Driving Faster Clinical Trial Setup: How Humans and AI Collaborate to Improve Quality, Speed, and Agility in Study Build

Study build inefficiency is rarely caused by a single failure point. Instead, delays accumulate across fragmented protocol interpretation, manual configuration, iterative amendments, and validation cycles that often surface issues late—when they are more costly to resolve. For many sponsors and CROs, this has become the accepted baseline. The more important question is whether it needs to be.

This webinar explores how a more integrated human–AI collaboration model can address these challenges. Drawing on practical experience from study build environments, we examine how AI can be applied not just to automate individual tasks, but to support the full lifecycle—from protocol interpretation through configuration and validation. In this model, AI contributes context-aware support grounded in clinical data structures and study design logic, while clinical and operational experts guide decisions, validate outputs, and ensure regulatory alignment.

We will share how this approach is being implemented in practice, including where AI can help reduce rework, improve consistency in configuration, and surface potential downstream issues earlier in the process. Particular focus will be given to how teams are balancing increased speed with the need for control, auditability, and quality.

Through real-world examples and live demonstration segments, attendees will gain a practical understanding of how human expertise and AI can work together to improve study build performance—enabling faster timelines while maintaining rigor.

Why attend:
– Gain a realistic, experience-based perspective on AI in clinical operations
– See how these approaches are being applied within active study environments
– Understand how to improve speed without compromising quality or oversight