1.0. Introduction

Artificial Intelligence is reshaping pharmaceutical research. From preclinical modeling and biomarker discovery to clinical trial optimization and pharmacovigilance analytics, AI enables faster insights, predictive decision-making, and efficient research.

However, adopting AI in pharmaceutical R&D poses a unique challenge. Unlike many industries, pharmaceutical innovation operates in highly regulated environments where compliance, data integrity, and patient safety are non-negotiable.

As organizations accelerate digital transformation, a critical question arises:

“How can pharmaceutical companies adopt AI while maintaining governance, quality oversight, and regulatory accountability in GxP environments?”

To explore this from an operational perspective, xLM Continuous Intelligence hosts an exclusive LinkedIn Live conversation with Larry Puderbach, Associate Vice-President of Research Quality at Merck & Co.

Larry leads global Research Quality in preclinical and clinical domains, overseeing vendor oversight, pharmacovigilance quality, technology governance, enterprise risk management, and global R&D audit programs. His team drives Quality by Design transformation and manages Quality Management System governance across research organizations.

Operating at the intersection of innovation, regulatory accountability, and enterprise risk leadership, Larry offers a rare perspective on how pharmaceutical companies navigate AI adoption in regulated R&D environments.

This session offers a unique chance to learn from a leader responsible for quality governance across global pharmaceutical R&D operations, where innovation and regulatory responsibility coexist.

2.0. AI Adoption Is Accelerating Across Pharmaceutical R&D

Pharmaceutical organizations are rapidly integrating AI into research. These technologies are used to:

• Improve preclinical modeling and predictive toxicology

• Enhance clinical trial design and patient recruitment strategies

• Analyze pharmacovigilance data for early safety signal detection

• Optimize research operations through advanced analytics and automation

• Support decision-making through predictive insights

Regulatory authorities like the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA) focus on ensuring AI systems maintain transparency, traceability, and reliability in regulated environments.

This creates a new reality: AI innovation must include robust governance, risk management, and quality oversight.

Organizations that balance innovation with compliance will accelerate progress while remaining inspection-ready and aligned with global regulations.

3.0. Learning from Merck Research Quality Leadership

Few roles sit closer to the intersection of AI innovation and regulatory accountability than research quality leadership.

At Merck, Larry Puderbach leads global initiatives across domains including:

• Research quality governance for preclinical and clinical programs

• Vendor and technology oversight across R&D ecosystems

• Pharmacovigilance quality and analytics oversight

• Enterprise risk management within research organizations

• Quality Management System governance and digital transformation

• Global R&D audit and inspection readiness programs

His team drives Quality by Design (QbD) transformation initiatives, embedding quality principles into research processes and digital systems.

This role provides insight into how pharmaceutical companies must evolve quality organizations, governance, and technology oversight to support AI-driven innovation.

4.0. What Attendees Will Learn in This Conversation

5.0. A Candid Conversation: No Slides, No Scripts

Unlike traditional webinars, this LinkedIn Live session is an unscripted, presentation-free discussion.

Hosted by Nagesh Nama, the conversation focuses on:

• Real experiences implementing governance frameworks

• Lessons learned managing quality oversight in digital environments

• Practical challenges introducing AI into regulated workflows

• Honest insights on AI adoption's opportunities and limitations in pharmaceutical R&D

This format creates a transparent, engaging discussion based on real experience rather than theory.

6.0. Why This Conversation Matters for Pharma Leaders

The pharmaceutical industry is at a critical point in its digital transformation.

AI can accelerate research, improve decisions, and unlock insights from complex biomedical data. Success depends on governance and integration within regulated systems.

For pharma leaders, the challenge is clear:

• AI systems must earn trust from scientists, quality teams, and regulators

• Governance frameworks must evolve to address new technological risks

• Quality organizations must shift from compliance roles to strategic digital innovation partners

Organizations that build proper governance today will create AI-enabled R&D ecosystems that are innovative, compliant, and resilient.

This session offers insights for professionals facing these challenges, including:

• R&D quality leaders

• GxP and QMS professionals

• AI and digital transformation leaders

• Risk and compliance officers

• Clinical and preclinical technology teams

By learning from leaders managing these complexities in global pharmaceutical organizations, participants can better understand how to operationalize AI responsibly in regulated environments.

7. Join the Live Conversation

If your organization is exploring AI integration in research, analytics, or regulated data systems, this LinkedIn Live event offers practical insights from a leader responsible for research quality at a leading pharmaceutical company.

📅 LinkedIn Live Event
March 26, 2026| 12:00 Noon EST
AI in the Age of Regulated R&D: Governance, Risk, and Quality Leadership

Featuring
Larry Puderbach - Associate Vice-President, Research Quality, Merck & Co.

Hosted by
Nagesh Nama - CEO, xLM Continuous Intelligence

This is a timely discussion for pharmaceutical leaders navigating AI innovation, quality governance, and regulatory accountability.

👉 Reserve your spot to join the live conversation and learn practical lessons from leaders at the forefront of AI-driven pharmaceutical R&D.