AI in Action: Breaking Down Clinical Trial Bottlenecks

Clinical trials advance our understanding of human biology, establish the safety and effectiveness of new drugs and devices prior to broad use, fill therapy gaps for rare and chronic conditions and drive medical progress. They are the backbone of medical innovation, having more impact on human health than most people realize.

In the United States alone, one in 10 people have a rare disease, yet only 5% of the 7,000 rare diseases currently have treatment options. Unsurprisingly, clinical trials are incredibly complex to design and execute. The 10- to 15-year drug development process can cost up to $2 billion, with the clinical trial stage accounting for nearly 70% of overall research and development costs.

Certain steps within the trial lifecycle, such as recruitment, retention and safety monitoring, are particularly prone to costly bottlenecks that can delay or derail trials, ultimately jeopardizing patient experiences.

Artificial intelligence (AI) offers a new path forward for clinical trial sponsors, contract research organizations (CROs), and research sites collaborating on trials. By replacing reactive protocols with powerful insights and preventative interventions, AI helps identify and address potential problems before they occur.

The outcome is more patient-centric, safer, and efficient clinical trials that improve experiences for every participant, research site, and sponsor involved. When combined with agentic AI workflows, the process becomes even more efficient by autonomously coordinating next steps across these groups.

Where the clinical trial lifecycle falls short

Identifying, recruiting, and retaining the required patient population for the trial and collecting, reviewing and analyzing safety data to ensure participants’ well-being are two significant bottlenecks. Any issue or delay within these functions can put patient safety and research integrity at risk, which can escalate into financial and operational repercussions.

For example, both recruitment and retention are notoriously complex to manage due to participant barriers and overly restrictive protocol criteria. Challenges with these interconnected functions is one of the biggest reasons that trials miss enrollment deadlines, with delays costing sponsors up to $8 million per day in lost revenue in some cases.

Safety monitoring gaps can also compromise clinical trials. One study found that adverse event (AE) reporting procedures varied across sites and individuals, creating room for inaccuracies and inconsistencies.

For example, manual AE reporting processes rely on participants to recognize, recall, and report their symptoms, which can lead to under- and over-reporting. These bottlenecks reach beyond simple frustrations.

They slow the path from discovery to delivery, delaying access to essential medical interventions for patients who need it most.

AI in action

Clinical trials create massive amounts of both structured and unstructured data throughout the lifecycle. Researchers must then collect and analyze diverse data types to extract meaningful insights, a time- and resource-intensive process thanks to the volume, variety, velocity, and quality of data from multiple sources.

Fortunately, AI thrives in this environment.

AI training models need vast amounts of data to mine and discover anomalies, trends, nuances and unmet needs which researchers then validate. This human-in-the-loop approach, a combination of robust data assets and deep expertise, allows sponsors, CROs and research sites to move faster and smarter throughout trials.

Agentic AI adds to this by automatically routing insights into actionable workflows, reducing lag time between data interpretation and execution. Real-world recruitment and safety applications include:

• Intelligent protocol and feasibility assessments: AI can analyze data from various studies and sites to better predict whether the trial design is realistic, accounting for insights into patient populations, such as geographic location and comorbidity exclusions, and help design protocols based on proven behaviors.
• Dropout prevention via behavioral science integration: Advanced AI applications can analyze patient inputs, such as compliance rates and engagement levels, which help indicate whether a patient is at risk of dropping out. These insights help sponsors, CROs and research sites tailor a more personalized trial experience for at-risk patients.
• Connected devices that trigger real-time interventions: AI can monitor patient vitals and symptoms in real-time through connected wearable devices, identifying when safety thresholds are crossed so immediate action can be taken rather than waiting for patients to report symptoms. Analyzing vast amounts of safety data also helps researchers better understand patterns and trends that might otherwise be overlooked.
• Automated, personalized communications: Participants receive various forms and questionnaires throughout trials, typically for safety follow-ups and clinical outcome assessments. AI can marry multiple data sources to understand and account for the nuances of each participant’s profile, such as comorbidities associated with their condition, and automate communications based on predetermined triggers and algorithms.

These AI use-cases are among a rapidly growing number of intelligent solutions that enable faster, higher-quality processes across the clinical trial lifecycle, as well as a better understanding of participants and their needs.

Ultimately, the integration of AI and digital innovations is yielding measurable efficiencies in clinical development. The IQVIA Institute’s 2025 Global R&D Trends report highlights meaningful improvements in trial cycle times and productivity as companies deploy these new tools.

For example, after years of lengthening timelines, median enrollment durations stabilized in 2024 and inter-trial delays (the time between trials in a development program) dropped sharply, from a peak of 32 months in 2022 down to 17 months.

Vision for the future

By accelerating the adoption of AI and agentic AI workflows, clinical life sciences organizations not only gain operational efficiency and a competitive edge, but they also advance the shared mission of delivering life-saving treatments to patients more quickly, safely, and effectively. As AI evolves from operational to strategic support, its greatest potential lies in enabling smarter, more inclusive and more empathetic human-in-the-loop research.

The future of clinical trials will be defined by studies that reflect real-world patient experiences, adapt dynamically to live data and personalize engagement to meet individual needs. This means trials won’t just be more efficient, they’ll be more humane.

The convergence of AI, decentralized technologies, behavioral science, and real-world evidence opens the door to a new era in which the industry proactively addresses participation barriers, integrates social determinants of health, and reimagines patient centricity. In this future, AI doesn’t replace human insight; it amplifies it, enabling researchers, clinicians, and patients to co-create a more responsive, equitable, and impactful clinical ecosystem.

About the Author

Chris is a Senior Director in clinical technology, currently leading the Patient Suite portfolio at IQVIA. With more than 20 years of experience spanning product management, R&D, and Information Technology, he excels at bridging the needs of patients, sites, and sponsors to deliver scalable, patient-centric solutions. Throughout his career, Chris has driven product innovation across global health platforms, particularly in IRT/RTSM systems and end-to-end patient journey technologies. His strategic vision is grounded in empathy for clinical stakeholders and a commitment to technical excellence. He believes in a future where care is tailored to each individual, enabled by accessible, secure, and intelligent systems. Outside of his professional life, Chris enjoys spending time with his growing family, while championing for adoption and for children blessed with Down syndrome.