Are We There Yet? – A Maturity Model for Enabling Process Monitoring and Learning in Biologics Manufacturing

• Discover why biopharma organizations use as little as 1% of their process data despite increased digitization, and how a structured 6×6 assessment can quantify as-is state
• Learn how to balance competing data priorities with a scoring system that aligns objectives across Quality, Digital, Manufacturing, and MSAT teams
• Explore how a process monitoring maturity model can drive AI-ready data foundations that enable organizations to understand and reduce process variability

Jack Prior
Head of Process Monitoring & Data Science/AI Strategy
Sanofi

Dr. Jack Prior leads MSAT Process Monitoring & Data Science/AI Strategy at Sanofi, where he spearheads global initiatives in process monitoring and AI-driven yield improvement for biologics manufacturing. Previously as Head of MSAT Digital, he led teams working to develop global process data analytics systems and to digitize laboratory operations for Industrial Affairs Specialty Care drug substance and drug product manufacturing.

His leadership spans nearly three decades at Sanofi-Genzyme, where he has led manufacturing science organizations supporting process characterization, modeling, technology transfer, and manufacturing operations for critical therapies treating rare genetic disorders. Throughout his career, he has focused on integrating process modeling and advanced analytics with manufacturing science to enhance biologics production across US and European operations. He has led manufacturing science teams supporting multiple tech transfers across the Sanofi network, including the team supporting transfer to a new continuous single-use digital facility, while advancing approaches to process modeling and scale-up.

Dr. Prior holds a Doctor of Science in Chemical Engineering from MIT, where his research in data reconciliation for bioprocess development laid the foundation for his work in process monitoring and control. His current work continues to advance manufacturing science through the integration of digital technologies, process modeling, and AI in biologics manufacturing.