he COVID-19 pandemic forced the clinical research industry to rethink how trials are conducted and revealed that we could adapt quickly under pressure. Today, with significant economic headwinds, escalating complexity, and global and domestic uncertainty reshaping the landscape once again, we face a new imperative: to evolve from reactive to proactive, leveraging technology to forecast with clarity and confidence.

It turns out that sponsors and contract research organizations (CROs) can navigate today’s volatility using AI-powered study forecasting to move from reactive to proactive planning. With real-time, scenario-based modeling, clinical trial teams can reduce risk, optimize timelines, and forecast with certainty even in an uncertain market.

AI is a key driver to accurately and continuously forecast study feasibility because it can enable granularity at the site, country, region, and study level and across the enterprise portfolio. However, AI accuracy requires comprehensive, harmonized data and built-in capabilities to model complex designs. One solution, for example, is built on historical data from more than 500,000 global clinical trials and covers more than 4,600 indications, including operational metadata such as country approvals and site startup timelines. Unifying clinical, operational, and even behavioral data at the site level in a structured way is key to surfacing actionable insights.

The Next Frontier in Clinical Development

The ability to model feasibility and continuously scenario-plan, even amidst changing dynamics, is the next frontier. This empowers teams to make data-driven decisions that derisk timelines, optimize budgets, and accelerate portfolio value.

Clinical trial planning has long suffered from being static and manual. Teams have historically invested four weeks to six months aggregating assumptions based on past trials, often with incomplete data. That lag delays critical decisions and introduces unnecessary risk. However, AI-powered approaches introduce real-time adaptability into a process of dynamic decision-making that evolves in tandem with the study rather than serving as a static snapshot. This enables a shift from reactivity to proactivity.

Traditional feasibility models often struggle to keep pace with the increasing complexity of trials. Over the past decade, increasing numbers of endpoints, inclusion/exclusion criteria (number or precision), and protocol amendments have elevated trial complexity across all phases and therapeutic areas. These complications, combined with global coordination requirements and shifting regulatory frameworks, challenge accurate forecasting. When assumptions change—and they always do—teams simply cannot update forecasts in kind.

Moreover, recent policy and regulatory changes have introduced additional volatility, and clinical trial teams often lack the ability to anticipate issues or plan effectively. Charting success through all the uncertainty requires that we treat feasibility as a dynamic, continuous learning process.

Here are four real-world examples of how sophisticated forms of AI can help.

① Charting Success Amidst Uncertainty: Enrollment predictions based on precise data

Dynamic AI-driven predictions rooted in historical studies enable sponsors and CROs to model feasibility prior to study startup while reducing manual workload and delivering reliable insight into likely enrollment success. However, current volatility is not captured in historical data. Here, advanced AI can be used to create a foundation for informed predictions based on historically similar studies. This technology also enables flexibility so teams can adjust parameters granularly (i.e., site-level enrollment limits or activation timelines, etc.) to reflect today’s environment. Trial sponsors have leveraged AI-powered pre-study scenario planning to improve forecasting accuracy by 70x and reduce forecast setup time from five weeks to five minutes or less. For example, on day 60 in one global phase 3 hematology oncology trial, an AI-powered approach identified risks to enrollment and accurately predicted that the trial would miss its enrollment target with a 5% error on the actual performance, compared to the 350% error on forecast performance exhibited by the existing naïve forecasting system.

Study teams can dynamically model study feasibility based on different site, region, country, and indication combinations—an attractive feature for real-time clarity. This enables an independent country and site mix perspective for optimal study performance, as study teams want to understand the implications of modifying timelines, adjusting eligibility criteria, or revising geographic region selections. In one case, a top-five pharmaceutical company collaborated with an AI-powered pre-study system to optimize its country and site plan for a complex phase 3 study; this advanced forecasting system provided a faster timeline to Last-Patient-In (LPI) and included two new countries with high enrollment potential to reduce concentration on US sites. It is important to provide flexibility to evaluate these options with confidence, plus the adaptability makes it easier to bring analytics into the operational decision-making process.

② Charting Success Amidst Uncertainty: Enable midstudy course correction

AI forecasting enables continuous monitoring and reforecasting of enrollment performance during study execution, supporting rapid adjustments based on live data. Using a combination of AI models—generative AI for comparisons of eligibility criteria, machine learning for predictive modeling, and causal AI to recommend the optimal country and site combinations—trained on the historical 500,000 studies integrated with live study data, study teams can continuously track enrollment results (i.e., discontinuation, screen failure rates, site activation delays) with all models leveraging the best data possible, a combination of the historical study comparators and live in-study data. One pharmaceutical company leveraged such solutions to rapidly identify and correct site training issues based on significantly higher-than-expected screen failures in specific countries.

Further, study teams can generate alternatives by simulating multiple scenarios, each within minutes. One CRO, for instance, generated “pessimistic,” “optimistic,” and “average” forecasts in response to a sponsor request for proposal (RFP) within 20 minutes. Then, when the sponsor asked the CRO to adjust its country plan, the CRO was able to deliver an updated, new forecast within 10 minutes. Similarly, these forecasting tools integrate ongoing study performance data to provide Mid-Study Optimization (MSO) and continuously reforecast the LPI timeline.

③ Charting Success Amidst Uncertainty: Quantify forecast certainty and confidence

AI models not only predict outcomes but also quantify levels of uncertainty with each forecast and each new variable using probabilistic methods, enabling better risk-based decisions. For example, the previous example uses Markov Chain Monte Carlo simulations to show confidence intervals and provides a visual representation of forecast variability, like weather forecasting models. It supports confidence intervals exceeding 80%, serving as a key validation metric.

No forecast or data model is 100% accurate, and methods described above characterize forecast uncertainty. In fact, it is equally important to capture the uncertainty of the forecast prediction as well as the prediction itself, since the forecast uncertainty also indicates the confidence study teams can have in a forecast. As better information—such as live study data—becomes available, the uncertainty attenuates. The absence of such reduction in the uncertainty could be a warning that enrollment performance is not systematically controlled in the study.

④ Charting Success Amidst Uncertainty: Bid defense and operational planning for CROs

CROs leverage AI-driven technology to create defensible forecasts that respond to sponsor RFPs and reduce dependence on fragmented internal systems. Start to finish, these can be generated in under 10 minutes—as evidenced by the previous CRO modified country plan example—accommodating ongoing study design modifications during the RFP process and faster adaptation to sponsor changes during bid cycles. Moreover, these “bid cycle” forecasts “seed” the approach to study execution, connecting study planning to study execution, rather than simply providing a model to close an outsourcing bid.

Feasibility Reimagined: No Longer Just a Box to Check

Historically, sponsors have expressed concerns that study feasibility processes are insufficiently responsive and not trustworthy enough to inform early decision-making. One sponsor characterized the traditional feasibility process as “piecemeal guesswork.” With a blend of advanced AI models, large volumes of historical trial information, and current study in situ data, sponsors and CROs can leverage scenario-based forecasts not just for planning purposes but also to pivot quickly when conditions change. For example, one sponsor leveraged an AI-powered tool to reforecast a study in five minutes due to an internal decision that loosened the eligibility criteria.

Most feasibility tools either provide basic benchmarking comparisons or require deep internal analytics, taking weeks to interpret. Further, these tools often treat study feasibility as a one-time exercise, separate from the realities of trial execution. This disconnect increases risk as assumptions made early on are not revisited or adjusted as the trial unfolds. When feasibility is aligned with execution, it provides ongoing visibility into how operational changes impact timelines and outcomes. Teams do not need to wait until operational issues arise; they can proactively identify emerging risks to reduce trial time, such as the previously mentioned site training issue, which the sponsor estimated saved the study six months as a result of increased participant enrollment from these previously noncompliant sites.

The role of feasibility in clinical development is undergoing a fundamental transformation. No longer a static, box-checking exercise, forecasting is becoming a strategic capability that empowers sponsors and CROs to make timely, defensible, data-informed decisions throughout the clinical trial lifecycle.

In a global environment distressed by volatility, escalating complexity, and heightened stakeholder expectations, continuous and adaptive forecasting is not simply beneficial: It is essential. AI-powered forecasting acts as a force multiplier, amplifying the impact of clinical operations teams by enabling real-time scenario modeling, risk mitigation, and accelerated decision-making. The future of clinical research will belong to those who do not merely collect data but who can also interpret it, simulate outcomes, and respond with precision.

References available upon request.