How AI Will Reshape Study Start-Up

By William Bryant III, Clinical Strategies by WB3

When it comes to using AI in clinical research, much of the attention is focused on drug discovery, protocol design, patient recruitment, and data analysis. Study start-up receives far less attention. That is a mistake, as country strategy, site selection, regulatory planning, contracting, and budgeting all begin here. Weak decisions at this stage can affect enrollment and overall trial execution, creating lasting downstream pressure on clinical supply, study budgets, and activation timelines.

Teams can improve these activities by using AI to help them organize complex operational information and recognize risks earlier. The true value of AI, then, is not automation but decision support. It cannot fix poor processes, weak data, or disconnected systems and it certainly should not replace professional accountability. In this context, the role of AI is to challenge assumptions and highlight patterns that might be difficult to see in traditional trackers. The future of study start-up depends on how well organizations blend AI's analytical power with the experienced judgment of research professionals.

Strategic Planning And Site Selection

Start-up teams work with vast amounts of information — protocols, regulatory requirements, site capabilities, and historical performance data. This information often sits in silos, making it difficult to decide what the data actually means for a trial's timeline.¹ AI can help bridge these gaps by summarizing country considerations, identifying missing documentation earlier, supporting feasibility reviews, and creating questionnaires that help teams select sites based on the operational needs of a protocol and historical site performance.¹ This evidence-based approach is critical for navigating complex global submission pathways, where requirements can differ significantly across jurisdictions.⁵

Traditional site selection often relies on investigator interest and epidemiology, but these do not always tell the full story. A site may report strong interest while lacking the staff capacity to manage a complex visit schedule. A country may have a high patient population but difficult regulatory or contracting timelines. Teams looking to review sites through a broader lens can use AI to help connect these disparate factors. Machine learning models can even combine prior recruitment data with real-world insights to predict site performance more accurately.² This deeper level of analysis ensures that the study team can consider the infrastructure needed for a protocol before making a final commitment.⁴

Moving From Tracking To Prediction

Most start-up teams are experts at tracking what has already happened — knowing which document is late or which contract is stalled. The harder task is to determine what is likely to happen next. This is where AI changes the conversation by identifying patterns that traditional trackers might miss.⁴ A delayed document in one country might seem isolated, but when viewed alongside slow contract negotiations in another, it could signal a broader threat to activation. By using AI to identify these warning signs earlier, the team can move from a reactive posture to a proactive one.¹

Study teams can also use AI to organize regulatory status, document readiness, and site responsiveness into a predictive framework. This allows teams to intervene before problems escalate to the critical path.¹ The goal is earlier recognition that gives teams the opportunity to pivot or mitigate risks before an activation milestone is officially missed. Transitioning from reactive tracking to proactive prediction is one of the most powerful shifts AI can enable in clinical operations.⁴

Streamlining Operational Execution

Start-up professionals are uniquely positioned to recognize operational challenges before they reach the site. With AI at their disposal, teams are encouraged to ask critical operational questions before protocol choices become expensive to change. AI can support this by flagging burdensome visit schedules or eligibility criteria that might reduce the site pool,³ organizing changing regulatory requirements and submission pathways across multiple jurisdictions, and even identifying document dependencies that could cause sequencing delays.⁵ This is especially useful when teams are managing several countries simultaneously and need to prioritize activities that impact the overall timeline.

Informed consent development is another practical use case. AI can compare consent language against the protocol to identify inconsistencies or language that participants might find difficult to understand.⁶ For site budgets, AI assists with Medicare Coverage Analysis by organizing routine cost information against CMS policy, ensuring that research-related expenses are accurately identified.⁷ In document review, AI provides a first-pass check for version consistency and missing sections across submission packages. While these tools improve efficiency, strict controls are required to ensure confidential information is protected and all legal interpretations are verified by reimbursement and compliance professionals.¹⁰

AI can also support activation forecasting by identifying historical stalling points in the contracting and regulatory process.¹ By analyzing where previous trials faced delays, the system can flag potential bottlenecks in current studies. This allows the team to allocate resources more effectively, focusing their attention on the most complex regulatory and ethics requirements. The human reviewer remains the final authority, but AI empowers them to find potential issues faster, ensuring that every submission is as robust as possible.⁸

Real-World Experience With Two AI Agents

While supporting a major pharmaceutical company, I developed Diane, a study start-up AI assistant within Microsoft Copilot Enterprise. With my guidance, Diane organized complex operational data and assessed risks inside a secure enterprise environment. I used the agent to examine start-up challenges through several lenses, including site readiness, regulatory status, and timeline dependencies. For example, I could use Diane to evaluate site performance across therapeutic areas to identify infrastructure gaps. The tool helped structure analysis and identified missing information, improving the speed and accuracy of our internal planning without automating final decisions.

Through my consultancy, I use a closed AI agent called AskJackie to support the assessment of clinical operations challenges. The agent helps me test assumptions and identify the underlying causes of activation delays, whether they stem from contracting bottlenecks, site responsiveness, or unidentified dependencies. By evaluating multiple scenarios, such as comparing the impact of different resource allocation strategies, the tool helps me develop more robust recommendations for my clients. Both agents are positioned as decision support tools, ensuring that accountability and final operational decisions remain with the professional.

These examples illustrate the two primary ways AI is being used in start-up: as an enterprise wide assistant for large organizations and as a specialized consulting agent for targeted operational strategy. In both cases, the value comes from the intersection of AI-driven analysis and professional context. The AI provides the structure, while the professional provides the interpretation and final recommendation. This distinction between support and accountability is central to the successful integration of AI into clinical research workflows.

Governance, Data Quality, And Risk Management

Clinical research professionals must treat AI outputs as starting points, checking key facts against reliable sources and documenting all major assumptions. Governance is essential and must be established before AI tools are deployed across an organization.

Additionally, FDA's 2025 draft guidance emphasizes a risk-based credibility framework for AI models supporting regulatory decision-making, while ICH E6(R3) focuses on quality by design and proportionate risk management.^8,5 Traceability matters; audit trails should capture the source data and human review when AI contributes to a meaningful study decision.⁵ NIST's AI Risk Management Framework further underscores the importance of transparency and trustworthiness in AI development and use.¹⁰ Data quality is the foundation of these efforts, as AI cannot correct a weak source simply by processing it faster. A commitment to data integrity and validation is required to ensure that AI-supported insights are reliable.⁹

The Evolving Role And Recommendations

AI will not eliminate start-up professionals, but it will change how they work by shifting their focus from repetitive administration to strategy, risk management, and professional judgment. The most valuable professionals will be those who know how to ask better questions and assess the quality of information. This evolution requires new skills in data interpretation, process design, and governance. As routine tasks become more automated, human judgment and cross-functional coordination become more essential than ever, shifting the role toward one of strategic oversight.

Sponsors and CROs should begin with controlled use cases, identifying high-value activities such as feasibility questionnaire development and activation forecasting. Each use case should have a defined owner and a clear validation process to ensure the outputs are safe and accurate. Organizations must also measure the impact of AI on speed, quality, and efficiency. A faster process that results in rework or poor decisions is not an improvement. The goal is an AI-supported function where professionals use tools to gather insights and challenge assumptions while remaining fully accountable for the trial's success.

AI-Supported, Not AI-Controlled

The future of study start-up is one of collaboration between AI and the human professional. AI can help study start-up teams move faster and think more clearly, provided human judgment remains at the center of the process. Organizations that treat AI as a substitute for experience may lose the judgment required to manage complex clinical trials. However, those that apply AI to bridge information gaps and identify risks early will be better positioned to accelerate clinical development. Ultimately, AI's value will depend on the professionals who use it, the processes that guide it, and the governance that keeps accountability at the center.

References:

1. Bryant W III, Martin A, Brathwaite JS. Improving Study Start-Up Efficiency to Accelerate the Clinical Trial Timeline. ACRP Clinical Researcher. February 17, 2026. https://acrpnet.org/2026/02/17/improving-study-start-up-efficiency-to-accelerate-the-clinical-trial-timeline
2. Hulstaert L, Twick I, Sarsour K, Verstraete H. Enhancing site selection strategies in clinical trial recruitment using real-world data modeling. PLoS One. 2024;19(3):e0300109. doi:10.1371/journal.pone.0300109.
3. Lu X, Yang C, Liang L, Hu G, Zhong Z, Jiang Z. Artificial intelligence for optimizing recruitment and retention in clinical trials: a scoping review. J Am Med Inform Assoc. 2024;31(11):2749-2759. doi:10.1093/jamia/ocae243.
4. Harrer S, Shah P, Antony B, Hu J. Artificial Intelligence for Clinical Trial Design. Trends Pharmacol Sci. 2019;40(8):577-591. doi:10.1016/j.tips.2019.05.005.
5. International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. ICH E6(R3) Guideline for Good Clinical Practice. Step 4. January 6, 2025.
6. U.S. Food and Drug Administration. Informed Consent Guidance for IRBs, Clinical Investigators, and Sponsors. August 2023; and ICH E6(R3) Good Clinical Practice.
7. Centers for Medicare & Medicaid Services. Medicare Clinical Trial Policies; National Coverage Determination 310.1, Routine Costs in Clinical Trials.
8. U.S. Food and Drug Administration. Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. Draft Guidance. January 2025.
9. European Medicines Agency and U.S. Food and Drug Administration. Guiding Principles of Good Artificial Intelligence Practice in Drug Development. January 2026.
10. National Institute of Standards and Technology. Artificial Intelligence Risk Management Framework (AI RMF 1.0). January 2023; Generative Artificial Intelligence Profile (NIST AI 600-1). July 2024.

About The Author:

William Bryant III has more than 20 years of experience in clinical research. His work has focused on study start-up, clinical operations, and global trial execution. He is the founder of Clinical Strategies by WB3, where he helps organizations improve study start-up strategy and operational performance. He also explores practical ways to use AI in clinical research while keeping human judgment and accountability at the center of decision-making.