NotebookLM is useful, but it’s still just LLMs deep down. There’s no magic technology that makes it immune to the problems of other AI systems.
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Seen last week on social media: a market researcher trying to save their job after reporting a nonsense, LLM-generated analysis of survey data. It’s a common mistake, and there’s not much training available for researchers on how to account for differences between generative AI and traditional research tools.
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The reality is that many researchers are now expected to use LLM-based tools in their work, despite their shortcomings. And it can be hard to push back on mandates from above, or competition from peers who don’t understand the risks. Are there ways we can integrate LLMs into research while still being responsible professionals about it?
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When we use AI tools in product design, strategy, and user research, we need to keep something important in mind: the AI is not “understanding” our requests, and even with safeguards, it may not catch when it’s making mistakes.
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Error-checking AI output takes time, so we need to be wary of claims of “time saved” using AI for product research. A thought-provoking quote by Emily M. bender from the March 31 episode of her Mystery AI Hype Theater 3000 podcast underscores this.
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Siloed work leads to blind spots in AI feature design–you need more diverse perspectives. A story from early in my career illustrates why this requires a new level of partnership between UX and engineering.
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Since it launched, I’ve been testing how well gpt-oss-20b does at qualitative research synthesis. I’ve been able to run dozens of giant, 52K-token prompts on my standard-issue MacBook Pro in just a minute or two apiece, and the results have been surprisingly good overall–albeit, with the same egregious errors that we’ve come to expect in LLM citations.
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There’s a widely circulated piece of bad advice when it comes to AI: “If you don’t know how to use AI, just ask the AI!” The problem with this advice is that it assumes the AI “knows” about its own functioning. Spoiler alert: it doesn’t! (But it is good at making up plausible sounding answers!)
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Researchers should be in the driver's seat when it comes to AI tools, and that means understanding their tradeoffs just like we do with traditional research methods. My AI for UX Researchers workshop is not about teaching you to use any one specific AI tool–it's about giving you the skills to evaluate the performance of any AI model or service critically.
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There’s a pressing need to measure AI adoption success not in terms of output, but outcomes. A lot has changed since I gave this talk on “Coexisting with AI” at Rosenfeld Media’s Advancing Research in March, but this core principle hasn’t.
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Last week, I presented to the UX research community at the Library of Congress. I shared five ways that UX roles are evolving with AI, from process changes to entirely new capabilities.
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In my AI+UXR workshops, I recommend starting a fresh chat each time you ask the LLM to do a significant task. Why? Because UX research tools need to be reliable, and the more you talk to the LLM, the more that reliability takes a hit.
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A highlight of last week’s AI+UXR workshop: seeing participants discover their own novel issues with AI synthesis for user research. This is the goal.
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UX and design professionals are at a pivotal moment in the AI “revolution”: do we choose to define our value in terms of output or outcomes? Focusing on efficiency and deliverables (“output”) is the road to commoditization. But we still have an opportunity right now to shape the discussion about how to measure design impact.
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As a design professional in 2025, what should you be doing to future-proof your career for AI? Last week I met with Bria Alexander, Brittany Hobbs, and Christopher Noessel to discuss the latest AI developments, and what was top of mind for them was not a specific new model or tool, but questions of access and resourcing.
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Most AI+UX discourse today is framed in terms of adoption vs. rejection. In prep for Designing with AI 2025, I talked with Carla Diana, Kanene Ayo Holder, and Tom Armitage on why this framing does designers a disservice.
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Designers use principles like unity, balance, and contrast to guide user attention in traditional interfaces, but what are the principles for designing "intelligent" interfaces? Josh Clark, Veronika Kindred, and Kritika Yadav may have cracked the code.
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As design professionals struggle to figure out what AI means for the way we work, our partners across our organizations–PMs, engineers, and more–are all trying to answer the same questions. Erika Flowers and John Donmoyer show how designers are taking the lead in AI adoption, helping designers and non-designers alike to envision new ways of working.
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