How Medical Affairs Professionals Can Build Their AI Skills

AI is moving quickly through Medical Affairs organizations, but individual MSLs are often left to figure out where and how to use it responsibly on their own. This conversation addresses that gap directly.

Nandini Sabharwal, Field Medical Director at Pfizer, joins Scott Thompson to make the case that AI adoption in Medical Affairs starts at the individual level, not with enterprise rollouts. Drawing on her background as a clinical pharmacist and Medical Science Liaison, Nandini walks through how personal AI use builds the confidence and fluency that later carry over into compliant field workflows. The conversation covers where public AI tools are appropriate and where company-approved tools are required, how a tool like NotebookLM can support scientific literature review, and where the practical wins already exist for planning, insight capture, and efficiency. If you're deciding where to start with AI in your own Medical Affairs role, this is a workable first step.

Key Takeaways:

• Skill-building starts with the individual, not the enterprise rollout. Nandini argues that personal AI use builds the confidence and fluency MSLs need before any organization-wide tool adoption makes sense.
• Public and company-approved AI tools serve different purposes. The conversation draws a clear line between using public AI tools for personal learning and using company-sanctioned tools for actual work product.
• NotebookLM and similar LLM tools can accelerate scientific learning, helping MSLs synthesize literature faster without replacing their own scientific judgment.
• Practical AI workflows already exist for MSLs today, particularly in planning, insight capture, and day-to-day efficiency, not just in future-state visions.
• Learning AI is a competitive necessity for the field, not optional. A 2026 Cureus survey of Medical Affairs professionals found 87% consider learning AI technology important or very important to remaining competitive, even though only about a third of organizations have established formal policies governing AI use by MSLs.
• AI is framed as expanding impact, not replacing the human element, with Nandini emphasizing that the goal is augmenting scientific engagement, not automating it away.

Resources Mentioned

NotebookLM by Google: An AI-assisted research tool Nandini references as a way to accelerate scientific literature review and synthesis.

About Our Guest

Nandini Sabharwal is a Field Medical Director at Pfizer. She is a seasoned Medical Science Liaison with over five years of Medical Affairs experience spanning multiple therapeutic areas, and she leads the Internal Medicine Field Medical AI workstream, focused on bridging AI innovation with field medical operations such as medical engagement and insight generation.

Connect with Nandini on LinkedIn: https://www.linkedin.com/in/nandini-sabharwal/

FAQ

Why does AI adoption in Medical Affairs start with individual skill-building instead of enterprise rollouts?
Because confidence and fluency with AI tools build faster through personal, everyday use than through a top-down platform rollout. Nandini frames individual skill-building as the foundation that later supports compliant, organization-wide adoption.

What's the difference between using public AI tools and company-approved AI tools in Medical Affairs?
Public AI tools are appropriate for personal learning and building familiarity with how the technology works. Company-approved tools are required once the use case touches actual work product, where compliance, data handling, and governance requirements apply.

How can a tool like NotebookLM support Medical Affairs work?
Tools like NotebookLM can help MSLs and other Medical Affairs professionals synthesize scientific literature more quickly, supporting faster learning without replacing the professional's own scientific judgment.

Does using AI risk replacing the human element of the MSL role?
No. The conversation frames AI as a way to expand an MSL's impact, not substitute for it. Scientific exchange, judgment, and relationship-building remain human-led; AI supports preparation and efficiency around that work.

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