Why Interoperability Is the Make-or-Break Bet for AI in Healthcare
The organizations investing in data connectivity today won’t just be ready for AI—they’ll define what AI-powered care looks like tomorrow.
Healthcare’s AI moment has arrived. But most organizations are arriving to the starting line with one leg tied behind their back.
According to Rhapsody’s State of Interoperability Report, 76% of healthcare leaders identify advancing data integration and interoperability as a top strategic priority for the next 12 to 24 months. That number should prompt every CIO, CMO, and CEO in the industry to ask a pointed question: if three-quarters of us say this is critical, why are so many of us still running fragmented, siloed operations that prevent AI from scaling beyond the proof-of-concept stage?
The answer is uncomfortable but clarifying. We’ve been treating interoperability as a technical obligation—a compliance checkbox, a legacy IT problem, someone else’s budget line. That framing has cost us years. It is now costing us our competitive position in the most consequential technology transition healthcare has ever seen.
The AI Tax on Poor Data Infrastructure
The Rhapsody report introduces the Digital Infrastructure Maturity Model (RDIMM), a four-stage framework—Connect, Standardize, Optimize, and Innovate—that precisely maps where organizations sit on the road to AI readiness. Most of the data suggests, that it remains in the earliest stages.
The operational drag is already measurable. Nearly half of IT teams spend more than ten hours per week troubleshooting integration issues alone. One in four organizations is burning over 20 hours per week on that same problem. That is not a technical inconvenience. That is a direct tax on your AI investment, one that compounds with every new tool, every new vendor, every new model you bring online.
A 2025 HIMSS poll underscores the broader stakes: 57% of U.S. physicians cite interoperability as the single greatest barrier to realizing health IT’s full value. Not reimbursement complexity. Not staffing. Not regulatory burden. Interoperability. The clinical workforce is telling us something the business case confirms—fragmented data doesn’t just slow innovation, it undermines the trust that AI-driven care depends on entirely.
Four Stages, One Strategic Imperative
The RDIMM framework is useful not because it is novel, but because it is honest. It tells organizations where they actually are, not where their vendor roadmaps claim they should be.
Stage 1 organizations are connecting core systems and eliminating the most brittle point-to-point interfaces. Stage 2 organizations are standardizing data governance, adopting FHIR and API-first architectures, and creating the conditions for AI pilots to produce outputs that clinicians can trust. Stage 3 organizations have embedded AI into operational workflows and are beginning to see measurable returns in scheduling efficiency, prior authorization throughput, and clinical documentation. Stage 4—the Innovate tier—is where AI is no longer a capability. It is a core competency. Self-healing data pipelines, ambient documentation, intelligent triage, real-time financial forecasting: these aren’t speculative outcomes. They are operational realities for organizations that invested in interoperability five years ago.
McKinsey estimates that AI built on interoperable platforms could generate $200 to $360 billion in global healthcare savings. That is not a forecast for the distant future. That is the return on infrastructure decisions being made right now.
Leadership Is the Variable No One Wants to Talk About
Here is what the Rhapsody report gets exactly right, and what most technology assessments miss entirely: the limiting factor is not the technology. It is the leadership’s will.
Organizations at the highest maturity stages share one attribute that has nothing to do with their stack. Their C-suite has made a deliberate, visible commitment to treating interoperability as a strategic asset rather than an IT cost center. They have aligned financial planning, workforce design, and patient experience strategy around a shared data vision. They have given cross-functional teams the authority and the resources to execute.
The organizations still stuck in Stage 1 are not short on ambition. They are short on executive alignment. They are running AI pilots in pockets while their underlying data infrastructure remains fundamentally disconnected.
The Window Is Narrowing
The competitive gap between Stage 1 and Stage 4 organizations is widening every quarter. As AI evolves from isolated models to agentic systems capable of reasoning and acting across the enterprise, the cost of poor interoperability will not grow linearly—it will accelerate.
The question every healthcare executive should answer before the next board meeting is not “are we investing in AI?” It is “have we built the foundation that makes AI worth investing in?”
Interoperability is that foundation. The organizations that recognize it now will not merely adopt what comes next in healthcare. They will build it.