Machine Learning in UX Design

One of the biggest challenges in UX research is understanding what users truly value. People often say one thing but behave differently when faced with actual choices. Conjoint analysis helps bridge this gap by analyzing how users make trade-offs between different features, enabling UX teams to prioritize effectively. Unlike direct surveys, conjoint analysis presents users with realistic product combinations, capturing their genuine decision-making patterns. When paired with advanced statistical and machine learning methods, this approach becomes even more powerful and predictive. Choice-based models like Hierarchical Bayes estimation reveal individual-level preferences, allowing tailored UX improvements for diverse user groups. Latent Class Analysis further segments users into distinct preference categories, helping design experiences that resonate with each segment. Advanced regression methods enhance accuracy in predicting user behavior. Mixed Logit Models recognize that different users value features uniquely, while Nested Logit Models address hierarchical decision-making, such as choosing a subscription tier before specific features. Machine learning techniques offer additional insights. Random Forests uncover hidden relationships between features - like those that matter only in combination - while Support Vector Machines classify users precisely, enabling targeted UX personalization. Bayesian approaches manage the inherent uncertainty in user choices. Bayesian Networks visually represent interconnected preferences, and Markov Chain Monte Carlo methods handle complexity, delivering more reliable forecasts. Finally, simulation techniques like Monte Carlo analysis allow UX teams to anticipate user responses to product changes or pricing strategies, reducing risk. Bootstrapping further strengthens findings by testing the stability of insights across multiple simulations. By leveraging these advanced conjoint analysis techniques, UX researchers can deeply understand user preferences and create experiences that align precisely with how users think and behave.

Your User Interfaces are becoming totally outdated with AI: 1. The old rules of UI design are fading. One-size-fits-all is out the window. There was a time when clean layouts, intuitive buttons, and predictable flows defined a great UI. That’s not enough anymore. AI has flipped the script. Interfaces are now dynamic, personalized, and proactive. Users don’t just interact with your page; your page needs to anticipate what users may need. 2. Static designs and rigid UX metrics? They’re not keeping up. We used to obsess over click-through rates, time-on-page, or task completion speed. But those don’t capture the full picture when AI-driven UIs adapt in real-time. How do you measure success of a chatbot that predicts your next question or a dashboard that reshapes itself based on your habits? Your traditional metrics probably miss the magic. 3. The new frontier: intent-driven, adaptive interfaces. Why force users to navigate when AI can meet them where they are? Think: - Contextual suggestions (e.g., auto-filling forms based on behavior) - Predictive layouts (e.g., surfacing tools before you know you need them) - Conversational UI (e.g., voice or text that feels human, not robotic) The data’s there: behavioral patterns, historical inputs, even sentiment analysis. You must use all of it to make this happen. 4. I challenge you to map your UI’s “AI maturity curve.” Take your product’s interface and plot it: (a) Static—basic and unchanging (b) Responsive—adjusts to screen or clicks (c) Personalized—tailors to user history (d) Predictive—anticipates needs (e) Autonomous—acts on behalf of the user Where are you now? Where’s the gap to the next stage? 5. What matters is delivering value before users ask for it. UI design used to be about ease and reduced friction. But now it’s about foresight. Designers and product teams need to orchestrate *everything* that shapes the experience: AI models, data pipelines, and yes, even the guardrails to keep it ethical and uncreepy. 6. Next-gen AI tools are accelerating this shift. From generative design (think AI mocking up wireframes) to natural language processing (making voice UIs seamless), the tech is here. The catch? Integration’s still messy. Tying AI outputs to real-time UI updates takes work. Experiment with small pilots, measure user delight (not just efficiency), and scale what sticks.

🤖 How To Design Better AI Experiences. With practical guidelines on how to add AI when it can help users, and avoid it when it doesn’t ↓ Many articles discuss AI capabilities, yet most of the time the issue is that these capabilities either feel like a patch for a broken experience, or they don't meet user needs at all. Good AI experiences start like every good digital product by understanding user needs first. 🚫 AI isn’t helpful if it doesn’t match existing user needs. 🤔 AI chatbots are slow, often expose underlying UX debt. ✅ First, we revisit key user journeys for key user segments. ✅ We examine slowdowns, pain points, repetition, errors. ✅ We track accuracy, failure rates, frustrations, drop-offs. ✅ We also study critical success moments that users rely on. ✅ Next, we ideate how AI features can support these needs. ↳ e.g. Estimate, Compare, Discover, Identify, Generate, Act. ✅ Bring data scientists, engineers, PMs to review/prioritize. 🤔 High accuracy > 90% is hard to achieve and rarely viable. ✅ Design input UX, output UX, refinement UX, failure UX. ✅ Add prompt presets/templates to speed up interaction. ✅ Embed new AI features into existing workflows/journeys. ✅ Pre-test if customers understand and use new features. ✅ Test accuracy + success rates for users (before/after). As designers, we often set unrealistic expectations of what AI can deliver. AI can’t magically resolve accumulated UX debt or fix broken information architecture. If anything, it visibly amplifies existing inconsistencies, fragile user flows and poor metadata. Many AI features that we envision simply can’t be built as they require near-perfect AI performance to be useful in real-world scenarios. AI can’t be as reliable as software usually should be, so most AI products don’t make it to the market. They solve the wrong problem, and do so unreliably. As a result, AI features often feel like a crutch for an utterly broken product. AI chatbots impose the burden of properly articulating intent and refining queries to end customers. And we often focus so much on AI that we almost intentionally avoid much-needed human review out of the loop. Good AI-products start by understanding user needs, and sparkling a bit of AI where it helps people — recover from errors, reduce repetition, avoid mistakes, auto-correct imported files, auto-fill data, find insights. AI features shouldn’t feel disconnected from the actual user flow. Perhaps the best AI in 2025 is “quiet” — without any sparkles or chatbots. It just sits behind a humble button or runs in the background, doing the tedious job that users had to slowly do in the past. It shines when it fixes actual problems that it has, not when it screams for attention that it doesn’t deserve. Useful resources: AI Design Patterns, by Emily Campbell https://www.shapeof.ai AI Product-Market-Fit Gap, by Arvind Narayanan, Sayash Kapoor https://lnkd.in/duEja695 [continues in comments ↓]

As AI reasoning models become more sophisticated, they're also becoming slower—deliberately taking time to process complex problems. This creates a UX challenge we haven't fully solved: How do we design interfaces that make AI thinking time productive rather than frustrating? One potential solution is to treat these windows like "supersets" in weightlifting. You do a push exercise, then immediately a pull exercise while your push muscles recover. You're always productive, just shifting focus. Applying this concept to AI interfaces: Imagine you're a lawyer using AI to review a complex 100-page contract: "Identify any unusual clauses, compliance risks, and compare terms to our standard agreements." While the AI works through this deep analysis, instead of watching a loading screen, the interface prompts you to begin preparing client-specific context notes or to outline negotiation strategy options based on different potential outcomes. The system intelligently guides you through complementary tasks matched to the processing time. When the AI completes its review, you've already completed valuable work that enhances your overall legal strategy. This "multitasking UX" approach seems better than the alternative of letting the user wait, sitting on their hands. Sure, over a long enough time horizon, this lag will eventually disappear. But in this emerging era, UX designers will increasingly need to solve for "reasoning model lag." Not by making users wait but by making waiting time productive.

I redesigned my entire UX/UI process with AI. It’s not about “use ChatGPT to brainstorm.” I mean, I rebuilt the whole pipeline. From product idea to prototype. What used to take months? Now gets done in days. Here’s what it looks like step-by-step: 1. Instant User Flows I drop rough product ideas into ChatGPT. (It's not the public one; it's a custom GPT trained on how I think.) It gives me: - Sitemap - User journey - Logic flows All in less time than it takes to make coffee. 2. Wireframes Without Drawing I stopped sketching. I describe the layout in plain English, and Magician does the rest. "Hero. CTA. Testimonials." Boom. Wireframe. No more dragging boxes like it’s 2015. 3. AI-Built Design System Spacing? Typography? Button styles? I just describe the vibe. Tools like Relume and Uizard take that and build me a full design system. This used to take WEEKS. Now it’s done before lunch. 4. Smarter Figma Time Now everything moves to Figma. But I don’t waste time pixel-pushing. AI plugins handle: - spacing - responsiveness - and accessibility. I just make the ideas click. 5. Prototyping = Auto-On Final step? Auto-connect flows with Figma’s AI tools. Clickable. Shareable. Client-ready. Dev-approved. No extra buttons. No guesswork. Here’s the real punchline: AI didn’t replace my work. It replaced the boring parts, so I can focus on design thinking. It’s not about working faster. It’s about designing smarter. We’re not in 2015 anymore. Let’s build like it’s 2030. What part of your UX workflow do you still do manually? Curious to hear.

How proactive AI will change UX - 📆 schedule ChatGPT requests! OpenAI has introduced a new task scheduling feature for ChatGPT. This means you can now ask ChatGPT to handle tasks at a future time — like sending you a weekly global news update, recommending a daily personalized workout, or setting reminders for important events. 💡 Why is this interesting from a UX perspective? This shift is a step toward proactive AI — moving from reactive systems (waiting for user input) to anticipatory, context-aware experiences that help users save mental energy and stay on top of their routines. Let’s break it down from a real-life use case - creating daily recipes: I currently eat sugar-free, gluten-free (because I am celiac), and generally low-carb and like to let ChatGPT create recipes for me. I don’t want a fixed meal plan, but I do need flexible, personalized recipe suggestions that fit my nutrition goals. Ideally, I’d want ChatGPT to  → suggest automatically 3-4 recipes daily around 3 PM → send them to me  → and based on my choice adjust future suggestions for the next days based on what I’ve already eaten that week (for balanced nutrients). With the new task feature, this kind of personalized experience could become much much more seamless. I wouldn't need to ask repeatedly — the assistant would learn my preferences over time and adapt its suggestions accordingly. 🎯 What can we learn from this in AI-UX design? 1️⃣ From static interactions to dynamic experiences: We often design AI tools that rely on users asking for something. But this update shows the value of continuous, evolving interactions. Users shouldn’t need to start from scratch every time — systems can proactively adjust to their needs and context. 2️⃣ Mental models of AI assistants: For users to trust AI routines, they need to understand what the assistant will do and when. It’s about designing predictability and transparency in a way that still allows for flexibility and spontaneity. 3️⃣ Proactive ≠ intrusive: There’s a fine balance between helpful and annoying. The best AI interactions feel like a supportive partner — offering assistance at the right time, based on context and past behavior, without overwhelming users with irrelevant notifications. In AI-UX, we’re increasingly designing for systems that adapt and evolve with the user.  This new feature is a great example of how AI can shift might be able rom a passive tool to an active assistant — can’t wait to try it. How do you see proactive AI changing the way we design user experiences? Would love to hear your thoughts! 👀

How I use AI to design the Top 1% user experience: (AI won’t replace, it’ll assist → if you know how) I tested countless AI workflows. Here’s the best one: 🧠 For Brainstorming → Use ChatGPT Prompt: "Generate 5 innovative UX ideas for a [specific product]. Consider user engagement, accessibility, cognitive load, and seamless interaction. Provide real-world examples and potential challenges for each idea." 🔍 For UX Research → Use DeepSeek Prompt: "Analyze the top pain points users face in [your industry]. Break down the psychological, behavioral, and technical challenges. Provide case studies, competitor insights, and suggestions to enhance usability." 📊 For Competitive Analysis → Use Perplexity Prompt: "Research the top-performing UX strategies in [your niche]. Analyze trends, user expectations, and key differentiators. Compare at least three successful companies, highlighting their UX strengths, weaknesses, and opportunities for improvement." 📐 For Wireframing → Use Claude Prompt: "Create a landing page that enhances UX and solves [paste problem statement]. Incorporate clear hierarchy, intuitive navigation, and mobile responsiveness. My goal is to [put your goal] and [goal 2]. Ensure accessibility compliance and smooth user flow." And this isn’t just a random AI trick. It’s built on: ✓ Years of UX expertise ✓ 100s of tested design iterations ✓ AI-assisted, human-approved strategies How to Use It: 1️⃣ Generate ideas with ChatGPT 2️⃣ Research pain points with DeepSeek 3️⃣ Analyze competitors with Perplexity 4️⃣ Wireframe instantly with Claude 5️⃣ Customize & refine for max conversion ⚠️ I'll let you in on the real secret: AI can assist, but it’s your creativity and empathy that make the experience truly exceptional. Blend this assistance into your process, and you’ll stand out effortlessly. PS. Do you use any of these AI tools for UX design? I'd appreciate you reposting this if it was helpful! Follow me for more insights like this!

AI has become a mainstream topic, but do you know how it can help you as a UX Designer? Several of the important process in your toolboxes can be time consuming and require truly understanding who the user is. And as many of you know... we don't always have that time or ability! Maybe you are working on super tight time constraints. Or maybe, you don't have access to the real user so you can't fully think through their mindset. Either way, AI can be a great tool to help with these problems! AI is able to help: 1️⃣ Provide data-driven insights Sifting through user data and insights can be super time consuming. This step is vital so it must be completed, but AI can help speed it up. This is done by processing large volumes of user data and extracting relevant information to begin the categorizing process. AI can be faster than us at this due to its programed abilities to recognize patterns. 2️⃣ Help with predictive design Similar to the recognition of patterns in the data, AI is able to analyze user behavior patterns and predict how users are likely to interact with a product or website. This can be super helpful for individuals working on a project where they don't have direct access to their user base. This way, even without the access to users, you can still try and better understand their needs, goals, and frustrations. 3️⃣ Create personalized user journeys We also need to understand the journeys of our users when it comes to our products. But once again, this is not only time consuming but also sometimes difficult to figure out depending on available resources. With AI now, we can use historical data and behavior patterns to predict personalization. The key though, is AI is not meant to replace you! It is just a helpful tool to make you more efficient!

AI is flipping UX upside down — and most designers aren’t ready. The more I reflect on how AI is reshaping our industry, the clearer it becomes: AI isn’t replacing UX. It’s replacing weak UX. We’ve spent years focusing on visuals, flows, and tools — but AI is revealing how much of our work has been surface-level. The deliverables, the documentation, the pixel-perfect mockups… AI can already replicate that. So where does that leave us? Here’s where I stand: • UX isn’t about wireframes anymore — it’s about outcomes. • Tools won’t save us. Figma won’t save us. • We have to evolve from interface designers to system-level thinkers. And yes — I’ll say it: Figma is the Titanic. Not because it’s a bad tool (it’s brilliant), but because so many designers are obsessively decorating the cabins while ignoring the iceberg right ahead — the AI shift. We’re clinging to tooling comfort while the entire industry model is changing underneath us. If your value lies only in your ability to use tools, you’re already replaceable. The future belongs to designers who can: • Think critically and strategically • Collaborate across functions • Understand real human problems • Design systems that adapt, scale, and solve This is a massive opportunity. But only if we’re willing to drop the safety nets and rethink what it really means to design. Inspired by Greg Nudelman — a must-read for anyone in UX right now. Curious — how are you adapting your UX mindset in this new age of AI? #UXDesign #DesignThinking #Figma #AIinDesign #FutureOfUX #ProductStrategy #HumanCenteredDesign #SystemsThinking

AI changes how we measure UX. We’ve been thinking and iterating on how we track user experiences with AI. In our open Glare framework, we use a mix of attitudinal, behavioral, and performance metrics. AI tools open the door to customizing metrics based on how people use each experience. I’d love to hear who else is exploring this. To measure UX in AI tools, it helps to follow the user journey and match the right metrics to each step. Here's a simple way to break it down: 1. Before using the tool Start by understanding what users expect and how confident they feel. This gives you a sense of their goals and trust levels. 2. While prompting  Track how easily users explain what they want. Look at how much effort it takes and whether the first result is useful. 3. While refining the output Measure how smoothly users improve or adjust the results. Count retries, check how well they understand the output, and watch for moments when the tool really surprises or delights them. 4. After seeing the results Check if the result is actually helpful. Time-to-value and satisfaction ratings show whether the tool delivered on its promise. 5. After the session ends See what users do next. Do they leave, return, or keep using it? This helps you understand the lasting value of the experience. We need sharper ways to measure how people use AI. Clicks can’t tell the whole story. But getting this data is not easy. What matters is whether the experience builds trust, sparks creativity, and delivers something users feel good about. These are the signals that show us if the tool is working, not just technically, but emotionally and practically. How are you thinking about this? #productdesign #uxmetrics #productdiscovery #uxresearch