Feasibility has long been one of the most consequential, and most fragile, stages of clinical trial planning. Decisions about where to run a study and which sites to involve shape everything that follows, from enrollment speed to data quality to overall timelines. Yet feasibility has traditionally relied on limited historical experience, manual surveys, and assumptions that don’t always hold once a trial is underway.
Data-driven feasibility is changing that equation. By bringing together richer clinical, operational, and physician-level data, teams can move beyond educated guesswork toward more objective, repeatable decision making. AI plays a supporting role by accelerating analysis, improving matching, and helping teams navigate complexity at scale.
One of the biggest shifts is moving feasibility earlier in the planning process. Instead of validating a nearly final protocol against a narrow set of sites, modern approaches allow teams to explore feasibility questions while protocols are still taking shape. This makes it possible to adjust inclusion criteria, visit schedules, or geographic strategies before they become constraints that are costly to unwind.
AI-enhanced feasibility also broadens the lens. Rather than focusing only on familiar investigators or regions, teams can identify sites and physicians they may not have worked with before, including those with access to relevant patient populations but limited prior trial exposure. This can be especially valuable in competitive or rare disease settings, where traditional site networks are quickly saturated.
Speed is another benefit, but not the only one. Faster site identification helps accelerate startup, but the larger value comes from improved alignment between protocol demands and site capabilities. When feasibility is grounded in real-world data about patient availability, investigator experience, and prior performance, studies are more likely to launch with realistic expectations.
As with other AI-enabled capabilities, transparency matters. Feasibility insights must clearly show what data they are based on and how conclusions were reached. Teams need to understand why certain regions or sites are recommended and what trade-offs are involved. Without this clarity, AI-driven recommendations risk being treated as black-box suggestions rather than actionable guidance.
Feasibility is also increasingly connected to execution. When feasibility insights feed directly into enrollment forecasting and ongoing performance monitoring, teams can maintain continuity between planning and delivery. This reduces the disconnect that often occurs when assumptions made at startup are forgotten once a trial begins.
Reimagining feasibility is not about eliminating human judgment. It is about strengthening it with better information. Data and AI help teams see possibilities they might otherwise miss and challenge assumptions that no longer fit the reality of modern trials.
As clinical development grows more complex and competitive, feasibility can no longer be a one-time checkpoint. It must become a dynamic, data-driven capability that supports smarter decisions from planning through execution.