Industry leaders share real-world insights on using agentic AI to streamline clinical trials, accelerate decisions, and improve study execution.

The ground has shifted for FSO (functional service outsourcing) and Unitized FSP (functional service providers) contract research organizations (CROs). 

For years, the playbook for managing these types of CRO economics was familiar. Sponsors negotiated rates while CROs managed headcount and utilization around those rates .

That playbook still holds true today in theory. However, the conditions underneath it have shifted enough that it no longer produces the results it used to. Funding is tighter, sponsors are smaller and more price-sensitive, timelines are compressing ahead of the patent cliff, and AI has moved from an experiment on the roadmap to a baseline expectation in every RFP.

None of that is unique to any one segment of the market, but these pressures land differently for FSO and unitized FSP providers than they do for full-service CROs. That’s because these businesses are built on rate cards for CRAs, monitors, and other functional resources. When sponsors squeeze rates or expect more output per unit, there's no broader program fee to absorb the hit, here the unit economics are the business.

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The way clinical trials are monitored is once again about to change. 

This isn’t the first time clinical trial monitoring has evolved. Over the past three decades, the industry has undergone two major transformations in how monitoring is performed.

For much of the 1990s and early 2000s, monitoring relied on frequent on-site visits and extensive source data verification (SDV), with many studies aiming to verify nearly every data point. While rigorous in intent, this approach became increasingly difficult to sustain as trials grew larger and more complex, delivering diminishing returns relative to its cost and operational burden.

The industry responded by adopting risk-based monitoring (RBM) and centralized monitoring, shifting from exhaustive verification to a targeted, data-driven approach focused on the risks that mattered most. This evolution was reinforced by FDA and EMA guidance and ultimately codified in ICH E6(R2) in 2016.

While RBM improved efficiency and data quality, it did not fundamentally change how monitoring work was performed. CRAs still spent much of their time manually reviewing data, reconciling information across systems, documenting findings, and coordinating follow-up activities. 

We are now entering a third shift. Unlike the first two, which primarily redistributed how monitoring effort was allocated, this one fundamentally changes the old rules on who and what is monitoring trial performance. 

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