Sustainable AI adoption in clinical feasibility is not a technology problem—it is a sequencing problem. At AstraZeneca, we built the foundation first: reframing feasibility as a strategic capability, standardising our data, and embedding robust process across our oncology portfolio. This session shares how that bedrock is now enabling a genuine AI-augmented feasibility ecosystem—and what we learned along the way.

Quantitative assessments of patient and site burden provide only a partial view. Incorporating qualitative insights from patients, caregivers, and sites reveals the true drivers of burden. This presentation explores how qualitative feedback has been integrated into burden reporting, enabling deeper, more meaningful evaluation of protocol design and supporting more informed, patient, and site-centred decisions.

This session explores a new AI-enabled feasibility model that shifts accountability from sponsors and CROs to sites themselves. Drawing on a digital network covering one-third of UK research-active practices, attendees will learn how AI validates recruitment plans, capacity, demographics, performance history, and operational readiness before human review begins. By requiring sites to prove feasibility upfront, this approach reduces start-up timelines, limits inflated capability claims, and creates a faster, more transparent site selection process.