For decades, researchers have tried to make clinical trials faster and more scalable by incorporating new technologies. Yet many platforms and portals overlook the day-to-day experience of site staff and operational teams. Adding more systems and processes can create additional friction that slows drug development. At a time when the pharmaceutical industry faces a patent cliff involving hundreds of billions of dollars in revenue,1 a more effective approach to clinical research is needed. We believe a potential transformation involves integrating embedded artificial intelligence (AI) agents into clinical trial workflows to improve study efficiency.2,3
Imagine a coordinator who never misses a protocol update, never forgets a patient re-consent, and never enters the same data twice. Now imagine they don’t work alone. They have an “AI teammate” that can help keep visit schedules on track, flag documentation gaps, and manage version control. Such applications are already being implemented at some research sites and sponsor organizations.4,5
These developments could represent an emerging shift in clinical trial operations. Sponsors that effectively integrate AI into their trial processes may be able to conduct studies more efficiently and reduce operational delays.
Complexity Is Outpacing Capacity
Clinical trials today demand more data, endpoints, documentation, and oversight. Although protocols have grown more complex, the infrastructure supporting them has not always evolved accordingly. Sites are expected to manage these increasing demands, often with existing tools and staffing levels. Coordinators may juggle multiple studies, navigate siloed portals, and spend substantial time completing regulatory and administrative tasks.
This administrative complexity has quietly become a dominant risk factor in trial execution, making high performance difficult to sustain. Coordinator burnout, missed enrollment targets, and protocol deviations can affect trial execution and, ultimately, patients. A more efficient approach would reduce redundant and burdensome administrative tasks, allowing clinical research staff to focus more attention on patient care and the development of new therapeutics. AI may offer one way to address these challenges.
Raising Pharma’s Artificial Intelligence Quotient
A company’s ability to apply AI across the drug development life cycle—its “artificial intelligence quotient,” or AIQ—may become one measure of its ability to use emerging technologies effectively.6 AIQ reflects the extent to which AI is integrated into the way a company designs, executes, and learns from its clinical trials.
Sponsors that stop treating AI as an experimental add-on and start viewing it as critical infrastructure are likely to have a significant competitive advantage in the coming years. Greater integration of AI could potentially contribute to faster enrollment, improved data quality, and greater protocol compliance while reducing administrative burden. Whether these efficiencies ultimately translate into shorter development timelines will depend on how effectively the technology is implemented and validated.
A Framework for AI in Drug Development
Not all areas of drug development are equally mature in their use of AI, but the technology has potential applications throughout the development process. Four areas illustrate its possible role:
1. Target Identification. AI use in target identification remains relatively early but is advancing. Large language models (LLMs) are being used to identify novel drug targets and predict mechanisms of action using omics data and real-world evidence. Although much of this work remains within pharmaceutical companies and academic laboratories, its effects could extend downstream into clinical development.
2. Protocol Design. AI is increasingly capable of modeling protocol feasibility against real-world patient populations, historical enrollment patterns, and site performance data. These analyses may help sponsors identify overly burdensome protocols, unrealistic eligibility criteria, or operationally challenging endpoints before a trial begins.
3. Trial Execution. This is where AI teammates can make an immediate and measurable impact. From informed consent tracking to document version control to visit scheduling, AI can reduce manual labor and ensure compliance without overwhelming staff. Coordinators no longer have to interpret 60-page protocols on the fly, because their AI teammate does that for them. Extending beyond site operations, AI is also being applied in critical areas like patient recruitment, engagement and retention, and image analysis, expanding its role across the clinical trial lifecycle.
4. Outcomes and Data Interpretation. Real-time data validation and signal detection can enable sponsors and research teams to identify potentially actionable information earlier. These tools may also improve data quality at the source. Poor-quality data can affect not only statistical analysis but also trial timelines, costs, and the interpretability of study results.
Efficiency in Drug Development
Sponsors, contract research organizations (CROs), and clinical sites increasingly recognize site enablement as an important component of trial execution. Execution quality can affect timelines, costs, data integrity, and the day-to-day experience of investigators and site staff. When protocols and workflows are simplified, physicians and coordinators may spend less time managing administrative tasks and more time focusing on patients. This may improve investigator engagement, sponsor–site relationships, and trial retention while reducing operational risk.
AI is not a solution to every operational challenge, but it may serve as an efficiency multiplier when applied appropriately. For example, AI can translate protocol complexity into specific workflow prompts, help sites remain aligned with protocol amendments, update trackers and documents, and identify potential deviations before they occur.
We have seen firsthand how AI teammates can remove the noise. In one trial, the team used AI to prescreen patients and help keep visit schedules aligned with eligibility criteria and protocol windows. The result was fewer protocol deviations, better evaluability, and a faster time to data lock. This should be a repeatable operational advantage.
Site experience is another important consideration. Research sites may consider their previous experiences with sponsors and CROs when deciding whether to participate in future studies. Frequent queries, late protocol amendments, and duplicative administrative work can increase the burden on site staff. AI that reduces redundant work and helps manage protocol updates could potentially improve the sponsor–site relationship.
Why Data Quality Matters
Enrollment alone does not determine the success of a clinical trial; enrolled participants must also generate evaluable data. Ten enrolled patients mean nothing if only 4 are usable. Problems can occur at any stage. Source documentation may not conform to protocol requirements, for example, or follow-up assessments may occur outside specified windows. Such issues can reduce the amount of usable data generated by a study.
These problems are not necessarily attributable to individual sites; they may reflect broader workflow and system limitations. AI could help address some of these issues by monitoring data in real time, identifying discrepancies, and flagging potential problems before they affect evaluability. Improving the proportion of enrolled participants who generate usable data could, in turn, affect both trial timelines and costs.
The Future of AI-Enabled Clinical Trials
We believe that the future of clinical trials belongs to those who can operate at scale without sacrificing precision. High-AIQ sponsors that embrace intelligent systems may earn the loyalty of sites, the trust of regulators, and the attention of investors. Those that continue to rely primarily on legacy workflows may find it increasingly difficult to keep pace with organizations that integrate AI more effectively into trial operations. RP
REFERENCES
1. DeAngelis A, Feuerstein A. JPM Conference 2025: will patent cliff spur big pharma deals? STAT. January 8, 2025. Accessed September 10, 2026. https://www.statnews.com/2025/01/08/jpm-2025-expiring-drug-patents-could-spur-pharma-mergers-acquisitions/
2. Chin-Yee D, Yalamanchili R. Reducing site burden and expanding research capacity with AI. Modern Retina. July 10, 2025. Accessed September 10, 2026. https://www.ophthalmologytimes.com/view/reducing-site-burden-and-expanding-research-capacity-with-ai
3. Barakat MR, Yalamanchili R. The clinical trial team gets an AI teammate. Retinal Physician. May 2025. Accessed September 10, 2026. https://www.retinalphysician.com/issues/2025/may/ai-in-clinical-trials/
4. Fox J, Yalamanchili R. Reimagining clinical trial operations with AI teammates at the site level. Ophthalmology360. June 2025. Accessed September 10, 2026. https://ophthalmology360.com/trending-topics/reimagining-clinical-trial-operations-with-ai-teammates-at-the-site-level/
5. Samman M, Yalamanchili R, Barakat MR. Bringing ophthalmic clinical trials into the 21st century: a short explainer. Expert Rev Ophthalmology. 2025; 20(4):163-166. doi:10.1080/17469899.2025.2503792.
6. Yalamanchili R. The AIQ imperative: a new competitive paradigm for biopharma. MedCity News. April 29, 2025. Accessed September 10, 2026. https://medcitynews.com/2025/04/the-aiq-imperative-a-new-competitive-paradigm-for-biopharma/