2026 as an Implementation Year for AI in Drug Development

In a recent video interview with Applied Clinical Trials, Angela Zubel, chief development officer, Debiopharm, described 2026 as an implementation year for AI and advanced analytics in drug development. She explained that many technologies had moved beyond pilot testing and were ready for broader adoption across clinical operations. Zubel highlighted opportunities to shorten timelines, reduce costs, and improve oversight through real-time monitoring, AI-supported site selection, and predictive analytics for compound prioritization. While acknowledging ongoing limitations in predictive modeling—particularly in oncology—she emphasized the importance of organizational openness to innovation. Sponsors that proactively standardized data, adopted practical AI tools, and experimented responsibly, she noted, were already seeing measurable gains in efficiency and competitiveness.

Editor's note: This transcript is a lightly edited rendering of the original audio/video content. It may contain errors, informal language, or omissions as spoken in the original recording.

ACT: Looking ahead to 2026, what do you expect will be the biggest inflection point for drug development programs overall?

Zubel: Looking ahead to 2026, I see this as an implementation year. I hope we move from pilots and testing into broader implementation of solutions across clinical trials and drug development. Not every solution will be ready for full-scale adoption, but I think many are.

In our own company, we’ve moved from early experiments a few years ago to actually trying to implement tools in a meaningful way. We’ll see how it goes, but more and more, these solutions are becoming part of daily operations rather than remaining in pilot mode. I believe 2026 will be the year when companies shift from testing AI in drug development to truly implementing it.