What You Should Know Before Using Google NotebookLM at Work
**TLDR**
NotebookLM is an AI assistant from Google that reads and answers questions about your own documents. It sounds simple, but if you use it without clear purpose or guardrails, you risk wasting time, sharing the wrong material, or drawing the wrong conclusions. This blog explains what NotebookLM does, where it fits, and how to use it safely in a workplace context.
“Just try it”—but you’re not sure what it is
If someone on your team or in your network has said “NotebookLM can summarise reports for you,” they’re not wrong. But they’ve skipped a few steps.
You’re being handed tools that sound useful — but without clarity on:
What they’re actually for
How much you can trust the answers
Whether they’re safe to use with work files
You’re not overthinking it. You’re being responsible. This is the gap most people fall into: trying tools before understanding them.
Let’s close that gap.
What is NotebookLM actually for?
NotebookLM is an AI tool built by Google. It lets you upload your own content — documents, PDFs, notes — and then ask questions about what you’ve uploaded. It answers based only on that material.
Think of it as a private assistant that reads your documents and responds based on what’s inside.
People typically use it to:
- Summarise long documents
- Extract key points or quotes
- Ask follow-up questions in plain English
- Generate drafts or FAQs from internal materials
But it has limits. It doesn’t fact-check. It doesn’t know if your file is a final version or a draft. It won’t warn you if the content is outdated or irrelevant.
That’s where problems start.
Before you start using it
Before you upload a report or project doc, ask yourself:
1. Is this the right kind of document?
NotebookLM pulls its answers only from the content you provide. If that content is incomplete, out of date, or poorly written, the tool still uses it. It won’t know any better.
2. Do I know what I want from it?
NotebookLM isn’t a general search engine. You won’t get value unless you’ve got a specific task in mind — like checking consistency across policies or pulling themes from a training transcript.
3. Am I uploading anything sensitive?
If you’re logged in through a personal Google account, you might be uploading work material into a tool without proper controls. Most people don’t mean to breach policy — it happens because no one’s explained the risks.
The tool isn’t dangerous on its own. But using it without thinking? That’s where risk creeps in.
Why “just experimenting” isn’t always safe
When AI tools feel easy to use, it’s tempting to jump in without a plan. But we’ve seen what happens when people skip structure:
- Uploading the wrong content
- Trusting inaccurate summaries
- Sharing outputs without checking them
You don’t need formal training. But you do need a baseline understanding:
- What kind of tasks it suits
- When to double-check outputs
- What not to upload
That’s what turns trial and error into capability.
What effective use actually looks like
Used well, NotebookLM can:
- Save time summarising complex content
- Help you prepare meeting packs or team notes faster
- Reduce back-and-forth on large internal documents
But that only happens when:
- You upload the right material
- You use it for scoped, clear tasks
- You know when human judgement is still required
No tool replaces thinking. But tools like this can help you work smarter — if you treat them with the right level of care.
Real example: A mistake that nearly went unnoticed
We worked with a team using NotebookLM to help summarise monthly audit materials. It was working well until someone uploaded a report from the wrong month. The tool summarised it accurately. The problem? It was the wrong input. The result nearly made it into a board update.
After that, the team added a simple file labelling process and set clear guidelines: what to upload, how to check dates, and when human review is non-negotiable. It didn’t slow them down, it just removed the blind spots.
That’s the kind of adjustment most teams never make. And that’s where small errors turn into real consequences.
Want to make AI tools useful — without guessing?
If you’re already trying tools like NotebookLM, or you can see them creeping into team workflows, it’s time to give people more than “just try it.”
We run No Code AI Implementation sessions that help teams:
- Match tools to the right use cases
- Build confidence in how to use them
- Avoid unintentional mistakes or rework
- Create small, structured workflows that actually save time
You don’t need to be technical. You just need to know where to start (and where things usually go wrong).
Book a call now to explore how we help teams build safe, useful AI workflows.