Clinical Researcher—June 2026 (Volume 40, Issue 3)
DATA BY DESIGN
Christy Christian
Delays in clinical trials affect much more than timelines. These delays directly influence patient access, overall costs, and ultimate trust in research pipelines. As time goes on, clinical trials are becoming increasingly more congested as protocol complexity, shortages, and operational delays become more significant. External factors such as global disruptions and demand volatility are only amplifying these bottlenecks.
Artificial intelligence (AI) is emerging as a tool to improve the flow and coordination of these trials. AI-enabled planning and prediction methods can reduce friction across the lifecycle of a clinical trial by identifying trends and patterns earlier, so proactive measures can be taken to mitigate the impact.
The Hidden Bottleneck: Operational and Supply Chain Friction in Trials
Clinical research heavily depends on interconnected supply chains that include investigational products, comparators, and ancillary materials. This reliance causes any forecasting inaccuracy to lead to shortages, overproduction, and even wasted materials. Manual and siloed planning processes limit an organization’s ability to anticipate impacts; this is where AI can support human expertise.
The global instability seen from tariffs and transportation constraints—on top of an already fragile global supply chain—has made these traditional planning methods less reliable. Along with other challenges, these factors are posing an extreme challenge to consistency across many industries. Operational traffic jams tend to surface when it’s too late, particularly when timelines are the most difficult and expensive to fix.
How Does AI Improve Workflow?
AI is best suited to identify patterns across large, dynamic datasets that would otherwise pose a long and tedious task for humans. When left to traditional methods, these large datasets are most prone to error and oversight, solely because of their size. Using AI to support this planning and forecasting helps organizations move away from reactive firefighting to making proactive decisions. Modern clinical trial analytics, including AI, have been shown to improve productivity by 15% to 30%.
Improving visibility will support coordination. This enables improved alignment across clinical operations, manufacturing, and logistics teams earlier on. These are a few methods that AI can support, and what it’s realistically able to improve:
- Predictive analytics can improve demand forecasting for trial materials. Its purpose is to reduce shortages and excess inventory. The more strategic tools are used to support predictive analytics, the greater the chance of operating on the most accurate prediction possible.
- Scenario modeling allows teams to test “what-if” conditions. There are a variety of different scenarios that could impact results in clinical trial workflows, and the more thoroughly prepared a team is to handle them, the better. A few examples of what scenario modelling can help test include enrollment changes, site delays, and regulatory shifts.
Implications for Clinical Research Professionals
More accurate forecasting will reduce site disruptions that will otherwise directly affect coordinators and participants. Around 72% of physicians in the United States say that some of the most relevant trials are simply taking too long. Increasing the reliability of trial supply will help to support more consistent enrollment and retention in the trial.
AI-driven insights can also inform protocol feasibility and site selection in trial planning. Using AI to support decision-making in clinical trials will help organize more cost-effective trials. Its ability to provide insights that will reduce waste and the need for reworking will improve efficiency. That said, human expertise remains necessary and central. AI is most effective when used to aid professional judgment, not replace it.
Clearing the Road for Faster, More Resilient Trials
As clinical trials become more complex, operational intelligence will become just as critical as scientific innovation. Organizations that invest in more innovative planning capabilities will ultimately be better positioned to manage disruption. There is no “one-size” implementation strategy to solve all bottlenecks, but patients will benefit from these changes by gaining access to therapies and more reliable trials. The reduction of traffic jams in clinical research is not just an efficient and strategic gain, but an ethical one.
Christy Christian, Senior Industry Principal for Life Sciences at Kinaxis, is a global supply chain leader who applies standard processes to complex situations to drive proactive discussions, aligned decisions, accountability, and ownership in challenging environments. She is known as a collaborative leader experienced in building high-performing global teams and aligning processes with technology to enable agility, sustainability, and predictability across manufacturing, services, and software sectors.
