From complexity to cohesion: AI’s growing role in building seamless clinical operations
The evolution of artificial intelligence in research and development marks a new era as generative AI, agentic systems, and other breakthroughs swiftly progress from concept to transformative practice. From protocol design to study start-up and through clinical trial closeout, AI is streamlining document management and workflows, extracting deeper insights from clinical content, and elevating data quality.
Below, we explore three pivotal areas where agentic AI is reshaping trial operations across the trial lifecycle: data review study setup; content generation, such as informed consent form (ICF) authoring in start‑up; and trial master file (TMF) management through closeout. Together, these capabilities point to a future when clinical trials are more efficient, consistent, and inspection-ready, without sacrificing the human decision-making and oversight that safeguard patient safety and scientific rigour.
1) AI in trial design: From data review to intelligent protocol strategy
Via well-designed AI tools and workflows, trial sponsors are aiming to unify cross-functional data ecosystems to enable a more integrated, proactive, predictive, and collaborative data review that elevates trial quality outcomes from the start.
Traditionally, medical reviewers, data managers, biostatisticians, and clinical scientists have worked in parallel, which can lead to duplicating queries, missing context necessary for deeper insights, and losing time to rework. An AI-driven centralised, real-time data hub can help these stakeholders see each other’s actions, track data lineage, and resolve issues collaboratively upon notice. This level of transparency and ability to act quickly reduces redundant queries and sharpens decision making to ensure clean, high-quality data informs trial design, instead of being corrected later in the process at a greater cost in time and resources.
Also, as individual trials now can generate a across electronic data capture systems, clinical laboratories, imaging, wearables, and patient-reported outcomes, manual review simply cannot keep pace. Embedded AI acts as a continuous efficiency layer, automating the creation of consistency checks and flagging anomalies, such as inconsistencies of patient data between vendors or potential protocol deviations.
One of the most time-intensive trial design tasks is configuring data review. This includes defining requirements, building validation programs, and testing outputs. AI agents can help study teams remove days, and eventually weeks, from the multi-week data review configuration process.
The impact is twofold: compressing the “white space”, or idle time between dependent tasks, while freeing clinical experts to concentrate on higher value analyses and scientific decision making. By incorporating “human-in-the-loop” safeguards, agentic AI becomes a supportive solution for experts who make the sensitive decisions and offer oversight and traceability.
Beyond automation, AI is helping study teams expand from efficiency to intelligence:
- Detect latent risks early. Invisible assistant systems surface patterns linked to protocol deviations or safety signals, enabling proactive adjustments before enrolment begins, which is a critical advantage as trial complexity grows.
- Standardise scientific judgments. AI models grade adverse events using accepted criteria (e.g., S. National Institutes of Health’s Common Terminology Criteria for Adverse Events), delivering consistent severity assessments that complement reviewer expertise and strengthen safety monitoring.
- Fine-tune protocol development. By converting protocols into machine-readable formats, AI can propose enhancements, such as tailored EDC designs, scenario simulations, and quantified patient burden. Sponsors can stress-test complexity, site feasibility, and operational risk early, reducing downstream amendments and improving enrolment strategies.
2) Accelerating study start-up: AI‑powered informed consent
Informed consent is foundational to ethical research and patient trust, yet, authoring informed consent forms is often a bottleneck in the study start‑up phase. Drafting global templates, aligning to countries’ regulations, and tailoring to site-level nuances can take weeks. As trial sponsors look to expand countries and sites for global trials, study start-up can become even more complicated and time intensive. By providing cleaner drafts of ICFs using AI, it is possible to ramp up site activation quicker, set more predictable first-patient‑in dates, and reduce downstream rework.
As noted previously, AI agents can assist in extracting content from study protocols and assembling drafts aligned with sponsor templates. These agents accelerate authoring by applying reflection layers for tone, consistency, and compliance checks, while an orchestrator agent manages workflow and handoffs. However, it is important to emphasise that regulatory compliance remains firmly guided by domain experts. At every stage, human oversight helps ensure drafts meet global and country-specific requirements.
At the country level, integrated authoring tools let teams iterate faster while helping to maintain country-specific and site-level alignment and ensuring content respects local requirements and cultural context. This helps to adapt content for human review, not to define or drive regulatory requirements. Built-in checks in these systems can surface missing required elements, inconsistent risk statements, or readability issues that prompt human edits. This repeatable and scalable approach helps trial sponsors manage the rising complexity of global trials, always under the direction of expert regulatory guidance.
3) Through trial closeout: Elevating trial master file activities via AI
If data review and ICFs set the trial up for success, the trial master file keeps it inspection‑ready. Ingesting, classifying, indexing, filing, reconciling, and verifying hundreds of thousands of trial documents is not simple. AI is transforming TMF operations from trial start-up to closeout with automation, intelligent quality control, and risk-based oversight.
AI-enabled document processing ingests large volumes of content, classifies relevant content, and indexes content to the correct TMF location accurately. Specialised agents perform quality checks for completeness, version control, duplicate detection, and Good Clinical Practice‑compliant eSignature validation to, again, reduce rework and accelerate cycle times. At scale, agentic workflows can process millions of documents annually. As they do so, they apply risk-based prioritisation to help re-focus study teams’ efforts on more important matters while preserving confidentiality and data privacy.
For audit preparation, intelligent agents can simulate end‑of‑line TMF reviews and apply rule sets that mirror human inspector expectations. Blind protection features can help prevent inadvertent unblinding, and data privacy protection agents will flag sensitive content. Orchestration layers maintain study context and historical reasoning traces, ensuring that each automated decision is explainable and auditable. This consistency reduces variability across global programs, harmonises standards, and bolsters continuous readiness for inspection.
As multi-agents process and review the TMF, human experts step in with nuanced guidance, rule changes, and feedback that improves future iterations. The payoff is fewer manual steps and better accuracy and compliance the first time around.
Bringing it all together: A multi-pronged, expert-focused change
The promise of agentic AI in clinical trials is not about replacing human experts. It is about augmenting their impact. Across data review, ICF authoring, and TMF management, agentic AI aims to reduce redundancy and improve consistency to ultimately ensure better data quality throughout, and increased efficiencies.
The human-in‑the-loop remains fundamental. Clinicians, statisticians, regulatory specialists, operational leaders, therapeutic experts, and others provide the scientific judgment, ethical stewardship, and nuanced context that AI cannot replicate, but is designed to support. When agentic AI is implemented thoughtfully, human guidance and feedback turns it into a catalyst to drive long-term operational advantage for drug development stakeholders interested in building trials that are efficient, ethical, and resilient from first draft to final inspection.
About the authors
John Gabra is director of innovation & AI, data sciences, safety and medical writing, at IQVIA. He brings more than two decades of clinical research experience to his current role, where his efforts are centred on strategic applications of AI to enhance business performance. Over the past 13 years, Gabra has contributed to the development of advanced technology solutions for IQVIA, including next-generation unified data and process platforms.
Yogesh Tambe is senior director of product management at IQVIA. He leads AI innovation in clinical content and trial master file operations. With more than two decades of experience in technology and life sciences, Tambe drives AI-enabled clinical and regulated content automation and agentic AI workflows for eTMF to improve inspection readiness and efficiency for global teams. He guides cross-functional groups, embedding risk-based design, and delivering compliant solutions that meet evolving industry demands.
Jon Walter is senior product manager of IT design & development at IQVIA. He is a senior product leader driving technology-enabled improvements in clinical trial operations. In 2025, Walter oversaw AI-driven automation for informed consent and quality control workflows. With more than 11 years in the CRO industry, he brings expertise in business operations, site relationship management, and operational efficiency.
