Why Trial Competition Is Undermining AI-Driven Gains

In a recent video interview with Applied Clinical Trials, Liz Beatty, co-founder and chief strategy officer at Inato, discussed how sponsors can balance efficiency pressures with patient access as competition for sites and patients intensifies. Beatty explained that while AI-driven tools are accelerating protocol design, feasibility, and trial operations, overreliance on a small pool of familiar sites can create bottlenecks that undermine those gains. She highlighted a shift away from historical site performance data toward real-time patient eligibility insights, enabling more accurate trial planning and inclusive site selection. Beatty also outlined how community research sites can remain competitive by using technology to demonstrate verified patient access and readiness. Looking ahead to 2026, she emphasized that sponsors able to move beyond isolated AI pilots—by redesigning underlying processes and committing to scaled change management—will be best positioned to shorten timelines and expand access to clinical trials.

The interview transcript was lightly edited for clarity.

ACT: Why is growing trial competition undermining AI-driven efficiency gains, and what changes are needed to break the site bottleneck?

Beatty: One of the most compelling promises of AI in clinical development is bringing new medicines to patients faster, and I am really passionate about this. We are already seeing real efficiency gains today. Sponsors are using AI to accelerate protocol design, optimize site feasibility, and reduce manual effort in trial operations. All of this meaningfully compresses timelines on paper.

But to truly bring drugs to market faster, every part of the system has to move faster, and this is where trial competition becomes a real constraint. Many sponsors are developing drugs in competitive disease areas, and often they are even competing internally across their own asset teams. Yet we still see sponsors relying on the same small pool of high-profile sites and using the same site lists to decide where trials will go.

This creates congestion. Sites are overloaded, patients are over-contacted, and startup and enrollment slow down. This offsets a lot of the efficiency gains that AI is delivering upstream. To break this bottleneck, sponsors need to rethink how they partner with sites. Success in competitive indications depends on working with the right mix of sites that are qualified, not just the usual ones that make the list, and including investigators with real access to patients who are often underutilized.

When sponsors broaden and diversify their site strategy, they remove bottlenecks, protect timelines, and allow AI-driven efficiencies to actually translate into faster trial completion and faster access for patients.