In this video interview, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, outlines the leadership priorities, team structures, and boundary-spanning capabilities that separate organizations that realize meaningful AI gains from those that struggle to move beyond the pilot stage.
In a recent video interview with Applied Clinical Trials, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, discussed how AI-driven tools are reshaping clinical operations across the full trial lifecycle—from case intake and workflow design to patient enrollment, data collection, and the emerging frontier of agentic AI. He opened by framing the current state of AI adoption around two core use cases: knowledge synthesis and applied intelligence, while making clear that the most significant barriers to successful implementation are no longer technological. The real obstacles, he argued, are the willingness to fundamentally reimagine business processes with AI by design, and the organizational commitment to upskilling the workforce to use these tools appropriately.
Cheriath emphasized that effective AI adoption requires balancing ambitious moon-shot objectives with bottom-up confidence building, and that clinical operations teams across biopharma and CROs struggle to carve out the space and time needed for meaningful innovation when day-to-day demands are relentless. He also stressed the need for boundary spanners—people who can fluently bridge clinical operations expertise and AI capability—as a critical and currently undersupplied organizational asset.
On patient centricity, Cheriath brought a notably grounded perspective, drawing on his own family's healthcare experience to argue that technology alone cannot solve enrollment and access challenges rooted in social determinants of health. He made a compelling case for using AI to reduce site administrative burden as an indirect but powerful lever for improving patient focus and care quality at the site level.
He closed with a detailed and pragmatic framework for thinking about agentic AI's impact on the clinical workforce, introducing an augmentation scale from level zero through level four and predicting that virtually all clinical trial roles will fall somewhere between levels one and three within two years—a shift he described not as a future possibility but as a workforce planning imperative for today.
