Scaling AI in Clinical Trials: 4 Lessons for Moving From Pilots to Practice
Our industry is no longer debating whether to use AI in clinical research. The question now is how to achieve enterprise-wide scale across the everyday workflows powering trials.
Most organizations are still working it out. Only a third have moved AI into implementation or deployment according to Medidata’s 2026 State of AI in Clinical Trials survey1, conducted by Everest Group. Investment isn’t the problem: 92% of respondents expect AI spending to rise. With only 29% reporting that AI has met or exceeded their expectations, it’s about turning that spend into measurable value.
Closing the gap between optimism and impact takes more than just picking the “right” tool. Here are four lessons for moving AI from pilots to practice, with insights from leaders at Worldwide Clinical Trials, Everest Group, and Medidata following a recent webinar hosted by Fierce Pharma.
- Even the “best” tools and capabilities will fail to scale when an organization doesn’t have the right infrastructure. Fragmented data and disconnected systems are among the biggest obstacles.
- Scaling AI meaningfully takes a connected platform, the right partners, and a plan to bring people–and processes–along.
1. The barrier isn’t capabilities. It’s connectivity.
An AI tool can perform well in isolation and still fail to scale. Scaling requires connecting it to the data, systems and workflows around it. When the infrastructure isn’t right, many initiatives stall.
The 2026 report respondents’ top barriers are integration complexity (79.5%), model accuracy (77.5%) and fragmented data foundations (75%). Governance adds another layer; Everest Group Vice President, Nisarg Shah, called it one of the biggest hurdles to scale. These barriers can easily be missed inside a contained pilot, but surface when the pilot tries to grow.
“The more silos you have, the more hurdles you’ll likely run into when you’re trying to scale AI. Siloed pilots can succeed, but the moment you try to scale them, they will eventually hit a wall, because you’re trying to solve for a lot of factors that were not foreseen during the pilot itself. It’s like trying to drive a Ferrari on a potholed road.”
– Nisarg Shah, Vice President, Everest Group
Nik Morton, Chief Strategy and Transformation Officer at Worldwide Clinical Trials, reached the same conclusion: “The most impressive AI capability will struggle to generate meaningful value if the underlying data sits across multiple systems, multiple processes, multiple teams.” That realization shifted the company’s focus from just picking an AI tool to getting the data, operating model and technology platform right.
2. Point solutions are easy, scaling takes a platform.
Point solutions are multiplying, and it’s easy to see why: standing up an AI use case has never been simpler. But a collection of disconnected tools doesn’t add up to scale. What sets a scaling partner apart, according to Nisarg, is domain expertise, a deep partner ecosystem and interoperability built in from the start, not a slide full of use cases.
Worldwide Clinical Trials evaluated virtually every option, from frontier AI companies to point-solutions and emerging start-ups. Nik expressed most of these discussions came back to, “not who’s got the capability to build a good AI solution, but how is that going to be best informed by the best data set.” That focus on high-quality, accessible data that keeps growing over time was one of the reasons the company partnered with Medidata, drawn by its massive dataset informed by 42K+ trials and 13M+ patients over the past 25+ years. It’s also why a platform became so compelling: it connects that data with new ways of working.
The philosophy behind Medidata Plus starts from the same place: focusing on what the technology can do and how organizations both adopt it and measure its value. Rather than “bolting on tool after tool after tool,” Tom explained, the goal is a single platform where “every step that you take is deliberate.” And as consumption-based pricing leaves many leaders weighing ambition against “a token budget,” Medidata Plus centers instead on the question, “What is the business outcome we’re driving?”
3. Trust at scale is a shared responsibility.
As AI moves into core trial workflows, who keeps it in check? Regulators continue to hold sponsors, investigators and care teams accountable, whatever technology they use. The providers behind that technology share responsibility for making effective oversight possible.
Tom pointed to a long-standing industry standard: “In life sciences, we’ve never been a black box type industry.” That expectation of evidence extends to AI: transparency into why a model made a decision, insights that are fully explainable, and a comprehensive audit trail.
Choosing an AI partner is just as much a governance decision as it is a technology one. Look for explainable models, formal validation before deployment, continuous monitoring after it, human-in-the-loop workflows and complete audit trails.
4. Trade proof of concept for proof of value.
For many organizations, change management is the hardest scaling hurdle to overcome. Expanding AI across multiple trial workflows asks more people across different teams (each with their own degree of skepticism) to change how they work. Unless the case for change is clear at every level, early enthusiasm can sputter out.
Isolated pilots, even if successful, tend to kick this challenge down the road rather than solving it.
“...we’re trying to ban the word pilot. Many [pilots] succeed because they’re run by a small group of very enthusiastic people working on an exciting new topic, with a lot of focus, a lot of management support, extra investment. The pilot’s successful, but then we can point to failed implementation.”
– Nik Morton, Chief Strategy and Transformation Officer, Worldwide Clinical Trials
The alternative is proof of value: measuring outcomes alongside the effort required, and the satisfaction or frustration resulting from new ways of working. Bringing that experience transparently into the business case, Nik noted, builds trust and prepares the organization for change at scale.
What’s next for AI in clinical trials: from operations to outcomes
From here, the focus of AI in clinical trials is expected to shift from automating and accelerating to autonomous workflows and decision-making with Agentic AI. Tom sees it taking hold first in operations, such as enrollment prediction and document drafting.
Protocol design and optimization remains a break-out use case. Tom discussed simulating a trial before a single patient enrolls, something Nik hopes will help move the needle on the industry’s most persistent challenge: enrolling the patients who match the protocol.
“The work that we’re doing today is impactful today, but it’s also building the muscle to unlock the opportunities of tomorrow. Today, we largely focus on operations. Tomorrow, we largely focus on outcomes.”
– Tom Doyle, CTO, Medidata
Check out the full discussion to go deeper on each of these lessons, get the panel’s advice on how to vet potential partners, and hear what organizations still in exploration should do in the next 12 months.
12026 State of AI in Clinical Trials Report
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