My take on Google NotebookLM

Let’s face it: juggling endless case readings, group projects, and prepping for a future product management role isn’t easy. As an MBA student eyeing the PM track, I’m always on the lookout for tools that can help me learn faster, synthesize information more efficiently, and maybe even give me a preview of how real product development decisions are made. Enter Google’s NotebookLM—a buzzy, experimental AI-powered note-taking and research tool.

In this blog, I’ll share how I, an aspiring product manager, see NotebookLM becoming part of my daily routine—what it nails, what needs some work, and where I still hit a few bumps along the way.

Quick Refresher: What is NotebookLM?

For the uninitiated, NotebookLM (formerly “Project Tailwind”) is Google’s experimental AI that connects to your Google Docs and helps you summarize, analyze, and even brainstorm ideas based on whatever content you feed into it. Think of it as a personal “AI sidekick” that lives inside your notes—your personal GPT-like assistant with all your study materials and class scribbles at its disposal.

Daily MBA Workflow: Where NotebookLM Fits

1. Case Study Summaries

Situation: We’re assigned another 25-page Harvard Business School case on market expansion—time to dive deep and uncover insights!

How NotebookLM Elevates the Experience:

• Quick Summaries: It distills the key points, giving me a solid foundation to build deeper analysis.

• Smart Q&A: I can ask focused questions like, “Why did Company X choose to enter Market Y first?” to guide my strategic thinking.

• Instant Access to Data: It surfaces relevant quotes and data, helping me connect the dots faster without losing time flipping through pages.

It’s like having a research assistant that enhances my learning process, making case studies more engaging and efficient!

Product Manager’s Lens: In the real world, product managers sift through user feedback, competitive analyses, and loads of market data. Having an AI that can quickly glean insights and highlight them is a huge time-saver. It’s almost like a mini user research aggregator.

2. Group Project Collaboration

Situation: My team and I have three Docs: one with competitor analysis, another with marketing data, and a third with our product roadmap ideas. We need to merge them into a cohesive strategy deck.

• How NotebookLM Helps:

  • It cross-references all three docs, letting me ask something like, “What are the top three differentiators we identified across these competitors?”

  • Helps generate quick bullet points for a pitch deck or to-do list based on each doc’s content.

Product Manager’s Lens: Collaboration is everything. As a PM, you’re pulling from engineering specs, marketing insights, sales figures, and user stories. Having an AI that seamlessly threads all these docs together is a glimpse into the future of integrated product development tools.

3. Brainstorming Feature Ideas

Situation: We’re tasked with coming up with new product features for a hypothetical app in class.

• How NotebookLM Helps:

  • It can take your existing data or notes and turn them into a list of potential features or user stories (e.g., “Suggest three improvements to solve X user pain point based on the user feedback doc.”).

  • It cites where it found those suggestions, so you can verify them.

Product Manager’s Lens: Ideation is often the toughest part. You’re trying to connect user feedback to actionable features. NotebookLM’s suggestions aren’t always perfect, but they spark new directions. It’s like having an extra brainstorming buddy on call.

The Pros: What I Love

1. Time-Saving Summaries
   Not gonna lie—speed matters. Having an AI quickly parse out the main points and letting me dive deeper on demand is a dream come true, especially the night before class when you have a jam-packed schedule.
   
   

2. Focus on My Own “Library”
   Unlike a general chatbot that tries to be an expert on everything, NotebookLM focuses on my docs, my notes, and my context. That personalization means the output is usually more relevant to what I’m studying or building.
   
   

3. Conversational Q&A
   I love being able to ask follow-up questions, refine them, and watch the AI hone in on exactly what I need. This iterative process feels somewhat akin to a real teammate who’s done all the reading.
   
   

4. Potential for PM Workflows
   Being a product manager means merging user insights, dev constraints, and business strategy. A tool that unites all those documents? Super promising.
   
   

The Cons: Where It Still Stumbles

1. Occasional AI “Hallucinations”
   Sometimes NotebookLM comes back with a response that seems off or references something I don’t recall seeing in the original doc. As an MBA student (and future PM), I’m taught to trust but verify—so I always fact-check.
   
   

2. Context Switching
   Right now, you have to grant NotebookLM access to specific docs. If your notes, case studies, and references are scattered across multiple tools (like Evernote, Notion, or local drives), you need to funnel everything into Google Docs. That’s an extra step.
   
   

3. Lack of Deep Product Templates
   Sure, it’s great at summarizing and brainstorming. But for real PM tasks—like building product requirements documents (PRDs), user journey maps, or detailed roadmaps—NotebookLM isn’t quite specialized enough yet. You can coax some frameworks out of it, but it’s not a one-stop shop.
   
   

Overall User Experience

If I sum it up: NotebookLM feels like a super-charged Google Docs extension that’s almost like working with a personal tutor or brainstorming partner. The user interface is straightforward—basically a side panel inside your doc or a separate interface where you can ask questions. It’s neat not having to toggle between a random AI chatbot website and your own notes. The integration keeps the friction low.

For an MBA student aiming to break into product management, it offers a taste of how AI-driven insights can streamline the research and ideation phases. It’s not perfect (we’re still waiting for it to handle spreadsheets and design docs more seamlessly), but it’s a strong early step towards a future where AI is embedded in every part of our workflow.

Final Thoughts

If you’re knee-deep in case studies or putting together product strategies for your MBA assignments, NotebookLM is definitely worth a spin—if you can get into the early access. It’s a fascinating glimpse into how we might do knowledge work in the future: with AI as a co-pilot, scanning our personal libraries, and surfacing insights on demand.

Just remember: no AI tool will ever replace the sharp thinking and domain expertise you bring to the table. You are the future PM, after all. Treat NotebookLM as an assistant that’s here to augment your skills, not do the job for you. With a bit of careful prompting and a watchful eye for accuracy, it can help you transform your MBA note-taking and case-crunching routine—while giving you some practical experience of how AI might fit into product workflows down the line.

What’s Your Take?
Have you tried NotebookLM or another AI note-taking assistant? Are you seeing the same benefits and drawbacks? Share your experiences below—I’d love to compare notes