Clinical trial sponsors are navigating a shifting landscape; NIH funding cuts are fueling site staff shortages, and FDA resource reductions are risking slower regulatory reviews. At the same time, evolving reimbursement frameworks within healthcare are impacting budgets and heightening cost concerns.
The result? Less capital flowing into drug development programs, decreased research capacity at sites, and slower studies.
In this constrained environment, AI has become essential. Sponsors are seeing new efficiencies and gaining more agency across the entire trial experience, from study design to study feasibility and site selection.
Predict More, Succeed More
Every sponsor’s end goal is to design a developmental program with the highest probability of clinical and regulatory success for the largest population.
AI-powered solutions like Medidata’s Trial Design or Simulants directly support this strategic clarity, introducing an unprecedented level of study predictability. Sponsors can:
- Model different trial scenarios, increasing the likelihood of success
- Predict and overcome obstacles and fine-tune the sample size
- Predict efficacy and identify high-risk patients
- Generate comparative evidence for clinical development decision-making and regulatory submissions
- Identify “impossible” to recruit patient populations
- And so much more…
Sponsors are getting answers to their “what ifs” and accounting for potential hurdles or roadblocks. Notably, for some therapeutic areas, AI-powered tools can even help sponsors gain accelerated approvals through the establishment of surrogate endpoints.
Solutions like Medidata’s Synthetic Control Arm (SCA®) can match patients from historical trials to patients in a sponsor's prospective trial, enabling a comparative analysis.
“Synthetic Control Arms transform historical trial data into scientifically valid comparators, supporting evidence generation in clinical trials where randomization is less feasible and helping accelerate patient access to innovation.”
– Ruthanna Davi, SVP Clinical Data and Regulatory Innovation, Medidata
For many rare diseases, there may be few eligible patients for a sponsor to enroll; this makes it impossible to build out a traditional control arm. Enabling a single arm trial through a synthetic control arm that’s built off similar patients in historical trials can empower sponsors to pursue new treatments that these patients desperately need.
Optimized from the Start
The ability to forecast trial timelines, costs, and other factors is essential for sponsors making the right decisions at the right time to maximize value.
The protocol design process can take anywhere from months to years to complete, while remaining prone to delays or amendments after the trial has started. AI-powered tools can analyze these documents and significantly streamline optimization by proactively identifying areas of improvement, including recommendations that can cut costs or reduce the anticipated burden a patient will experience (a.k.a., “‘patient burden score”’) when participating in the trial.
“Think about protocol complexity and what that does for operational uncertainty, and how it impacts enrollment challenges. What’s the cost of the additional procedures that we're asking sites to do or patients to experience? What’s the cost of the site having to do additional training for their staff to learn a new procedure or activity? How does all of this reflect how you’re going to enroll or how you're going to model out your financial success?”
– Meghan Harrington, VP, Clinical Trial Financial Management
When staffing and funding pressures challenge sites’ capacity to execute complex assessments, AI can also suggest validated digital alternatives (such as eCOA or sensors) to be used in place of manual, individually led assessments; this eases the workload on sites without compromising endpoints.
“We can look at many different protocols, understand where those different instruments are being used, whether they’re reliable surrogates, and use AI to help configure them for the sponsors to avoid cost and time disruptions.”
– Jacob Aptekar, VP, Data Science & AI
Amendments to protocols can be costly and negatively impact trial timelines, making it even more important for sponsors to get it right the first time.
Site Selection and Performance Monitoring
AI can capture and analyze historical data across various sponsors, standardizing the performance of individual studies, sites, and entire countries. This includes patient enrollment speeds and the consistency and quality of captured data.
Armed with these granular, site-level metrics surfaced through solutions such as Medidata’s Intelligent Trials, sponsors can select the sites best suited to their study from the start by leveraging historical operational and diversity performance data. Even current factors, including the number of active trials or competitive studies a given site is already running, can be referenced throughout the selection process.
Once a study is underway, AI lets sponsors detect early signs of strain and underperformance before they escalate. With visibility into global site benchmarks, sponsors can compare relative performance and initiate timely shifts—whether by redirecting resources or activating new sites in different regions.
“Say all your sites are slumping. You may not know there are sites doing better within or outside of the US. We have the ability through Medidata’s Study Feasibility Tool to help sponsors identify new sites if they need, and start them up.”
– Jacob Aptekar
A Future-Ready Framework
The current study build process is still cyclical and time-consuming, with many meetings and reviews at each step. With AI, we see this process evolving, replacing long review cycles with a faster, more efficient test-driven development process.
“What’ll be really transformative is getting the study build process so seamless that it mimics test-driven development where builders build a study quickly, test it, work through the errors, eliminate them, test it again, and then it generates the synthetic test data and test scenarios. You reduce all this time and effort; we don't need days of meetings at every stage.”
– Jacob Aptekar
Simulation capabilities are expected to grow in many ways. LLM-powered organ simulations, for example, present an opportunity for researchers to model drug efficacy at a biological level before human trials begin.
Improvised chat bot experiences are also on the horizon. Sponsors may one day be able to directly ask questions of historical data and receive even more personalized recommendations for their studies. What emerges is a new era in which sponsors define the outcomes they care about most, with AI working backwards to recommend optimal trial strategies, automatically propagate protocol changes, and more.
In a period of tighter budgets, fewer hands, and higher stakes, AI isn’t just enhancing clinical trial operations; it’s becoming essential to making sure they happen at all.
A Unified Experience Driven by AI
When we look at the power of AI, it’s imperative that we do so through a unified lens. Threading these benefits together creates an experience that is smarter, more efficient, and more effective at its core.
When sponsors leverage AI to design smarter protocols, sites spend less time troubleshooting and more time engaging with patients. When sites are better supported, patients enjoy a more consistent, less burdensome experience. And when patients can participate more easily and reliably, sponsors acquire cleaner data, faster approvals, and more meaningful outcomes.
AI helps everyone, but its real promise shows through when it’s connecting all of us.
Get a closer look at the benefits for patients, and learn more about the impact for sites.
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