The life sciences sector is facing a data crisis that cannot be solved by simply hiring more experts.
Drug development, for example, demands and generates a massive amount of information spanning preclinical data, clinical trials, real-world evidence, and competitive intelligence.
“For most people, there is so much content that it causes cognitive overload,” explains Gowtham A Rao, Board-Certified Physician, Life Sciences Consultant at EPAM Systems.
This means decision-making slows, critical insights are missed, and regulatory submissions are jeopardized. The result is years of delay, millions in lost revenue and untreated patients.
Generative AI and Large Language Models (LLMs) can help by consuming vast datasets, but traditional AI approaches cannot inherently visualize the complex relationships between a genetic marker, a regulatory change, and a market opportunity.
“AI does not think,” Rao cautions. “But it can see patterns, with so much context, that humans cannot.”
So, to unlock the next wave of scientific advancement, organizations must change how they use the technology. AI must evolve from being an assistant to being a co-reasoning partner.
This approach leverages technologies like knowledge graphs, allowing AI to highlight novel connections and unexpected linkages between data points. Scientists can then “co-reason” with the platform, pressure-testing hypotheses and uncovering new development pathways to drive science forward.
EPAM is pioneering this shift in a strategic partnership with Google Cloud, developing platforms that act as true thinking partners for R&D teams.
Accelerating R&D with traceable intelligence
The implications extend far beyond the lab. Take the example of a new drug launch, Rao explains, where any launch delay will cause revenue loss and customer impacts.
Traditionally, companies deploy hundreds of experts over several months to anticipate every potential holdup.
However, EPAM is working with a major pharmaceutical company to transform this workflow using an AI co-reasoning platform built on Google Cloud.
Trust and transparency are paramount. With so much at stake, explains Rao, it is not enough to rely on “black box” AIs that could mislead scientists with hallucinations.
So, EPAM integrates scientists and industry subject matter experts directly into the development loop alongside engineers. They deconstruct complex workflows into granular steps, applying AI to each one. Crucially, Rao notes: “We validate all the content that is generated by the AI.”
Google’s AI tools are critical to this work. EPAM uses Google’s Vertex AI platform to access multiple large language models. Google’s agentic framework is used to orchestrate specialized AI “roles,” such as literature reviewer or medical safety officer.
The user experience is designed with scientific rigor in mind. “We design this complex UI to be self-documenting,” says Rao.
If a conclusion is reached via a reasoning flow, the whole thinking process is mapped and traceable. This ensures every AI-generated insight is auditable—essential for regulatory scrutiny.
The results have been transformative.
In the case of the pharmaceutical launch process, Rao explained: “Through smart prompting and lots of scientific content and a lot of compute, we were able to generate similar or comparable content to scientists working for weeks, maybe months.”
Moreover, he said: “We identified more ideas than the internal core team. That was a powerful ‘ah-ha’ moment for us.”
In the race to deliver life-saving treatments, AI co-reasoning is no longer optional—it is the foundation of future competitive advantage.