Bristol Myers Squibb accelerates clinical trial timelines by months with Workbench generative AI platform

Phase III clinical trials are the most expensive and operationally complex stage of drug development, with median costs ranging from $19 million to over $100 million per trial. BMS needed to reduce delays in bringing potentially life-changing treatments to patients and address inefficiencies across its R&D, Global Product Development & Supply, and Commercialization divisions. Clinical trial operations were largely reactive, with limited real-time visibility into key metrics across partners and trial sites.

In partnership with Accenture, BMS implemented more than 30 generative AI solutions across its major divisions and built 'Workbench,' a clinical trial accelerator that fuses AI-driven insights with real-time operational data. Workbench organizes structured and unstructured data into decision-ready information, delivers role- and context-based recommendations, and anticipates issues by analyzing current and historical trial data. The platform standardizes best practices and creates a single source of truth across the drug development organization.

Workbench was adopted by over 30 of BMS' top priority clinical trial teams, with unique platform users surging from under 100 to nearly 900 in just 3 months. The approach contributed to an overall acceleration of BMS clinical trials by months and increased the speed of reaction time to prevent issues. BMS named Workbench its official platform for priority clinical trials and plans to expand it to all clinical trials.

• Real-time operational data combined with GenAI recommendations can shift clinical trial teams from reactive to proactive management, reducing costly delays.
• Rapid user adoption (9x growth in 3 months) signals that platform value must be immediately tangible — role-based, contextual insights drive engagement far faster than generic dashboards.
• Capturing and sharing institutional knowledge from past trials through AI creates compounding value over time, improving consistency and trust across a distributed R&D organization.