AI in Pharma R&D: From Hype to Regulated Impact
This LinkedIn Live session with Larry Puderbach explored how AI is being implemented across pharmaceutical R&D while ensuring compliance, data integrity, and inspection readiness in regulated environments.
AI in Regulated R&D
AI is transforming preclinical, clinical, and pharmacovigilance processes. This session highlighted how AI must operate within GxP frameworks, integrating with QMS to ensure validation, traceability, and regulatory compliance across the R&D lifecycle.
Real-World Implementation
The discussion focused on practical challenges including risk-based governance, vendor oversight, and explainability. It showcased how organizations are moving from AI pilots to production-ready systems that support audits, quality, and decision-making.
Event at a Glance
350+
Registrations
90+
Unique Viewers
3364
Minutes viewed
Featured Voices
Visionaries who led the conversation.
Nagesh Nama
Chief Executive Officer, xLM
Larry Puderbach
AVP Research Quality, Merck
60 Minutes ofPractical Insights
March 26, 2026
Full access session recording available on demand.
60 Minutes of Content
Candid discussion covering governance, compliance, and AI adoption in pharma R&D.
This LinkedIn Live session brought together industry expertise to explore how AI is being integrated into pharmaceutical R&D within regulated environments. From risk-based governance and QMS integration to vendor oversight and data integrity, the discussion highlighted practical approaches to making AI inspection-ready while accelerating innovation.
Detailed Event Overview
The LinkedIn Live event on AI in Pharma R&D explored how life sciences organizations are moving from experimental AI initiatives to structured, compliant adoption within regulated environments. The session highlighted how AI must integrate with quality systems, risk frameworks, and governance models to deliver real impact while ensuring inspection readiness.
AI Adoption in Regulated R&D
Explore how pharmaceutical organizations are moving beyond AI experimentation toward practical implementation within regulated development environments.
Governance & Risk Management
Understand the governance frameworks, risk controls, and oversight models required to support responsible AI adoption.
Data Integrity & Compliance
Learn how organizations can maintain data integrity, compliance, and validation expectations while deploying AI-powered solutions.
Inspection-Ready AI Programs
Discover practical approaches for building AI initiatives that align with regulatory expectations and support inspection readiness.