The pharmaceutical industry’s research and development marathon — stretching 10 to 12 years from hypothesis to approval — is set to shrink as AI-driven drug development compresses that timeline.

The shift from AI experimentation to real business workflow transformation is accelerating across industries. Yet few examples carry higher stakes than pharma, where a drug’s failure at phase three of a clinical trial means patients have been waiting nearly a decade for nothing. Governing data at enterprise scale is the prerequisite for that transformation, according to Emmanuel Frenehard (pictured, left), chief digital officer of Sanofi S.A.

“We’re starting to simulate even clinical trials before they happen,” he said. “We still have to do them, but we do them with more precision. We do them with a better appreciation of the kind of patients we need in those trials. My intention is to have the time it takes, so from 10 to 12 to five to six.”

Frenehard and Dayne Turbitt (right), senior vice president of EMEA at Snowflake 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 AI-driven drug development, agentic workflows and how a unified data foundation enables AI to reinvent enterprise processes. (* Disclosure below.)

AI-driven drug development and the case for unified data

Frenehard laid out a striking vision for how AI can accelerate the pharmaceutical development cycle at every stage. A core strategic principle at the company is that 90% of drugs still fail in phase three of a clinical trial, close to the finish line, with patients already waiting. For Turbitt, the moment is significant because it represents a new motivation for data modernization — one that goes beyond cost or efficiency.

“What’s interesting about this time is we’ve gone through this period of tech rationalization where you’ve moved data from legacy systems to modern systems,” he said. “What’s different about now is there’s a new reason to do that. And the reason to do that is in the way you want to change work and workflows.”

Achieving that ambition requires a single, governed data environment as the foundation. Fragmented data is the enemy of AI precision, both speakers agreed. Sanofi’s answer is to run all workflows directly on a unified platform, treating Snowflake as an operating system rather than a database. This strategy removes the need for intermediary systems and ensures intelligence is never siloed, Frenehard explained.

“All our data shall be on Snowflake and shall be what we call AI-ready data, which means it’s governed, which means [it’s] of quality we can trust,” he said. “The workflow is what runs on top of the data. Snowflake is an operating system; it’s not just a data platform.”

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

(* Disclosure: TheCUBE is a paid media partner for Snowflake Summit event. Neither Snowflake, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Photo: SiliconANGLE