As clinical trial data grows more complex, organizations often leap toward AI before securing the foundational elements required for long-term success. This session focuses on building strong data governance, aligned analytics practices, clear domain ownership, and a culture that embraces data-driven decision-making. With these foundations in place, organizations become equipped to responsibly and effectively explore AI capabilities in their future roadmap.

ClinicalAIDEN is an agentic AI–powered decision support system that transforms how clinical trial feasibility and study design insights are generated, evaluated, and translated into strategic options. By orchestrating AI agents across data aggregation, analysis, and scenario evaluation, the system reduces manual effort, shortens insight cycles, and enables teams to explore multiple, evidence-backed development options earlier. This leads to faster, more robust, and better-aligned R&D decisions.

Role-based AI “Virtual Teams” can generate exploratory clinical analyses by coordinating analysis planning, statistical coding, and narrative summaries. Because generative models are non-deterministic, repeated executions can be treated as stochastic samples from a distribution of plausible analytical ideas. Specifically, we will describe how this AI workflow repeats its processes, compares results across multiple runs, and provides the end user with convergence and variability assessments to enable efficient human-in-the-loop validation and accountability.

This session presents a comprehensive framework for implementing managed, reusable data products in clinical development. Learn how to combine business purpose, semantic standards, technical delivery, and governance into AI-ready assets accessible through standardised interfaces. Discover practical approaches to data contracts, quality rules, lifecycle management, and regulatory compliance that enable consistent, automated decision-making across R&D.