Accelerate Your UX Research with NotebookLM: My Experience as a Product Designer
As a product designer, especially if you’re on a smaller team, conducting user research can feel like a never-ending task. Between balancing design work and gathering insights, it’s easy to wonder if there’s a faster way to get the answers you need. Enter NotebookLM — a tool I recently used while working on the edX mobile app, and it might just change how you approach research.
The Problem: Synthesizing UX Research is Hard
When designing for the edX mobile app, my goal is to increase weekly active users (WAUs) by refining the user experience. To get started, I need to gather insights from several different sources available to me — past user interviews, app reviews, blogs, and expert tips on engagement strategies.
Traditionally, this process is labor-intensive. User interviews require diligent observation, and without experience or fast note-taking skills, it’s easy to overlook critical feedback. To prevent this, we’d typically bring in extra team members to split up note-taking and moderation tasks. While this ensures more accuracy, it stretches our UX resources, diverting time from other pressing design tasks. After that, synthesizing the findings — a process that often involves multiple people — takes even more time, even if it does help the team build ownership over the problem.
The challenge I faced was finding a way to quickly summarize multiple sources from across the web and our own moderated interview notes without compromising quality.
How NotebookLM Fits into the UX Workflow
NotebookLM can fit seamlessly into a typical UX research workflow. It streamlines some of the most labor-intensive parts of the process: Gathering user interviews and external resources to “affinitize” into actionable themes. Traditionally, this requires hours of manual work, often involving multiple team members. NotebookLM automates much of it, reducing the workload and freeing up time for interpreting insights and making informed design decisions.
Using NotebookLM: A Game-Changer for Research Synthesis
When I introduced NotebookLM into this workflow, the change was immediate. I uploaded over a dozen sources, including interview notes, app reviews, and blogs, and within minutes, the tool generated summaries that highlighted key themes and recurring patterns.
What would normally take hours, or even require a team effort, was completed in a fraction of the time.
NotebookLM in Practice: Spending Time Wisely
For teams in software product development, UX research is often not a primary role but still critically important. This is where NotebookLM fits in so well. It doesn’t replace the expertise of the researcher or the nuances of human understanding, but it eliminates the heavy lifting that can consume too much time. This will allow me to spend more time considering the insights and developing solutions rather than wrestling with data analysis.
An Expert Take on AI Tools for UX Research
While I’m not a dedicated UX research specialist, I have conducted independent research that’s driven business outcomes — whether it’s increasing conversion rates or shaping product strategy. From that experience, I can say that AI tools like NotebookLM have the potential to augment research efforts. But to put it in the words of UX research experts, these tools should be seen as aids, not replacements.
Here’s what a UX research expert might say:
- AI accelerates research synthesis but needs human review to ensure accuracy and relevance. (User Interviews)
- It’s particularly valuable for large-scale projects, saving time by automating the laborious parts of research. (Heymarvin, UXTweak)
- While AI can highlight patterns, the final insights and recommendations require human interpretation. (OptimalWorkshop)
- By streamlining the process, tools like NotebookLM make UX research more accessible to smaller teams, leveling the playing field for better-informed design decisions. (OptimalWorkshop, UXTweak)
Icing on the Cake
One of the most delightful surprises was NotebookLM’s ability to generate an AI-based, two-person podcast summarizing all the sources I provided. The podcast was under 25 minutes and summarized our user research in an engaging, conversational format. The AI voices even threw in jokes, making it an entertaining way to share key findings. It aligned very well with our strategic direction for the app, and it was a refreshing way to communicate insights to stakeholders in a digestible format.
Conclusion: Give NotebookLM a Try
If you’re a product designer, especially one without easy access to a UX research specialist, I encourage you to give NotebookLM a try. It enables you to focus on what you do best — designing — while automating the heavy lifting of research synthesis. Whether you’re working solo or as part of a small team, NotebookLM could be the bridge that connects user feedback and great design decisions, without the grind.