Study startup remains the most expensive and least improved phase of the clinical trial lifecycle. According to Tufts CSDD, site contract negotiations average 230 days, new sites take 36 weeks from first contact to first patient enrolled, and Phase III trials cost $55,716 for every day of delay. For small-to-mid biotech sponsors running two to five concurrent trials, these delays don’t just erode budgets – they threaten the survival of the program.

Yet the industry has entered 2026 at an inflection point. Agentic AI – autonomous AI agents that can reason, act, and adapt across clinical workflows – is now being deployed against study startup bottlenecks with measurable results. From automated database builds to intelligent site activation sequencing, these AI agents are compressing timelines that have remained stubbornly static for over a decade.

In this webinar, we unpack the anatomy of study startup delays, quantify the true cost of inaction using industry benchmarking data, and demonstrate how agentic AI is being applied to compress the study build-to-site activation timeline. We will walk through real-world application scenarios showing how AI agents automate protocol-to-database translation, accelerate site feasibility and selection, streamline regulatory document preparation, and enable parallel rather than sequential activation workflows.

Attendees will leave with a clear understanding of where their study startup process is leaking time and money, which AI-addressable bottlenecks offer the fastest payback, and a practical framework for evaluating agentic AI solutions against their operational reality.

This session is designed for PI’s, site leaders, clinical operations leaders, biotech executives, and CRO decision-makers who are past the “what is AI” stage and are looking for evidence-based, implementation-ready approaches to their most persistent operational challenge.

What Attendees Will Learn

• The true cost anatomy of study startup delays – benchmarked data on where time and money are lost across the study build-to-first-patient-in timeline
• How agentic AI differs from traditional automation in clinical trial workflows – and why it matters for study startup specifically
• Real-world application scenarios showing AI agents compressing database build, site activation, and regulatory document preparation timelines
• A practical evaluation framework for assessing AI readiness within their own study startup operations
• The competitive landscape: what leading organizations are doing now and the cost of waiting

Register now to learn how leading clinical teams are applying Agentic AI to reduce delays, improve efficiency, and accelerate study startup timelines.