The clinical trial landscape is fundamentally transforming with generative AI adoption. Historically, trials have relied on manual processes for protocol design, database lock, and regulatory documentation, often leading to inefficiencies, rising costs, and prolonged development timelines. Growing clinical data volume and increasingly stringent regulatory requirements pressure pharma companies to optimize costs and accelerate drug development.

Generative AI is uniquely positioned to address these challenges by synthesizing fragmented datasets, automating routine tasks, and enabling real-time data analysis and predictive modeling. By streamlining trial workflows, supporting adaptive trial designs, and enhancing decision-making, it is helping sponsors improve operational efficiency while maintaining data integrity and compliance.

Integrating generative AI into clinical trials requires redefining core processes, reimagining stakeholder roles, and implementing new workflows that allow AI-driven insights to be seamlessly embedded into trial operations. This Viewpoint explores how sponsors can effectively adopt generative AI, key implementation considerations, and the best practices to ensure successful clinical development integration.

Scope

• Industry: life sciences
• Geography: global

Contents

In this report, we examine:

• How generative AI addresses inefficiencies and drives innovation in trial processes
• Strategic changes required to integrate generative AI, including redefining roles and optimizing workflows
• Key considerations and best practices for successful adoption and organizational goal alignment

Membership(s)

Clinical Development Technology

Sourcing and Vendor Management