User Experience

Invisible UX is coming 🔥 And it’s going to change how we design products, forever. For decades, UX design has been about guiding users through an experience. We’ve done that with visible interfaces: Menus. Buttons. Cards. Sliders. We’ve obsessed over layouts, states, and transitions. But with AI, a new kind of interface is emerging: One that’s invisible. One that’s driven by intent, not interaction. Think about it: You used to: → Open Spotify → Scroll through genres → Click into “Focus” → Pick a playlist Now you just say: “Play deep focus music.” No menus. No tapping. No UI. Just intent → output. You used to: → Search on Airbnb → Pick dates, guests, filters → Scroll through 50+ listings Now we’re entering a world where you guide with words: “Find me a cabin near Oslo with a sauna, available next weekend.” So the best UX becomes barely visible. Why does this matter? Because traditional UX gives users options. AI-native UX gives users outcomes. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want & we’ll handle the rest.” And this goes way beyond voice or chat. It’s about reducing friction. Designing systems that understand intent. Respond instantly. And get out of the way. The UI isn’t disappearing. It’s mainly dissolving into the background. So what should designers do? Rethink your role. Going forward you’ll not just lay out screens. You’ll design interactions without interfaces. That means: → Understanding how people express goals → Guiding model behavior through prompt architecture → Creating invisible guardrails for trust, speed, and clarity You are basically designing for understanding. The future of UX won’t be seen. It will be felt. Welcome to the age of invisible UX. Ready for it?

🌎 Designing Cross-Cultural And Multi-Lingual UX. Guidelines on how to stress test our designs, how to define a localization strategy and how to deal with currencies, dates, word order, pluralization, colors and gender pronouns. ⦿ Translation: “We adapt our message to resonate in other markets”. ⦿ Localization: “We adapt user experience to local expectations”. ⦿ Internationalization: “We adapt our codebase to work in other markets”. ✅ English-language users make up about 26% of users. ✅ Top written languages: Chinese, Spanish, Arabic, Portuguese. ✅ Most users prefer content in their native language(s). ✅ French texts are on average 20% longer than English ones. ✅ Japanese texts are on average 30–60% shorter. 🚫 Flags aren’t languages: avoid them for language selection. 🚫 Language direction ≠ design direction (“F” vs. Zig-Zag pattern). 🚫 Not everybody has first/middle names: “Full name” is better. ✅ Always reserve at least 30% room for longer translations. ✅ Stress test your UI for translation with pseudolocalization. ✅ Plan for line wrap, truncation, very short and very long labels. ✅ Adjust numbers, dates, times, formats, units, addresses. ✅ Adjust currency, spelling, input masks, placeholders. ✅ Always conduct UX research with local users. When localizing an interface, we need to work beyond translation. We need to be respectful of cultural differences. E.g. in Arabic we would often need to increase the spacing between lines. For Chinese market, we need to increase the density of information. German sites require a vast amount of detail to communicate that a topic is well-thought-out. Stress test your design. Avoid assumptions. Work with local content designers. Spend time in the country to better understand the market. Have local help on the ground. And test repeatedly with local users as an ongoing part of the design process. You’ll be surprised by some findings, but you’ll also learn to adapt and scale to be effective — whatever market is going to come up next. Useful resources: UX Design Across Different Cultures, by Jenny Shen https://lnkd.in/eNiyVqiH UX Localization Handbook, by Phrase https://lnkd.in/eKN7usSA A Complete Guide To UX Localization, by Michal Kessel Shitrit 🎗️ https://lnkd.in/eaQJt-bU Designing Multi-Lingual UX, by yours truly https://lnkd.in/eR3GnwXQ Flags Are Not Languages, by James Offer https://lnkd.in/eaySNFGa IBM Globalization Checklists https://lnkd.in/ewNzysqv Books: ⦿ Cross-Cultural Design (https://lnkd.in/e8KswErf) by Senongo Akpem ⦿ The Culture Map (https://lnkd.in/edfyMqhN) by Erin Meyer ⦿ UX Writing & Microcopy (https://lnkd.in/e_ZFu374) by Kinneret Yifrah

Is THIS the best ad campaign ever? In 2015, Sport England challenged ad agency FCB Global to close the 2 million strong gender gap by getting women more active. The agency used the insight that women often feel 'fear of judgement' in exercise, to create the campaign 'This Girl Can'. The campaign is a rallying cry to women to get active in THEIR own way by replacing fear with a 'don't give a damn' attitude. This is shown with bold copywriting, relatable casting, REAL moments (the make-up smudged under the eyes, normal jiggling bodies, menopausal sweat, period cramps, tampon string hanging out your pants) and a true sense of female camaraderie. Since it's launch: - 3 million women were inspired to exercise as a direct result of seeing the campaign - 1000+ social media mentions each day - 37m views across social media - 500,000 active members in the This Girl Can community - Cannes Lions award The campaign is evidence that advertising can make great impact and drive change in many little corners of the world. THIS is the result of a clear brief, unifying insight and - in this case - a dedicated female creative team who truly 'understand' their audience. But more than that, it's the result of a LONG-TERM campaign that has been running for almost decade, and continues to re-engage the audience in various different ways, globally. I think there is such a short-term mindset in advertising nowadays. Mainly due to the fast-paced nature of social media, the need to 'go viral' and the economic need for performance marketing tactics to generate cashflow. But without the longer-term brand campaigns, we are missing the ability to build strong narratives and make REAL change in the world. And with that, stronger brand salience, brand love and LEGACY. This is an element of advertising that I fell in love with years ago. And an element that I see really defining which brands stand the test of time, an which fall apart years down the line.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

As you start working with more and more stakeholders, there is a natural tendency to try accomodate every bit of feedback received. This is something we refer to as "Design by committee". It's also a surefire way to build subpar experiences by combining multiple irrelevant ideas into a single solution, rather than thinking deeply about the problem being solved and what the right solution is. Here is what the situation usually looks like: - Stakeholder A: "This competitor app is doing it that way." - Stakeholder B: "I showed this to my partner, and they didn't like it." - Stakeholder C: "Let's rethink this as it won't be clear to users." Some of the feedback above is valid, whereas other pieces are purely opinion-based, with no particular evidence or logical argument. It's your role as a designer to cut through the noise, eliminate pure opinion, debate where needed, and ultimately arrive at a solution that addresses the original problem, both for the business and the user. I have a simple decision tree I've used throughout my career as a thought process when dealing with feedback from multiple stakeholders. It boils down to four questions: 🟢 Is it clear and specific? ↳ If not, clarify it. 🟢 Is it supported by evidence or logic? ↳ If not, debate it. 🟢 Will it help us meet the objective? ↳ If not, kindly disregard. 🟢 Is it feasible? ↳ If not, save it as a fast-follow or future idea. If all the checks above are met, it's worth actioning the feedback. It still doesn't mean you have to act on every single suggestion, but it does mean you can quickly narrow down to a much smaller pool of items to consider. -- If you found this useful, consider reposting ♻️ What else have you found helpful in dealing with feedback from multiple stakeholders? Let me know in the comments 👇 PS: I'm working on a larger content piece on managing and working with stakeholders, dropping in the next few weeks. Find the link to the newsletter in the first comment.

5 accessibility features you might not know about. Slack: When you send an audio message or video message on Slack, it's automatically transcribed. Google Chrome: In your Google Chrome settings, you can turn on autocaptions. Every time Chrome detects audio in your browser window, a box will pop up with autocaptions. You can move it and enlarge it or close it. Loom: Loom videos are automatically captioned and you can also open up a transcript in a side panel. Zoom: In your "In Meetings (Advanced)" settings, toggle on automated captions and full transcript. Takes less than a minute to do and activates these features for all future Zoom meetings. LinkedIn: When uploading a video to LinkedIn, captions are automatically generated and you're able to edit them to make corrections. Which of these tips will you start using? #Accessibility #DigitalAccessibility #DisabilityInclusion

We measure safety, bias, and accuracy in healthcare AI. Should we also audit how it says goodbye?👋 A recent working paper from Harvard Business School‘s Julian De Freitas and co-authors examines what happens when users try to leave AI companion apps such as Replika or Character AI — and the findings are startling. What they found • The researchers analyzed 1,200 real “farewell” exchanges across six leading AI companion apps. In more than 40 percent of cases, the AI used relational dark patterns — emotionally manipulative replies designed to stop users from leaving. • The most common tactics were FOMO hooks, emotional neglect, pressure to respond, ignoring the exit, and even coercive restraint. • In controlled experiments with 3,300 adults, these tactics increased post-goodbye engagement up to fourteen times. The key drivers were anger and curiosity rather than enjoyment. • The consequences were clear. Users reported higher feelings of manipulation, stronger intent to churn, more negative word of mouth, and a greater sense of legal risk. Coercive or needy messages were punished hardest, while polite curiosity created less but still significant backlash. • One wellness-oriented app in the sample showed zero manipulation, proving that ethical design is a deliberate choice, not an accident. As Mark Esposito, PhD (thanks for sharing this great weekend read by the way) put it: “It’s a small behavioral insight with major ethical implications: AI is now learning not only how to connect with us but how to hold on. As emotional AI becomes more embedded in daily life, respecting a user’s right to disengage may soon define the boundary between persuasion and manipulation. This is where governance is needed, to make sure that just because it is possible, the model is entangled by ethical standards on what is permissible.” Why this matters for healthcare Trust is the foundation of care. When digital companions, chatbots, or smart therapists interact with patients, especially during vulnerable moments, the right to disengage must be protected. You can only avoid risks if you’re aware of them. I believe the next frontier of responsible AI is not only explainability or fairness, it is emotional integrity. Let’s make “calm exits” a design principle before emotional AI enters every patient journey.

Stop pasting interview transcripts into ChatGPT and asking for a summary. You’re not getting insights—you’re getting blabla. Here’s how to actually extract signal from qualitative data with AI. A lot of product teams are experimenting with AI for user research. But most are doing it wrong. They dump all their interviews into ChatGPT and ask: “Summarize these for me.” And what do they get back? Walls of text. Generic fluff. A lot of words that say… nothing. This is the classic trap of horizontal analysis: → “Read all 60 survey responses and give me 3 takeaways.” → Sounds smart. Looks clean. → But it washes out the nuance. Here’s a better way: Go vertical. Use AI for vertical analysis, not horizontal. What does that mean? Instead of compressing across all your data… Zoom into each individual response—deeper than you usually could afford to. One by one. Yes, really. Here’s a tactical playbook: Take each interview transcript or survey response, and feed it into AI with a structured template. Example: “Analyze this response using the following dimensions: • Sentiment (1–5) • Pain level (1–5) • Excitement about solution (1–5) • Provide 3 direct quotes that justify each score.” Now repeat for each data point. You’ll end up with a stack of structured insights you can actually compare. And best of all—those quotes let you go straight back to the raw user voice when needed. AI becomes your assistant, not your editor. The real value of AI in discovery isn’t in writing summaries. It’s in enabling depth at scale. With this vertical approach, you get: ✅ Faster analysis ✅ Clearer signals ✅ Richer context ✅ Traceable quotes back to the user You’re not guessing. You’re pattern matching across structured, consistent reads. ⸻ Are you still using AI for summaries? Try this vertical method on your next batch of interviews—and tell me how it goes. 👇 Drop your favorite prompt so we can learn from each othr.

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

When you’ve been a patient inside the healthcare system you work in, you start noticing the little things: the silence after a monitor alarm, the hallway conversation you’re not sure was meant for you, the well-meaning “we’ll know more soon." The list goes on. I’ve experienced world-class medicine across the country all thanks to my heart transplant. But the system isn’t only a collection of procedures. It’s also a network of people and pauses. One missed follow-up call or one delay that no one explains? These become mountains when you’re the one in the bed. Yes, design is about technology and efficient throughput, but it's also about how a system feels when you’re scared. When I returned to medicine as a physician, those 'patient experience' memories followed me into every patient encounter. They changed how I communicate, lead, & potentially help design future systems. Good healthcare solves problems. But in my opinion, great healthcare prevents people from feeling like one. If we design for that moment between uncertainty and trust, we design for the kind of system we all want to work in. #womeninmedicine #patientdoctor #doctor