runs deeper than many realize. "AI is the new operating system for organizations; they are learning how to take calculated risks while still achieving the outcomes they seek," he said in an interview with Information Security Media Group.
Healthcare has struggled for years with fragmented data environments. Providers and payers have been slow to exchange information, and outdated processes combined with hesitant adoption of interoperability standards locked data in silos. The new CMS rule, finalized as CMS-0057-F, compels a level of collaboration that earlier initiatives could not deliver.
CMS-0057-F: A Forcing Function for Data Sharing
Kutty is optimistic that the rule, combined with advances in agentic AI, will drive data exchange at a scale the industry has not seen before. "I am genuinely bullish that in the next few years, we may see more data collaboration in healthcare than we have ever seen before," he said. The mandate removes the voluntary nature of previous efforts, creating a regulatory requirement that both payers and providers must meet.
This regulatory push is directly enabling AI for Healthcare by giving algorithms access to more complete datasets. Without shared data, AI models often operate on incomplete information, limiting their effectiveness in clinical and administrative settings.
Governance and Talent Gaps
Despite the momentum, Kutty pointed to two areas where healthcare organizations are lagging. AI governance remains underfunded, even as AI deployment accelerates. As models move into operational workflows, the lack of strong oversight frameworks introduces risk. Additionally, the demand for "forward-deploy context engineers"-specialists who can bridge the gap between AI systems and real-world healthcare operations-is growing fast. These roles are critical for scaling AI beyond pilot projects.
Kutty's own career path underscores the blend of technical and business acumen that healthcare AI requires. He previously held leadership roles at Kaiser Permanente, Tesla, and VMware, where he worked with C-suite executives to translate analytics into business outcomes.
Why this matters for healthcare
For healthcare leaders, the message is clear: AI investment must be treated as a core business strategy, not a collection of IT projects. The CMS-0057-F rule is not just a compliance deadline-it is a structural shift that will determine which organizations can use data to improve care and reduce costs. As AI for Executives & Strategy becomes a boardroom priority, the gap between organizations that govern AI well and those that don't will widen. The time to build the right governance and talent pipelines is now.
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