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?
Understanding User Experience
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Head of Product Design @ Voy, Hands-on Product Design Leader, AI & Healthcare, Builder
54,540 followersData is everything in product design. Without data, we open ourselves up to: - Biases - Opinions - Confusion - Misalignment When we are data-informed and that data is accurate, we can truly make educated product decisions. I like to think of data in two layers: a) What’s happening and b) Why it’s happening. Let’s break it down. What’s happening: - Business data tells us how the business is doing - Marketing/sales data tells us where our customers come from - Retention data tells us when and why customers are leaving us - Engagement data tells us how customers are using our product Why it’s happening: - User research gives us rich insight into why something is happening - Voice of the customer data shows us how customers talk about our product - Usability scores show us how people perceive our product or feature experience in a measurable way - Product market fit & satisfaction scores give us a simple and actionable metric to track and improve over time In terms of accessing that data, methodologies vary, but generally speaking, I always advise the following: 1. Get access to growth and retention data through business dashboards. 2. Get access to product data through your product analytics tool. 3. Set up a cadence to gather customer reviews & comments, either manually or via automated tools. 4. Set up a cadence to speak to your users continuously to answer the why. 5. Set up a recurring survey to track satisfaction and usability. If you don’t have the data structure for any of the above, speak to your product and data team to see if you can change that. If not, rely on the data that you can actually get. PS: The list of metrics is indicative: Actual metrics will differ greatly from one company to another and largely depend on the industry, niche, as well as your data infrastructure and setup. — If you found this useful, consider reposting â™»ï¸ How are you collecting and using data in your design process? What else are you tracking?
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Practical insights for better UX • Running “Measure UX†and “Design Patterns For AI†• Founder of SmashingMag • Speaker • Loves writing, checklists and running workshops on UX. ðŸ£
227,385 followers🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike†AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👠https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👠https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👠https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design
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AI is killing the UX Design role as we know it. Designers who adapt will evolve into Strategic Experience Architects who will be in high demand. While traditional designers are "pixel-pushing," a new set of designers is emerging. They're using AI to fast-track design ideas and turning prototypes into working code. A lot of what UX designers are doing manually today is exactly what AI tools are getting good at: • Rapid wireframing concepts • UI component creation • Basic user research • Persona development • Usability testing automation The ability to automate some UX tasks is already here. We have to assume that the technology will only advance quickly. I recently spoke with several Product Managers who are already replacing basic UX tasks with AI tools. When PMs can generate, iterate, and validate designs using AI, what happens to the traditional UX role? Simple products and startups will streamline. PMs with AI will be able to handle the basics. We're already seeing this shift. However, there's a big opportunity here as well. AI has a critical blind spot: it can't grasp the nuanced psychology of human behavior. It can't navigate complex stakeholder dynamics. It can't translate business objectives into meaningful user experiences. This is where the evolution happens. The future belongs to Strategic Experience Architects who: ✦ Define the right problems to solve ✦ Extract insights from human complexity ✦ Align teams around user value ✦ Guide AI with human context The market is splitting: → Basic products: UX roles blend into other roles on the team → Complex enterprises: Strategic UX roles become critical Fortunately, most valuable products are complex and human-centered. Want to stay relevant? Here's what to consider. 1. Master AI design tools   But don't just use them, learn to orchestrate them 2. Evolve from maker to strategist   Your value is in thinking, not in pushing pixels (AI will eventually handle this) 3. Develop business intelligence   Connect user needs to revenue 4. Study human psychology   This is your moat against AI 5. Learn systems thinking Focus on developing repeatable systems in your daily work The UX industry isn't dead, but it is transforming. -- â™»ï¸ Share if you think this will help others âž• Follow Jason Moccia for more insights on AI and Product Design
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CEO @ Crazy Egg (est. 2005), building tools teams use to make marketing decisions.
44,387 followersI just got off the phone with a founder. It was an early Sunday morning call, and they were distraught. The company had launched with a breakout AI feature. That one worked. It delivered. But every new release since then? Nothing’s sticking. The team is moving fast. They’re adding features. The roadmap looks full. But adoption is flat. Internal momentum is fading. Users are trying things once, then never again. No one’s saying it out loud, but the trust is gone. This is how AI features fail. Because they teach the user a quiet lesson: don’t rely on this. The damage isn’t logged. It’s not visible in dashboards. But it shows up everywhere. In how slowly people engage. In how quickly they stop. In how support teams start hedging every answer with “It should work.†Once belief slips, no amount of capability wins it back. What makes this worse is how often teams move on. A new demo. A new integration. A new pitch. But the scar tissue remains. Users carry it forward. They stop expecting the product to help them. And eventually, they stop expecting anything at all. This is the hidden cost of broken AI. Beyond failing to deliver, it inevitably also subtracts confidence. And that subtraction compounds. You’re shaping expectation, whether you know it or not. Every moment it works, belief grows. Every moment it doesn’t, belief drains out. That’s the real game. The teams that win build trust. They ship carefully. They instrument for confidence. They treat the user’s first interaction like a reputation test, because it is. And they fix the smallest failures fast. Because even one broken output can define the entire relationship. Here’s the upside: very few teams are doing this. Most are still chasing the next “AI-powered†moment. They’re selling potential instead of building reliability. If you get this right, you become the product people defend in meetings. You become the platform they route their workflow through. You become hard to replace. Trust compounds. And when it does, it turns belief into lock-in.
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At airtel, I ran an iPhone giveaway marketing campaign three times, and to my surprise, none of them performed. Logically, you’d think offering a prize as attractive as an iPhone, especially during the launch period, would drive massive engagement. The assumption is that everyone would rush to participate, download your app, engage with the campaign, and complete the required actions. But what actually happened was the opposite. Engagement was shockingly, embarrassingly low. In contrast, campaigns that offered much smaller rewards—like a ₹1,000 or ₹500 voucher, or even just a free mobile recharge—generated higher participation rates, more app downloads, and greater overall engagement. But why does this happen? This outcome can be largely attributed to consumer psychology. When the reward seems too large or unattainable, people instinctively doubt their chances of winning. The concept of *perceived probability* comes into play here. When the prize is something as high-value as an iPhone, people immediately think, "What are the odds that I’ll actually win?" This skepticism causes them to disengage and not even bother trying, as they don't see the reward as realistically achievable. On the other hand, smaller, more attainable rewards feel within reach. A ₹1,000 voucher or a free recharge doesn’t carry the same sense of improbability. People feel like they have a real shot at winning something smaller, which encourages them to take the necessary actions, leading to better campaign results. In essence, psychology plays a far more critical role in shaping consumer behavior than we give it credit for.
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AI Architect & Engineer | AI Strategist
725,022 followersAPI performance issues can silently erode user experience, strain resources, and ultimately impact your bottom line. I've grappled with these challenges firsthand. Here are the critical pain points I've encountered, and the solutions that turned things around: ð—¦ð—¹ð˜‚ð—´ð—´ð—¶ð˜€ð—µ ð—¥ð—²ð˜€ð—½ð—¼ð—»ð˜€ð—² ð—§ð—¶ð—ºð—²ð˜€ ð——ð—¿ð—¶ð˜ƒð—¶ð—»ð—´ ð—¨ð˜€ð—²ð—¿ð˜€ ð—”ð˜„ð—®ð˜† ð—£ð—¿ð—¼ð—¯ð—¹ð—²ð—º: Users abandoning applications due to frustratingly slow API responses. ð—¦ð—¼ð—¹ð˜‚ð˜ð—¶ð—¼ð—»: Implementing a robust caching strategy. Redis for server-side caching and proper use of HTTP caching headers dramatically reduced response times. ð——ð—®ð˜ð—®ð—¯ð—®ð˜€ð—² ð—¤ð˜‚ð—²ð—¿ð—¶ð—²ð˜€ ð—•ð—¿ð—¶ð—»ð—´ð—¶ð—»ð—´ ð—¦ð—²ð—¿ð˜ƒð—²ð—¿ð˜€ ð˜ð—¼ ð—§ð—µð—²ð—¶ð—¿ ð—žð—»ð—²ð—²ð˜€ ð—£ð—¿ð—¼ð—¯ð—¹ð—²ð—º: Complex queries causing significant lag and occasionally crashing our servers during peak loads. ð—¦ð—¼ð—¹ð˜‚ð˜ð—¶ð—¼ð—»ð˜€: Strategic indexing on frequently queried columns Rigorous query optimization using EXPLAIN Tackling the notorious N+1 query problem, especially in ORM usage ð—•ð—®ð—»ð—±ð˜„ð—¶ð—±ð˜ð—µ ð—¢ð˜ƒð—²ð—¿ð—¹ð—¼ð—®ð—± ð—³ð—¿ð—¼ð—º ð—•ð—¹ð—¼ð—®ð˜ð—²ð—± ð—£ð—®ð˜†ð—¹ð—¼ð—®ð—±ð˜€ ð—£ð—¿ð—¼ð—¯ð—¹ð—²ð—º: Large data transfers eating up bandwidth and slowing down mobile users. ð—¦ð—¼ð—¹ð˜‚ð˜ð—¶ð—¼ð—»: Adopting more efficient serialization methods. While JSON is the go-to, MessagePack significantly reduced payload sizes without sacrificing usability. ð—”ð—£ð—œ ð—˜ð—»ð—±ð—½ð—¼ð—¶ð—»ð˜ð˜€ ð—•ð˜‚ð—°ð—¸ð—¹ð—¶ð—»ð—´ ð—¨ð—»ð—±ð—²ð—¿ ð—›ð—²ð—®ð˜ƒð˜† ð—Ÿð—¼ð—®ð—±ð˜€ ð—£ð—¿ð—¼ð—¯ð—¹ð—²ð—º: Critical endpoints becoming unresponsive during traffic spikes. ð—¦ð—¼ð—¹ð˜‚ð˜ð—¶ð—¼ð—»ð˜€: Implementing asynchronous processing for resource-intensive tasks Designing a more thoughtful pagination and filtering system to manage large datasets efficiently ð—£ð—²ð—¿ð—³ð—¼ð—¿ð—ºð—®ð—»ð—°ð—² ð—•ð—¼ð˜ð˜ð—¹ð—²ð—»ð—²ð—°ð—¸ð˜€ ð—™ð—¹ð˜†ð—¶ð—»ð—´ ð—¨ð—»ð—±ð—²ð—¿ ð˜ð—µð—² ð—¥ð—®ð—±ð—®ð—¿ ð—£ð—¿ð—¼ð—¯ð—¹ð—²ð—º: Struggling to identify and address performance issues before they impact users. ð—¦ð—¼ð—¹ð˜‚ð˜ð—¶ð—¼ð—»: Establishing a comprehensive monitoring and profiling system to catch and diagnose issues early. ð—¦ð—°ð—®ð—¹ð—®ð—¯ð—¶ð—¹ð—¶ð˜ð˜† ð—–ð—µð—®ð—¹ð—¹ð—²ð—»ð—´ð—²ð˜€ ð—®ð˜€ ð—¨ð˜€ð—²ð—¿ ð—•ð—®ð˜€ð—² ð—šð—¿ð—¼ð˜„𘀠ð—£ð—¿ð—¼ð—¯ð—¹ð—²ð—º: What worked for thousands of users started to crumble with millions. ð—¦ð—¼ð—¹ð˜‚ð˜ð—¶ð—¼ð—»ð˜€: Implementing effective load balancing Optimizing network performance with techniques like content compression Upgrading to HTTP/2 for improved multiplexing and reduced latency By addressing these pain points head-on, we can significantly improve user satisfaction and reduce operational costs. What challenges have you faced with API performance? How did you overcome them? Gif Credit - Nelson Djalo
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The most fascinating piece of research I've read this week: Anthropic's study on "disempowerment patterns" in AI—cases where AI distorts users' beliefs, values, or actions rather than informing them. Two findings stand out: 1ï¸âƒ£ Users actively seek these outcomes—asking "what should I do?" and accepting answers without pushback. The disempowerment comes not from AI overriding human agency, but from people voluntarily ceding it 🤯 2ï¸âƒ£ Users rate these harmful interactions more favorably in the moment. Satisfaction only drops after they've acted on the advice ðŸ¤¦â™€ï¸ We've seen 1ï¸âƒ£ in productivity software before. Features that empower sophisticated users can create mindless dependency in others. The difference lies in whether the tool teaches you the "why" or just handles the "what". The most empowering products build capability over time, not just provide quick hacks for grunt work. We've also seen 2ï¸âƒ£ before. For a decade, social platforms optimized for engagement that users validated through clicks and shares. Content that felt compelling in the moment—validation, tribal reinforcement—often left people worse off. The gap between what users wanted and what was good for them became a central tension. Designing AI products and experiences continues to be an incredibly creative and human endeavor... https://lnkd.in/g8K7A_4P
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I've been working on mapping the landscape of AI products. Specifically, to understand the user experience of the different "archetypes". If you were at UX London or Future Frontend in Helsinki, I shared this as a work in progress in my talks. I've also been iterating this with clients. (Hello and thank you :) You know who you are!) Now it's ready to share. The challenge is that there are SO MANY gen-AI-powered products now that it's hard to get oriented. Which means it's tough to find design inspiration, and to identify UX challenges. 🔎 I'll give a quick overview here – the detail is in my longer post, linked at the bottom. My idea was to create a landscape of AI capabilities. To tease apart all these products. See, a large language model (LLM) isn't enough... new products emerged as three different capabilities were added: 👉 Context – adding more data to the prompt so that you can steer the AI more precisely 👉 Structured output – so you can embed the AI in other systems, eventually allowing for autonomous, tool-usingagents 👉 Real-time – so you can use the AI in interactive interfaces. That gave me a triangle diagram: a map of how much different products rely on different underlying capabilities. Then I plotted dozens upon dozens of products. It turns out that these can be named as PRODUCT ARCHETYPES. There's everything from inline tools, to virtual employees, to agents-as-UI, to character chat, and more. I can group those further, teasing out four major clusters. Users relate to the AI in different ways: 1ï¸âƒ£ Tools. Users control AI to generate something. 2ï¸âƒ£ Copilots. The AI works alongside the user in an app in multiple ways. 3ï¸âƒ£ Agents. The AI has some autonomy over how it approaches a task. 4ï¸âƒ£ Chat. The user talks to the AI as a peer in real-time. And they have different design challenges! So I'm able to use this landscape of gen-AI products in a few different ways: Like, simply to understand the specific UX challenges for this product. Or... as a way to look at what other similar products are doing. It's a way to orient and look around without getting overwhelmed. Or as a way to stimulate the imagination. Say: take the AI product that we're making, and now imagine it as a live tool, now a copilot, now an agent... and so on. I've found it to be a handy workshop tool that brings clarity, or conversational prop. A REQUEST! I've found this map useful in my own work and thinking, and I'd love to get it out in the world. So please do feel free to make use of it and build on it yourself. And if you do then please link back to the post and let me know. 👇👇👇 Here's the blog post where you'll find - Tons of examples and links, to bring these archetypes to life - A map of 1st generation gen-AI products, and today's products too - Deeper explanations I hope you find it useful. LINK: https://lnkd.in/eNxaWFFz
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406,123 followers
Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.