Scaling AI Implementation Across Clinical Research

Artificial intelligence is transforming clinical research operations, yet many organizations struggle to move beyond isolated AI pilots to achieve enterprise-wide implementation that delivers sustained value across multiple studies and therapeutic areas. The challenge lies not in proving AI's potential, but in creating scalable infrastructure, governance frameworks, and organizational capabilities that enable consistent AI deployment across diverse clinical research environments.

Successful scaling of AI in clinical research requires addressing complex challenges around data standardization, vendor management, regulatory validation, and change management while maintaining the flexibility to adapt AI solutions for different study designs and therapeutic contexts. Leading organizations are developing systematic approaches to AI implementation that balance standardization with customization, enabling them to realize efficiency gains and quality improvements across their entire clinical research portfolio.

Join us to discuss:

• What implementation frameworks and organizational structures are proving most effective for scaling AI solutions across multiple clinical trials and research programs?
• How are leading clinical research organizations addressing data quality, vendor selection, and regulatory validation challenges when deploying AI at scale?
• What change management strategies and capability-building approaches are enabling research teams to successfully adopt and optimize AI tools across diverse study environments?