NotebookLM for MSLs: Save Time & Prep Smarter for KOL Meetings

If there’s one thing MSLs know, it’s that preparation is everything. But life doesn’t always go as planned. Meetings sneak up on you, your schedule fills with last-minute requests, and suddenly, you’re facing a big KOL meeting without enough time to prepare. Sound familiar? That’s where AI can be a game-changer.

In a previous article, we explored two powerful AI approaches: custom GPTs and prompt-based strategies. Both methods help MSLs extract info about their KOLs quickly and prepare efficiently. Now, let’s throw a third option into the mix (with a great feature that is extra helpful for MSLs): NotebookLM.

In this article, I will convince you that you should check out NotebookLM by taking a pre-meeting planning prompt previously used in ChatGPT and comparing the outputs. Does it produce groundbreaking insights? Eh, probably not. But it does pull pre-meeting planning insights quickly and helps cut the prep time down? 100% yes. And that’s why you should check it out.

And get 3 surprising ways MSLs are starting to use NotebookLM in 2025!

AI Disclaimer: Use It Responsibly

Before diving in, let’s address a crucial point: always use AI tools responsibly.

1. Validate all outputs against verified data sources (get more tips on how to spot hallucinations here).
2. Follow your organization’s compliance policies and avoid inputting proprietary or sensitive data into any tool.
3. Think of AI as an assistant. It can help with efficiency and creativity but it is not a substitute for your expertise.

Where NotebookLM Excels Compared to ChatGPT

NotebookLM is designed to work directly with source documents. What does this mean for MSLs? It’s an excellent application for reviewing the literature and pre-meeting planning. What sets it apart? Its ability to show citations and highlight exactly where the information was pulled from makes it a great tool for fact-checking and verifying outputs. If you are nervous about hallucinations, you will love this feature.

For MSLs preparing for a KOL meeting, this means you can:

• Quickly extract actionable info from compliant, non-sensitive documents, like publications
• Verify every output with its citation, giving you confidence in the AI’s recommendations

For example, if you are reviewing a KOL’s recent study, NotebookLM not only summarizes key findings but also pinpoints where in the publication these points are made, allowing you to confidently cite details in your meeting.

New Ways MSLs Are Using NotebookLM in 2025

NotebookLM is really starting to take off among MSLs.

Sarah Snyder and I are loving all the creative ways we’re seeing teams use it. Not just to prep faster, but to actually learn, connect dots, and think more strategically. And while turning papers into AI-generated podcasts continues to be one of the biggest crowd-pleasers, these next-level use cases are quickly becoming new favorites.

Keep reading to see how you can use NotebookLM for pre-meeting planning and grab a ready-to-use prompt you can test out with your own KOL materials.

In the meantime, here are 3 powerful ways to start using NotebookLM right now:

1. Study Guides for Learning TAs & Disease States

Need to get up to speed fast? Upload clinical guidelines and key publications, then ask NotebookLM to:

• Summarize core mechanisms of action

• Compare treatment algorithms

• Quiz you on key data

This is an easy way to accelerate your learning curve or refresh your knowledge before a high-impact meeting or congress. Another great way to check out get up to speed fast on a disease state is with deep research tools. 

 2. Upload Multiple Papers to Look for Patterns

Instead of manually trying to synthesize five different papers, let NotebookLM help you:

• Identify repeated themes, terms, or key points

• Surface consistencies in outcomes or methodology

• Flag conflicting data that could spark follow-up questions

Use this to quickly spot patterns and trends that would normally take forever to identify. This comes in handy when going wanting to speak at a deeper level with top KOLs. 

3. Create Mind Maps From Your Source Docs

This one’s for the visual thinkers.

Click a button for NotebookLM to generate a mind map from your uploaded documents. It can:

• Show how major themes connect

• Clarify complex scientific ideas

• Help you teach or explain pathways, mechanisms, or research landscapes to others

Perfect for simplifying complicated topics for your own prep or educating your cross-functional partners. Or maybe try it out for a journal club!

👉 Want to go even deeper? Check out our AI for MSL Excellence training to learn how to use AI like a pro. 

Pre-Meeting Planning Prompt: A Framework for Comparison

To test NotebookLM’s capabilities, I used the same pre-meeting planning prompt I used with ChatGPT. In this scenario, we pretended I had an upcoming meeting with Dr. Gil Yosipovitch and didn’t prepare very well. There were 3 papers I needed to review and I needed to plan for the meeting. 

Here’s the prompt I used to explore its output and compare results across tools:

“You are an experienced senior MSL and an expert in leveraging AI to enhance pre-meeting preparation and relationship-building with KOLs. Here’s the scenario:

– KOL Name: [Insert KOL’s Name]
– Institution: [Insert Institution]
– Area of Expertise: [Insert KOL’s Therapeutic Area/Research Focus]
– Relevant Publications: [Copy-paste relevant publications here]

Please provide insights into the following:

1. Key Takeaways: Summarize the most critical findings from the provided publications, emphasizing their impact on the field.
2. Motivations and Research Interests: Deduce potential areas of continued research and professional focus for the KOL.
3. Challenges and Pain Points: Identify possible hurdles or limitations faced by the KOL in their work or therapeutic area.
4. Emerging Topics: Point out related trends or new research areas that might be of interest based on the publications.
5. Value-Adding Data/Resources: Suggest relevant data, literature, or collaborative opportunities that could support the KOL’s research.
6. Personalized Engagement: Propose strategies for aligning your approach to reflect the KOL’s communication style and professional goals.
7. Conversation Starters: Develop questions or comments based on the publications that could help foster a meaningful discussion.
8. Key Collaborations: Highlight notable co-authors or frequent research partners that might be valuable to mention or inquire about.
9. Follow-Up Actions: Recommend potential follow-ups to maintain engagement and build the relationship post-meeting.
10. Non-Professional Interests: Note any insights into the KOL’s non-academic interests that could help establish rapport.

Ensure your response is concise, actionable, and optimized for a quick review to support effective pre-meeting preparation.”

Take this prompt and try it out for yourself! Use it in your favorite AI tool or even the internal tool your company provides. Then tell me how it went! 

Pre-Meeting Planning Output with NotebookLM

So, how did NotebookLM do with this prompt? ChatGPT and NotebookLM produced similar outputs with two callouts:

1. Transparency of Conclusions

– A major highlight of NotebookLM is its ability to show exactly where its conclusions were pulled from, making it easy to fact-check and verify. As I highlight in the video, you can click on a reference and it highlights where that info was pulled from in the source document. Super useful!

2. Variation in Key Insights

– NotebookLM identified a different key collaboration and provided unique suggestions compared to ChatGPT, showcasing its strength in analyzing and synthesizing details directly from source documents. I hadn’t seen highlighted before. This underscores its ability to find nuanced connections in source documents, an invaluable feature when preparing discussion points tailored to a KOL’s network (and to leverage for warm introductions). 

Here are the exact results from NotebookLM. What do you think of these?

The Key: Experimentation and Personalization

The best AI workflow is the one that works for you. Start experimenting:

• Use prompts to extract tailored insights (bad at prompting? Try mine)
• Leverage NotebookLM for fast analysis of specific documents, like publications
• Dive into SciSpace or scite for detailed literature exploration (it’s like having a professor on demand)
• Apply custom GPTs for workflows requiring high precision and repeatability (get 2 custom GPTs built for MSLs here)

By integrating these tools, you can maximize efficiency, deepen your understanding, and deliver more value during KOL meetings.

Which tool do you find most useful in your workflow? Let me know. I’d love to hear how AI is transforming your approach!

Get inspiration in my AI Guide for Productivity and Creativity for MSLs. Enter your info below to download 👇.

How NotebookLM Fits Into the MSL AI Landscape

1. Custom GPTs

2. Prompt-Based Strategies

3. NotebookLM

4. SciSpace

5. scite

AI Comparison Table for Pre-Meeting Preparation

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