How is AI Being Used in Clinical Trials? 5 Key Statistics for 2026

AI in clinical trials isn't a future bet anymore. It’s an operational reality.

The 2nd Annual State of AI in Clinical Trials survey, conducted with Everest Group across senior clinical operations leaders in pharma, biotech, and CROs, captures an industry in mid-transition. One-third of organizations are using AI in a handful or majority of their trials; the remaining two-thirds are in active exploration and pilots. With 82% of organizations using AI in clinical trial operations for 18 months or less, the wave is young but moving fast.

Budgets are rising, and early adopters are pulling measurably ahead. The window to establish a competitive position is narrowing.

Here are five key insights every clinical trial organization should be paying attention to.

1. 92% of Organizations Plan to Increase AI Spend in the Next 12-24 Months

When the majority of organizations are planning to increase AI investment in the next 1-2 years, the signal is unmistakable: AI has graduated from an innovation experiment to a strategic budget line item. Clinical operations leaders aren't asking "should we invest in AI?" They're asking "how fast, how deep, and where first?"

Expected budget increases are coupled with an optimistic outlook on returns: 82% of respondents expect a 2-3x ROI. Most expect to realize those returns in 1-2 years.

What separates leaders from everyone else now isn't budget. The organizations pulling ahead have built the necessary foundation to actually implement that investment meaningfully and scale it effectively.

2. AI Exceeds Expectations in High Volume, Rules-based Workflows

Broadly, only 29% of respondents say AI has met or exceeded expectations to date, a gap that reflects both the ambition of early AI roadmaps and the time required for complex, multi-system deployments to mature. But in targeted, high-frequency workflows, the impact is clear: 46.5% report above-expectation improvements in task/workflow automation. Data cleaning (40.5%) and query resolution (36.5%) follow close behind.

These are the natural first wins. They’re measurable, defensible to stakeholders, and deployable incrementally without disrupting ongoing trials. Those same metrics were reported at an even higher rate by early adopters, landing at 62.2%, 45.9% and 48.6% respectively. 

The organizations seeing above-expectation results now are funding tomorrow's ambitions with today's proof points. Each proven use case builds the institutional confidence, and the budget case, for expanding AI into progressively more complex clinical applications.

3. Early Adopters with 18+ Months of AI Experience Report Stronger Results Across Nearly Every KPI

This is the most significant stat for late movers to know. Organizations with 18 months or more of AI experience reported above expectation results at a higher frequency than everyone else across nearly every performance metric. This gap persists beyond easier initial wins into the harder to move outcomes where the industry is struggling.

Among the more challenging metrics are shortened trial timelines and reductions in protocol deviations. Where just 15% and 26.5% of the broader population report above-expectation results in these areas respectively, those numbers jump to 29.7% and 40.5% for early adopters.

The message this sends is clear: AI’s value in clinical trials compounds over time, and the results that matter most take longer to realize. These types of KPIs don’t improve with a single pilot or deployment. They require AI to be embedded across multiple, interconnected workflows, allowing improvements to build into each other.

The full report breaks down performance across eight distinct KPIs and the gaps are striking. Starting sooner matters, but only when paired with the foundation that makes enterprise-wide deployment possible.

4. ~90% Are Using or Planning to Use AI for Protocol Design and Optimization

While data integration and standardization is the most common active use case today (69.5% currently using AI), protocol design and optimization is emerging as AI's next breakout use case in clinical trials. 58% currently use AI in this way, and an additional 32.5% are planning to do so. That means 90.5% of organizations see this area as a key use case for AI moving forward.

The focus on this workflow makes sense. Poorly designed protocols are one of the most common drivers of trial failure. They trigger amendments, can slow enrollment, and inflate costs. AI addresses this by analyzing historical trial performance, flagging elements that contribute to high burden or introduce risk, and modeling potential outcomes against real-world data before a single patient is enrolled. 

A reduction in protocol deviations is a direct benefit of this use case. The gap noted above between early adopters and the broader population in recognizing this outcome reinforces that protocol optimization and design is one of many workflows requiring multiple deployments and enterprise-wide scale to see real impact. It’s unsurprising that those who moved earlier and built the right foundation are seeing above-expectation results here more frequently than their lagging peers.

5. The Clock Is Ticking: An 18-24 Month Window Before the AI Gap Becomes Structural

The cost of waiting is real, it’s growing, and it worsens when organizations aren’t prepared. The report predicts an 18-24 month window before the gap between AI-enabled leaders and the rest of the industry becomes structural, one that’s difficult to close with technology adoption alone.

It’s no question that AI is delivering value. But the organizations deep in their AI journey are building stronger advantages that accumulate over time. These capabilities compound in ways that simply purchasing the shiny new tool can’t replicate.

Those with refined data infrastructures, embedded governance processes, cross-functional AI literacy, and teams with the confidence to push AI into progressively more complex workflows are the ones getting more out of AI than anyone else. 

For organizations in the active pilot phase, the window is open but narrowing. Those starting today will reach this compounding stage, but the longer they wait to start, the further behind they’ll be.

These stats are a snapshot into the current adoption trends and the value AI in clinical trials is delivering in 2026. Download the second annual State of AI in Clinical Trials report now to go deeper into adoption trends, the move toward virtual twins, and the complete early adopter KPI breakdown that quantifies exactly how much experience matters.