Countries compete for commercial clinical trials but most operate through fragmented infrastructures that create unpredictable timelines. The UK has implemented a coordinated national delivery model—the NIHR Industry Hub—aligning regulators, health systems and research infrastructure around a single pathway. This session explains how a whole-system approach enabled consistent 150-day study set-up at scale, and what other health systems can replicate to improve reliability, sponsor confidence and patient access.

Academic CRO and ARO models are increasingly important in the delivery of trials with registrational potential, especially where rapid feasibility, site engagement, and complex operational support are required. This presentation will examine how networked trial delivery platforms can improve site selection and activation by combining centralized coordination, embedded acceleration networks, and direct access to clinical expertise. It will also discuss why commercially competitive not-for-profit models are gaining traction in the US and Europe, and how reinvesting surplus into research infrastructure can strengthen long-term trial capacity. Attendees will gain practical insight into how these models support faster trial setup, more effective feasibility assessment, and more sustainable delivery of registration-enabling studies.

I'll share how AI and ML can enhance clinical trial site selection through a transparent, data-driven framework that evolves with every stage of the process. Topics covered include: Multi-source data integration: AI harmonises internal, external, commercial, clinical, real-world, and ad hoc data into a unified analytical catalogue; Interpretable modelling: Glass-box models, not black boxes, transparently rank sites, enabling stakeholders to understand and trust the outputs; Iterative refinement: Feasibility outreach data is fed back into the model, dynamically updating site rankings; and Actionable insights: Bridging predictive analytics with ground-truth site intelligence for better-informed decisions.

Site activation remains a critical bottleneck in clinical development, delaying patient access and increasing pressure on biotech organisations. In this session, attendees will gain a fresh perspective on how a biotech is redefining feasibility and site selection to dramatically accelerate activation timelines and improve trial performance. Discover how smarter upstream decisions can unlock faster execution, improve efficiency, and give clinical operations a true competitive edge in today’s dynamic environment.

We explore the innovative platform Pfizer developed to streamline feasibility and site selection decisions, using a data-led, site-centered approach. The platform consolidates and transforms the data into actionable strategies, allowing to anchor decision-making at the site level while scaling an AI-driven strategy that still values expert judgment. With this approach, Pfizer aims to bring efficiency into the decision, without losing the valuable input from our ultimate partners: the sites.

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-centered 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.