AI Implementation Challenges With Legacy EDC Systems: Is Your EDC Limiting The Impact Of AI On Your Data?

AI promises major gains in clinical data management, yet many organizations still struggle to realize its value due to the limitations of legacy EDC systems. These older platforms were never designed to support machine learning workflows, automation, or the rich data pipelines now essential in modern trials. Their rigid architectures slow down integration, make data standardization difficult, and often require heavy manual oversight—undercutting the efficiencies AI is meant to deliver.

This article explores why many AI initiatives stall when built on outdated infrastructure and highlights the operational gaps that prevent teams from unlocking real productivity improvements. It also examines how clinical teams can better position themselves for AI readiness, from rethinking data governance to adopting more flexible, interoperable systems.

Readers will gain a clearer understanding of the root challenges behind AI adoption in clinical research and what it takes to move from experimentation to measurable impact. Access the full article to learn how organizations can overcome these barriers and build a future‑ready data environment.