Commentary|Videos|February 27, 2026

Clinical Ops Leaders Must Prioritize Data Flow and Scalable Workflows

Jonathan Andrus, co-CEO of CRIO, discusses how governance across the data lifecycle, site-focused technology adoption, and scalable AI-enabled workflows will define operational readiness in 2026.

In a recent video interview with Applied Clinical Trials, Jonathan Andrus, co-CEO of CRIO, discussed how 2026 is expected to mark a continued shift toward site-based technologies and protocol-driven eSource to improve data quality, compliance, and trial efficiency. He emphasized the importance of capturing high-quality data at the point of patient encounter, reducing fragmentation across sites, and enabling real-time data access and monitoring. Andrus also highlighted the growing need for cross-functional collaboration during protocol design, stronger governance across the data lifecycle, and increased use of AI to streamline study build, data review, and operational workflows.

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 the rest of 2026, what should clinical operations leaders prioritize now to stay ahead of execution and compliance challenges?

Andrus: I would highlight a few key priorities. First, design source systems and downstream environments together during protocol development—don’t try to retrofit them later.

Second, evaluate technology based on site adoption and usability, not just feature sets. Reducing site burden is critical, and we’ve seen that firsthand working with a large network of sites using eSource workflows.

Third, build stronger governance across the entire data lifecycle—from initial patient data capture through database lock. That includes clear ownership, standardized documentation, and being inspection-ready at all times.

And finally, continue investing in scalable, repeatable workflows, including the use of AI. While AI is often discussed at a high level, there are practical applications already being implemented—such as using protocol inputs to automate study design elements or streamline workflow configuration.

These approaches are already improving efficiency, reducing manual effort, and helping teams move faster, and I expect that momentum to continue into 2026.


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