Healthcare AI is moving out of the pilot phase and into production environments, where the gap between a compelling demo and a clinically trustworthy output has never been more consequential.

As AI agents take center stage, the healthcare and life sciences industry faces a defining question: How do you build AI that practitioners can actually trust? The answer, increasingly, begins with data — not models, according to Jesse Cugliotta (pictured, right), vice president, global head of healthcare and life sciences at Snowflake Inc. From fragmented patient records and fax-era workflows to the promise of AI-summarized charts, the industry’s most stubborn problems are structural, he explained, and solving them requires a data foundation built long before a model is ever trained.

“The average patient chart is 46,000 words,” Cugliotta said. “That’s the length of Fahrenheit 451. If you’re walking into an emergency room and the physician has 27 other patients, they’re not reading through your entire chart history, even if they could get access to it. The ability to leverage AI … is to provide a more complete picture, to understand what is the most accurate way to work up this particular patient … that really has a critical impact not just on the caregiver’s day-to-day experience, but on the patient outcome as well.”

Cugliotta and Amit Sangani (left), chief technology officer of Komodo Health Inc., spoke with theCUBE’s Dave Vellante and Rebecca Knight at Snowflake Summit 2026, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how data foundations enable trusted healthcare AI and patient journey insights through Komodo Health’s Marmot platform on Snowflake. (* Disclosure below.)

Healthcare AI trust built on patient journey data

The structural challenge in healthcare AI runs deeper than model selection. Patient data flows through disconnected systems — claims, labs, prescriptions, provider records — with no single organization holding a complete longitudinal view. Komodo Health has spent a decade assembling that foundation, stitching together more than 350 million patient journeys across its Healthcare Map, Sangani noted.

“[When] a patient goes to a doctor, gets diagnosed, then gets a prescription, the data is all over the place. How do you combine all of that data for a single patient and create a full journey? You aggregate that across different segments or therapeutic areas. Once you do that, it becomes really powerful,” Sangani said. “In the world of AI, deterministic answers are very difficult to get, but because we build all the guardrails, we’re able to provide that to our customers.”

That transparency is the competitive differentiator for Komodo’s Marmot platform, which layers multi-agent orchestration on top of the Healthcare Map using Snowflake for storage, compute and governed caching. A Snowflake research study found that 85% of healthcare leaders view interoperability as foundational to scaling AI, a finding that maps directly to Komodo’s architecture, where every analytic step is logged, auditable and reproducible. For life sciences analysts running deep research on GLP-1 therapy migration patterns or clinical trial cohort design, every step in the workflow can be inspected, modified, or replicated, Sangani explained.

“These healthcare providers, when they ask a question, they don’t want just an answer. They want to understand what is the underlying data providing and how did you arrive at that answer? They want to see every step. The first step is the plan. How is the AI going to create a plan and help me understand what cohorts they are creating, how they are doing the filtering, what SQL is being written, what is a Python, which basically generates the report,” he said. “That gives them the trust that when the final answer comes, it’s like a kid doing a math problem. If they just give you an answer, you say: ‘What are the steps you followed?’ That’s exactly what we are doing.”

Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of Snowflake Summit 2026:

(* Disclosure: TheCUBE is a paid media partner for Snowflake Summit 2026. Sponsors of theCUBE’s event coverage do not have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE