“For almost a decade, it’s taken nearly eight months on average to get from site identification to study startup completion, when all sites are initiated and ready to enroll patients.”

While it’s often hard to quantify the average time spent on clinical trial activities like study startup, this 2018 quote from Tufts Research author Mary Jo Lamberti showcases the very real problem regarding the time it takes to reach key trial milestones.

In fact, in the seven years since that quote, the clinical trial landscape has only become more complex, with clinical trial cycle times increasing despite technology advances. Cycle times are defined as the total duration from the approval of a clinical trial protocol to the database lock (DBL). As reported by Statista, the average clinical trial cycle from 2020 to 2024 increased by seven months.

Additionally, IQVIA recently released research indicating that almost 50% of drug development time is attributed to non-scientific delays, aka operational bottlenecks that create unnecessary gaps between critical milestones.

Where trials commonly stall

Between protocol finalization and site activation, delays frequently arise from the slow pace of regulatory, ethics committee, and site committee approvals. Even when approvals are underway, incomplete or unclear site feasibility feedback can create confusion, while misalignment between operational readiness and document readiness further stalls progress. Last-minute protocol amendments often compound these challenges, triggering eClinical system rebuilds that reset timelines. In parallel, delays in site training and IRB approvals frequently leave teams waiting before they can proceed to the next stage.

During the period between first activation and last patient last visit (LPLV), trials encounter a different set of inefficiencies. Site activation typically ramps up unevenly as each country and site comes online at a different pace, leaving large gaps of inactivity. Patient enrollment introduces additional white space as sites struggle to identify and bring in participants, with recruitment often stalling for weeks at a time. Protocol amendments add to the disruption, forcing pauses, retraining, and restarts. Critical decision points such as IDMC reviews or cohort transitions are also delayed by the lack of cohesive data across systems, slowing down pivotal moments in trial execution.

Finally, between the last patient visit and database lock, trials face one of the most stubborn bottlenecks: data cleaning. Queries accumulate and require painstaking resolution long after the last patient visit. Serious adverse event (SAE) reconciliation and endpoint adjudication add further delays, while sponsors are often forced to wait for external data sources such as central lab results or imaging reads. Together, these factors extend the final stretch of trial execution, turning what should be a straightforward closeout process into months of unnecessary waiting.

Solving the waiting game

Thankfully, a new technology has been helping sponsors and CROs dramatically improve the amount of time it takes to complete key trial activities: agentic AI.

Agentic AI offers a compelling path forward by directly addressing the operational characteristics of clinical development work. Many tasks within a trial are repeatable, rules-driven, and data-intensive, which is ideal for automation and also what makes the tasks all the more challenging to perform at scale with human resources. 

AI agents are built to handle exactly this blend of complexity and repetition. They can coordinate information from multiple systems, apply relevant rules or guidelines, and execute decisions or trigger workflows with or without requiring human initiation.

Real-world examples of time savings

The use of agentic AI in site activation highlights how this technology can solve one of the biggest bottlenecks in clinical development.

Traditionally, site activation is a long, step-by-step process. It includes finding and qualifying sites, negotiating contracts, submitting regulatory documents, delivering training, and setting up technology. Each of these steps involves multiple stakeholders, and the handoffs between teams often cause delays and miscommunication.

Agentic AI can streamline this process by running activities in parallel instead of one after another. For example, it can prepare site packages while contracts are still being negotiated, automatically schedule training based on real-time availability, and flag issues that could slow down activation before they become problems.

In recent years, CRAs have been overwhelmed by the need to manage data across multiple eClinical systems. Instead of focusing on guiding and supporting sites, much of their time is spent chasing missing data and trying to maintain oversight.

AI Agents change this by providing continuous, real-time oversight instead of relying on scheduled monitoring visits or periodic reviews. This enables proactive intervention before small issues become major problems, allowing CRAs to return to their core role of supporting sites.

These systems also learn from past site activation experiences. By recognizing patterns, such as which sites are likely to face delays and what interventions work best, they can tailor strategies for different site types, regions, therapeutic areas, and protocol complexities. Performance improves over time as more data is captured, delivering benefits that scale beyond single trials to entire development portfolios.

The monitoring and data management capabilities of agentic AI may offer the fastest wins. Today, human reviewers must comb through individual patient records across multiple systems, identify issues, and work with sites to resolve them. This manual process often delays database lock and slows trial completion. AI can automate much of this work, helping to ensure data quality while significantly reducing bottlenecks.

Finally, the integration capabilities of agentic AI systems address one of the fundamental structural barriers to reducing whitespace; decision making. These systems can operate across traditional organizational boundaries, accessing data from multiple sources, coordinating activities between different functional groups, and providing comprehensive visibility into trial performance that enables more effective decision making. Rather than requiring human intermediaries to translate information between different systems and stakeholders, agentic systems can serve as intelligent integrators that ensure information flows seamlessly throughout the trial ecosystem. An agent created towards eliminating whitespace during study startup can quickly synthesize regulatory requirements, therapeutic area precedent, and sponsor objectives to draft protocol designs and trial documents in record time. 

Across all these examples, agents replace idle time with active progress, ensuring that the moments between trial milestones are used to advance the program rather than to wait for the next task to begin. 

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Conclusion: Transform waiting into working

Clinical development has long been constrained by inefficiencies that stretch timelines and stall innovation. But agentic AI offers a clear inflection point: the ability to transform waiting into working. 

Solutions like Medable’s CRA Agent, offer AI-driven solutions that automate and optimize clinical trial monitoring by proactively identifying and prioritizing site risks, generating comprehensive pre-visit summaries, and providing actionable recommendations to enhance trial oversight and compliance.

By continuously monitoring, automating repeatable tasks, and orchestrating processes across silos, these systems can compress cycle times that have stubbornly resisted change for decades.

To learn more about this topic, click here to read our latest whitepaper on agentic AI and clinical trial white space. To see a demo of Medable’s Agent Studio, click here.

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