Seminar 2026

AI ambitions are often limited by fragmented, study-level data that isn’t designed for reuse or scale. Siloed systems, inconsistent metadata, and limited ability to generate cross-study insights are all evidence of these data shortcomings. Leaders will share their perspective on: What it takes to unify EDC, CTMS, imaging, biomarker, and operational data into a cohesive, AI-ready layer Which architectural and interoperability standards are emerging to support scalable analytics Where legacy systems are creating friction and how to modernize without disrupting active trials How organizations are enabling real-time insights and cross-study learning at enterprise scale