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 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.
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
Key metrices from our successful webinar session
350+
Registartions
90+
Unique viewers
3364
Minutes viewed
Featured Voices
Visionaries who led the conversation.
Larry Puderbach
AVP Research Quality, Merck
Nagesh Nama
Chief Executive Officer, xLM
60 Minutes of
Practical Insight
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 in Pharma R&D Insights
This session featured expert insights from Larry Puderbach, focusing on the realities of implementing AI across preclinical and clinical domains. The discussion emphasized governance, data integrity, and quality transformation required to operationalize AI within GxP-regulated ecosystems.
- Why AI must operate within QMS and GxP frameworks to be compliant and scalable
- Challenges in deploying AI, including data fragmentation, validation complexity, and oversight requirements
- How risk-based governance enables safe and efficient AI adoption across R&D workflows
- The importance of explainability, transparency, and audit readiness in regulated AI systems
- Building future-ready quality organizations to support AI-driven transformation
A Glance at Our Online Event
Couldn't make it in person? Our hybrid model ensured that thousands joined us virtually. Experience the high-fidelity stream that brought the summit to living rooms worldwide.
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