Nielsen Norman Group

A reminder that data ≠ findings ≠ insights 😵💫 AI can help you collect and process data (observations). It can help surface findings (patterns across said data). Your competitors can, are, and will do the exact same thing...maybe even with the same tools. Insights are judgment applied to how these patterns connect to your context (think constraints, timing, business model, historical knowledge, potential actions, etc). If you rely on AI for insights, what are you doing that your competitors (or a junior researcher with 0 years of experience) can't replicate? Two great related articles on Nielsen Norman Group: 🔗 AI-Moderated Interviews: https://lnkd.in/gdk5ZeVF 🔗 Data vs. Findings vs. Insights: https://lnkd.in/gqEX2q3A

While I’m taking a step back from the NN/G UX Podcast over the next few months, the show will be in very capable hands as the amazingly witty and brilliant author and research leader Laura Klein steps in as a new co-host of the show! In our most recent episode, I interview Laura about what “lean” really means and how it’s gotten bastardized over the years. Check it out and follow along for even more great eps hosted by Laura coming soon!

For folks who miss the What is Wrong with UX podcast, I am going to be hosting the NN/G podcast for a while! But first, here I am being interviewed on it about Lean and Agile. https://lnkd.in/gFdeRucS

What makes a research repository actually work? It’s not just the tool. It’s the culture, curation, and care behind it. On the latest episode of the Rosenfeld Review podcast, Maria Rosala explores how AI can strengthen repositories by surfacing connections and insights while reinforcing the essential role of human judgment. 🎧 Listen here: https://lnkd.in/ehKhscbP #UXResearch #ResearchOps #AI

So, what did Lean UX actually mean to accomplish? If you work in UX, you've probably heard "lean," "Agile," and "MVP" more times than you can count. But the terms have become so commonplace that their original intent often gets lost in translation — or buried under process for process's sake. In this episode of the NN/G UX Podcast, Laura Klein joins Therese Fessenden to re-visit the foundations of Lean UX and explore how teams today can apply its principles without falling into common traps. They discuss: → The skill of moving between product vision and interaction details → Why growing companies struggle to balance speed and consistency → How to design thoughtfully in fast-moving environments Plus, we're excited to announce Laura as the newest co-host of the podcast. Stay tuned for her first episode as host next month! 🎧 Tune in: https://lnkd.in/eXGG6ak7 #LeanUX #UXStrategy #ProductDesign #DesignLeadership #NNgroup

If you work in research or product, you’ve likely felt the frustration of recommendations that never make it to users. In his latest article, my colleague Brian Utesch tackles this head‑on by introducing something his team at Cisco has relied on for years: the Recommendation-Adoption Score (RAS). RAS gives teams a structured, evidence-based way to measure where value is lost between insight and implementation. It forces clarity in recommendations, real ownership, and more honest conversations about adoption. Brian breaks down: - Why recommendations must be treated like inventory - How to calculate RAS - What the score actually means - How long teams should track - A real example of RAS transforming adoption behavior Highly recommend giving this one a read.

Clients are not asking for AI slop from expert UX consultants. That might sound blunt, but it reflects what we're hearing consistently across our consulting work. In the age of AI, client expectations haven't shifted toward automation — they've sharpened around the human skills that AI can't replicate. Clients want: → Strong judgment — knowing when and how to apply AI responsibly, not just knowing the tools exist → Clear recommendations — synthesizing evidence into a point of view, not presenting ten AI-generated options and asking stakeholders to choose → Respect for constraints — designing within organizational realities like legal requirements, legacy systems, and approval bottlenecks → Research rigor — thoughtful interpretation that surfaces meaningful patterns, not shallow insights produced too quickly → Plain language — cutting through hype and challenging AI output when it lacks specificity Being useful matters more than being impressive. And right now, the most useful thing a consultant can bring is the judgment, taste, and intentional care that experienced UX professionals are known for. Read Anna Kaley's full article: https://bit.ly/4qIYS36 #UXConsulting #AI #UXStrategy #UserResearch #NNGroup

AI can now facilitate user interviews — but knowing when to use it (and when not to) matters more than the technology itself. AI-moderated interviews are convenient — participants complete them on their own time, and you get transcripts without scheduling a single session. But convenience comes with tradeoffs. These tools fall short in semistructured discovery — the kind of research where the best insights come from following unexpected threads, adapting questions in real time, and reading nonverbal cues. We've identified 4 situations to keep AI out of your interviews: → The topic requires deep domain knowledge. If participants use specialized jargon, AI may miss key details or fail to follow up on what matters. → You're in early discovery. Discovery research requires pivoting in real time — if you don't know what to ask yet, it's tough to set an AI up for success. → Each interview needs a different path. AI applies the same structure across sessions, which can miss opportunities to go deeper when participants have very different roles or contexts. → Participants need to show, not just tell. AI can't observe screen shares or task demos and respond with relevant questions. These tools are early, and they'll get better. But for now, they work best as a supplement to human moderation… not a substitute. Read about Maria Rosala's study: https://bit.ly/4qGTbCH #UXResearch #AI #UserInterviews #ResearchOps #NNGroup

I can’t quite believe how many wonderful voices we already have lined up for this year’s uxcon vienna – and how generous people are with their time, experience, and curiosity. I'm genuinely excited (and grateful) that we’ll be welcoming all of them to Vienna: 🤖 Meaghan Choi — Design Lead for Claude Code at Anthropic. 🧠 Elizabeth Churchill — HCI pioneer and former UX executive at Google, eBay and Yahoo, now working as a professor and department chair at MBZUAI. 📺 Indya McGuffin, Design Program Manager on Netflix’s design systems team. 🔍 Maria Rosala, Director of Research at Nielsen Norman Group. 🍏 Emma S., UX Writer at Apple (formerly Dropbox) 🔐 Max Schrems, lawyer, author and data protection activist at noyb, known for his legal challenges against Facebook. Seeing this group come together makes me really happy because of the perspectives, questions, and depth they bring into the room. This is already feeling very special!