A care manager at a regional health plan picks up the phone to call a member who needs help most. But before she can make that determination, first she has to become an analyst.
That means synthesizing benefits, plan details, months of claims, admission-discharge-transfer feeds, and condition notes. That also means twenty minutes of preparation. And even after all that, the member usually doesn’t pick up.
Across the country, a lead surgeon corners his health system’s data chief with a simpler version of the same frustration. The hospital had bought a beautiful operating-room analytics platform that did everything . . . and then nobody used it, because nobody had time.
His ask: “I just want somebody I can call. I don’t have time to launch my computer and become an analyst.”
Both of these examples point to the same truth, and that truth was the throughline of “The Future of HCLS: Interoperability and AI,” a speaker session at Snowflake Summit 2026.
The hard part of enterprise AI in healthcare and life sciences is no longer getting to the data. It’s getting machines and the people who depend on them to understand what the data means, fast enough to matter.
The Bottleneck Moved
For two decades, interoperability was a plumbing problem: mandates, fax machines, file formats, and eventually cloud data sharing. With zero-copy sharing and protocols like MCP wiring agents directly into systems, moving data is no longer the thing that keeps teams up at night.
So the bottleneck relocated. The new constraint is what we call semantic interoperability—that is, whether an AI agent can correctly interpret the data it can already see. This matters now because natural language is becoming the default way people query their systems. When a clinician or analyst simply talks to the data, the model needs to know what “adherence” means, that a code maps to a real clinical concept, that two tables describe the same patient. Access without meaning produces fluent, confident, and very wrong answers. In this industry, that’s a risk.
One reference architecture made the headroom vivid: letting Cortex agents reason directly over an ontology, using plain Snowflake with no external graph database, delivered a 28-percentage-point lift in accuracy on a biomedical benchmark.
All that said, the most striking part is that the winning design was the simplest one in the bake-off. When the semantics are right, the architecture gets lighter.
What it Looks Like in Production
Three organizations showed what they’ve shipped.
The payer turned that twenty-minute prep session we talked about into under a minute. Information from disparate APIs snaps into one snapshot; AI reads it, surfaces the two or three things worth focusing on, and recommends a next-best action so managers reach more members.
Regeneron’s commercial stack buckled as the business went international. In roughly twelve months the team consolidated tooling, built reusable data products, leaned on zero-copy sharing (80–90% of their data is externally sourced), and added a semantic layer plus a knowledge graph mapping patients to physicians—all modeled natively in Snowflake, with no external graph database. An adherence analysis across 10 million rows that once took four to six weeks now runs in minutes.
Northeast Georgia Health Systems answered the surgeon’s objection directly: an AI-ready OR model of roughly 700 metrics and 400 dimensions behind a voice interface. A clinician asks a question out loud, and the system interprets, queries, and answers in speech, offering intelligent drill-downs into supply costs, turnover times, and first-case starts. The proof of concept ran on everyday parts, with a vision extending to AI glasses, meeting clinicians wherever they are.
The Pattern Underneath
Strip away the stories and the same blueprint appears three times: a trusted data foundation, a semantic layer that captures meaning, then AI in every decision-making workflow. All integrated with a governance framework that’s consistent and on by default, aligned to standards like HIPAA.
The economics follow. Encode meaning once and reuse it, and the cost of each new use case drops while time-to-value compresses from weeks to minutes. The organizations that win won’t be the ones with the most data (almost everyone has plenty). They’ll be the ones whose AI understands what all that data means.
If you’re exploring how to modernize your data foundation, operationalize AI, or create a more trusted and interoperable data ecosystem, Hakkoda and IBM can help. Contact our team to start the conversation today.
