Clinical trial software company Medidata’s second annual survey of AI adoption in drug development, conducted with Everest Group and covering senior decision-makers at pharmaceutical and biotech sponsors, found that 72.9% of organizations with more than 18 months of hands-on AI experience report measurably reduced clinical trial timelines, and 67.5% report fewer protocol deviations — the errors and inconsistencies in how a trial is actually run that can compromise data quality or trigger regulatory scrutiny. Yet the same survey found that only a minority of respondents overall say AI has met or exceeded their original expectations, pointing to a gap between what experienced adopters are achieving and what the broader industry has managed so far.
Why Clinical Trials Became an AI Target in the First Place
Running a clinical trial involves enormous amounts of structured and unstructured administrative work — screening patient records for eligibility, monitoring sites for protocol compliance, reconciling data from multiple contract research organizations, and manually reviewing adverse event reports — much of which has historically been done by teams of clinical research associates working through spreadsheets and paper-based processes largely unchanged for decades. That operational overhead is also one of the biggest drivers of a widely cited industry statistic: that bringing a new drug to market can take over a decade and cost more than a billion dollars, with clinical trials consuming the largest share of both time and expense in that process.
Where the Gains Are Actually Showing Up
According to Medidata’s survey, 86.5% of respondents report improvements specifically in “high-volume, rules-based workflows” — the kind of repetitive, well-defined administrative tasks, like document review or data entry reconciliation, that are easiest for current AI tools to automate reliably. That pattern echoes a broader trend across many industries adopting generative and predictive AI: the clearest near-term wins tend to come from automating narrow, repetitive processes rather than from AI making complex scientific or strategic judgment calls, which remain squarely in human hands.
The Optimists’ Case
Lisa Moneymaker, Medidata’s chief strategy officer, argued the 2026 data shows the industry has “moved past the era of speculation,” pointing to the 92% of surveyed organizations that say they are increasing AI investment and the 82% that expect a two-to-threefold return on that spending. Advocates point to the compounding nature of the reported gains among experienced users — faster timelines and fewer protocol deviations together suggest AI tools are not just saving time but potentially improving data quality, which could eventually translate into faster, cheaper, and more reliable drug approvals across the industry.
The Skeptics’ Case
The survey’s own framing — distinguishing sharply between organizations with 18-plus months of experience and the broader respondent pool — implicitly concedes that most sponsors have not yet achieved the gains more experienced adopters report, and the fact that only a minority of all respondents say AI has met or exceeded expectations suggests a meaningful portion of the industry’s investment so far has produced underwhelming results. Clinical trial experts have also cautioned that improvements in “rules-based” administrative workflows, while genuinely useful, are a different and much lower bar than the harder problems AI advocates have long promised to solve — things like predicting which patients are likely to respond to a given therapy, or designing smarter adaptive trial protocols — areas where the survey data shows comparatively less traction.
What’s Next
With 92% of sponsors planning to increase AI investment regardless of mixed results so far, the gap this survey identifies between experienced adopters and the broader industry is likely to narrow simply as more organizations accumulate the 18-plus months of hands-on experience the data suggests is the real threshold for meaningful returns. The more interesting test over the next year or two will be whether sponsors can push AI tools beyond rules-based administrative automation into the harder, more scientifically consequential parts of trial design and patient matching — the areas where Medidata’s own data suggests the industry still has the most ground to cover.
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