Medidata Report Finds Early AI Adopters Pulling Ahead on Clinical Trial Timelines

Medidata has released its second annual State of AI in Clinical Trials report, and the headline finding is straightforward: organizations that moved earliest on artificial intelligence (AI) are now seeing measurable operational gains, while the rest of the industry is still working through the foundational challenges that make scaling difficult.¹

The independent survey, conducted by the Everest Group, covered 200 senior decision makers across pharmaceutical companies, biotech firms, and CROs.

The most striking data point is that 72.9% of respondents with more than 18 months of AI experience report a reduction in clinical trial timelines. A similar share, 67.5%, are seeing reductions in protocol deviations.

Those numbers stand in contrast to the broader industry picture, where integration complexity, model accuracy concerns, and weak data foundations remain the top barriers to progress, cited by 79.5%, 77.5%, and 75% of respondents respectively.

Investment appetite, at least, is not the problem. Ninety-two percent of respondents plan to increase AI spending, with only 1% expecting a decrease. Expectations for returns are ambitious, with 82% anticipating a two-to-three times ROI and nearly two-thirds expecting to reach that threshold within 12 to 24 months.

Looking further out, respondents identified protocol and operational simulation and digital twins as the top priorities for the next three years.

Trust and governance remain front of mind alongside those ambitions. More than 63% of respondents rated data trust and regulatory compliance as critically important, 64.5% require legal and compliance review of AI applications, and 63% mandate human oversight.

"The 2026 data reveals that we have moved past the era of speculation," said Lisa Moneymaker, chief strategy officer at Medidata, in a press release. "We are now seeing a clear performance gap between those who are scaling AI and those stalled by legacy infrastructure."

The Everest Group framed the findings similarly.

"Early adopters are beginning to demonstrate how sustained investment and deeper integration can translate into broader operational gains, particularly as use cases mature beyond initial pilots," added Chunky Satija, partner at Everest Group, in the release. "Organizations that take a long-term, execution-focused approach will be best positioned to convert early momentum into durable, enterprise-level impact."

Addressing the data layer

The report's findings on data foundations connect directly to work already underway in the industry.

In a recent Applied Clinical Trials Podcast episode, Samir Jain, vice president of product management for healthcare data interoperability and EHR solutions at Medidata, and Jonathan Andrus, co-CEO of CRIO, discussed how their companies' integration partnership is tackling one of the most persistent sources of operational friction in clinical trials—manual data entry between site-level and enterprise systems.²

Jain put the cost of that friction in concrete terms.

"I've read figures where it's upwards of 20% of a trial's cost is attributed in some way, shape, or form to some human having to go in and capture data in one system and manually transcribe it into another system, and the kind of ripple effects that that creates," he said. "It's not just the time spent doing that manual activity, but all of the verification that comes afterwards and the data quality issues and query resolution and all of that. When you start to really aggregate that, it's a material amount of the trial time and money and effort that's being spent on this hugely manual activity."

The recent CRIO-Medidata integration addresses that problem through a direct, plug-and-play connection between site-level eSource and the Medidata Platform, eliminating the need for manual data re-entry and the downstream errors it produces.³

The conversation also explored what that kind of connectivity signals about the broader shift toward a more connected, eSource-driven clinical data ecosystem—and what it means for smaller, resource-constrained sites that have historically struggled to implement advanced integrations.

The full State of AI in Clinical Trials report from Medidata can be found here.

References

1. Medidata’s Second Annual AI Report Shows a Shift from Pilots to Enterprise Adoption with 72.9% of Early Adopters Seeing a Reduction in Study Timelines. News release. Medidata. May 18, 2026. Accessed May 19, 2026. https://www.medidata.com/en/about-us/news-and-press/medidatas-second-annual-ai-report-shows-a-shift-from-pilots-to-enterprise-adoption-with-72-9-of-early-adopters-seeing-a-reduction-in-study-timelines/

2. CRIO and Medidata Partnership Targets Clinical Trial Data Friction. Applied Clinical Trials. May 8, 2026. Accessed May 19, 2026. https://www.appliedclinicaltrialsonline.com/view/crio-medidata-partnership-clinical-trial-data-friction

3. Medidata, CRIO Partnership Advances eSource Integration to Improve Data Quality and Trial Efficiency. Applied Clinical Trials. March 4, 2026. Accessed May 19, 2026. https://www.appliedclinicaltrialsonline.com/view/medidata-crio-partnership-esource-integration-improve-data-quality-trial-efficiency