Timelines are tightening, complexity is increasing, and clinical teams are stretched thin. Yet despite a clear need, most AI efforts in drug development still fall short of delivering measurable operational value where it matters.

Agentic AI is changing that by embedding directly into study workflows, going beyond generic automation. Today's agents are reconciling site data across systems, triaging protocol deviations, drafting query responses, surfacing enrollment risks, and orchestrating study start-up activities in real time. Most crucially, they do not remove the need for experienced teams but can help them refocus their strategic attention to where it is most needed.

The result: less manual coordination, faster decisions, and fewer operational blind spots.

In this 60-minute session, panelists will share how these capabilities are being applied in real trials. They’ll examine specific use cases, performance metrics, and regulatory considerations—and the human factors involved in introducing agentic AI into established study execution.

Learning Objectives:

  • Review practical examples of agentic AI use in clinical trial operations.
  • Understand workflow changes that reduce burden and support timelines.
  • Examine metrics used to evaluate quality, efficiency, and readiness.
  • Learn best practices for compliant, human-centered AI adoption and change management lessons.

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Speakers:

Sashka Dimitrievska Global Head, Clinical Information & AI Strategy, R&D Oncology, AstraZeneca

Michael Rosenblatt Vice President and Managing Director, Syneos Health

Yazhene Krishnaraj Chief Information & Digital Officer, Sarah Cannon Research Institute · McKesson

Angie Maurer VP, AI-Enabled Clinical Development, Medable

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