Harnessing AI for Pre-Award Landscape Analysis And Site Selection

Artificial intelligence is transforming early-stage clinical trial planning by enabling faster, more precise decisions in site selection and feasibility analysis. Traditional approaches often rely on fragmented data and manual processes, which can lead to delays and missed opportunities.

AI-driven landscape analysis aggregates diverse datasets—such as historical trial performance, investigator experience, and regional patient demographics — into actionable insights. This allows teams to identify optimal sites, predict enrollment potential, and mitigate risks before contracts are signed. By leveraging predictive modeling and real-time analytics, sponsors and CROs can streamline pre-award activities, reduce costs, and improve trial success rates.

Understanding how AI reshapes these processes is critical for organizations seeking a competitive advantage in an increasingly complex research environment. Explore how AI can accelerate your pre-award strategy and optimize site selection—read the full resource today.