UX Design And Artificial Intelligence

🚀 I Stopped Designing Alone. I Started Designing With AI. And honestly? It changed my entire UX process. Over the past few months, I’ve been integrating AI Figma plugins directly into my real-world client projects,not as shortcuts, but as thinking partners. Here’s how I actually use them in real projects 👇 1. UX Pilot: My Rapid Prototyping Engine When I receive a PRD or rough client requirements, I don’t jump straight into polished UI. I prompt UX Pilot to: • Generate quick wireframes • Create possible user flows • Explore multiple layout structures This helps me validate direction in hours instead of days. I never ship AI output directly, I refine it with business logic and user behavior insights. 2. Clueify: My Pre-User-Test Check Before showing designs to stakeholders, I run an AI usability audit. It helps me analyze: • Visual hierarchy • CTA focus • Cognitive overload • Attention flow It’s like doing a “silent usability test” before real users ever see it. 3. Stark: Accessibility Is Not Optional Real-world products serve real people. I use Stark to: • Check contrast ratios • Simulate visual impairments • Ensure WCAG compliance Accessibility isn’t a feature. It’s responsibility. 4. Octopus.do: I Structure Before Screens In large projects (especially SaaS dashboards), structure matters more than UI. Before designing anything, I: • Map the entire sitemap • Validate navigation depth • Align user journeys Because messy structure = messy experience. 5. Magician: Fast Ideation Mode When brainstorming: • Placeholder content • Icon ideas • Micro-interactions • Empty states Magician speeds up exploration so I can focus on strategy. 6. MagiCopy: UX Writing That Converts Good UI means nothing without clear communication. I use it to: • Generate button variations • Test tone (friendly vs professional) • Improve clarity Then I humanize it with brand voice. 7. Uizard: From Sketch to Prototype Sometimes clients send hand-drawn ideas. Instead of rebuilding from scratch: I convert sketches → editable wireframes → interactive prototypes. Faster iteration. Faster validation. 💡 My Personal Approach AI doesn’t replace UX thinking. It accelerates it. In real projects, I follow this rule: - AI for speed. - Human for strategy. - Users for validation. The result? • Faster delivery • Better alignment with stakeholders • More time spent on problem-solving • Less time on repetitive tasks And most importantly, better user experiences. If you’re a designer still afraid AI will replace you… It won’t. But designers who use AI effectively? They will replace those who don’t. Let’s build smarter. 💜 Whats your way of design? Comment below👇 UX Pilot AI Clueify #UXDesign #UIDesign #Figma #AIinDesign #ProductDesign #UXResearch #DesignProcess #Accessibility #SaaSDesign #UserExperience #DesignThinking #Prototyping #UXWriting #FutureOfDesign #designtools #uiux

One of the areas that excites me the most about AI is prototyping. I'm constantly trying out new tools so that I can share my experience. And I think what Figma has achieved with Figma Make is very impressive. But to achieve great results, you need to know when and how to use it. Figma Make excels at the following: - Prototyping complex interactions. - High accuracy when translating a design to code. - Coming up with ideas based on an existing design. I’ve used other vibe coding tools to go from idea to product as quickly as possible, without a starting design. But when it comes to high accuracy in design and prototyping complex interactions that would have taken ages with traditional prototyping, Figma Make can be incredible. Here are a few examples of where I use Figma Make instead of traditional prototyping: - Creating interactive components. - Complex interactions for web apps. - Advanced logic or data-heavy products. - Trying out different responsive approaches. - Anything that requires external libraries, such as data visualization. Nowadays, when I want to communicate an interaction idea to an engineer, I first try and do it in Figma Make. After testing it a few times, it becomes second nature. 1. Think of an interaction you want to prototype. 2. Send your design to Figma Make. 3. Describe and build. 4. Duplicate and try alternatives. In this carousel, I'll be taking you through my workflow and examples in detail. (Swipe to get started 👉) -- If you found this useful, consider reposting ♻️ Are you using AI prototyping in your workflow? And when? Let me know in the comments 👇
 #productdesign #uxdesign #ai #figmapartner

Talk less. Prototype faster. The best teams don’t discuss ideas endlessly; they just build them. But how do you get the right prototype fast enough? Most new product initiatives are not about creating a new product. They're about improving existing ones. In other words, they already have a product, customers, and a design language. The machine is slow, perhaps rusty, but it has worked for ages now. Any attempts to improve the process usually failed or gave barely any noticeable improvement. However, this is where the AI comes in and why I’m genuinely impressed with Reforge Build, which has now been launched in beta! It’s an AI prototyping tool made for product teams, not solo builders. It starts where your product already is and accelerates what comes next. Don't take my word for it, try it yourself: Check out Reforge Build and explore what’s possible with AI that actually understands your product: https://lnkd.in/duh4YC_H But why did it impress me? 1) Looks like your product Upload a screenshot or connect to Figma. Reforge Build instantly matches your real design system: colors, fonts, spacing, everything. No endless cleanup. No imagination is needed when painting a vision of a future successful product to the stakeholders. 2) Understanding the context Add your product data, strategy docs, and customer insights. Build the prototypes using your actual tiers, features, and messaging. This won't be just a rough draft, but something your actual design team could have presented to you after weeks of work. 3) Plans before it generates Instead of vague prompts, you define user needs, metrics, and layout priorities. AI creates a plan before generating, so the first version is already close to your vision. After all, you need a workable prototype, not an AI slop wannabe! 4) Explores options, not just outputs This REALLY left me with my jaw on the floor: Reforge Build generates multiple design directions, compares them side by side, and mixes the best ideas. I can only imagine this is the experience of a Product Manager with multiple design teams ready to work on a single project... 5) Works like a team tool, not a solo hack Comment, remix, reuse templates, so your second iteration takes minutes, not hours. Nobody's perfect, not even your AI teammate, but every teammate gets better with proper feedback! Impressive, isn't it? Would such an AI prototype tool speed up your new feature's go-to-market time? Let me know in the comments! #productmanagement #ai #ux

🔮 AI Accessibility Design Patterns. With practical guidelines for designers to keep in made to make AI experiences more accessible and inclusive ↓ AI features are rarely accessible by default. As we rush to ship AI-powered products, most of the time AI interactions are barely usable nor accessible or inclusive. Too often with open-ended input ("ask-me-anything"), poorly structured output and plenty of slow, repetitive and inefficient tasks. Writing prompts well is hard and time-consuming. Navigating within AI-generated wall of text is difficult. Finding relevant bits in long-lasting conversations is an adventure. And tweaking queries and AI output to meet user's needs and expectations is remarkably painful. These aren’t attributes of great AI experiences. In fact, AI features have a lot of UX challenges which require intentional and deliberate UX work: 1. AI suddenly imagines things 2. AI silently assumes things 3. AI suddenly forgets things 4. AI suddenly changes its mind 5. AI says what people want to hear 6. AI often takes too long to reply 7. AI is too verbose when replying 8. Quality of AI output declines over time 9. Only amplifies averages and mistakes 10. Rarely asks for missing details or context On the other hand, the accessibility of AI products is uncharted territory. AI features typically come with a lot of accessibility challenges, and usually they aren’t addressed at all: 1. Users could use a task builder for better prompts 2. Add “Skip to chat” or “Skip to last reply” links 3. Keyboard navigation works bottom up (Shift + Tab) 4. Group interaction controls to reduce tabbing 5. As AI is busy, keep buttons enabled, show hints 6. Repetitive “busy” messages for screen reader users 7. Add navigation landmarks to navigate within AI responses 8. Highlight what's AI-generated and what isn't 9. Link references to relevant fragments, not pages 10. References should show up on tap/click, not hover. 11. Allow users to to adjust the verbosity of AI output. 12. Most charts and visuals don't have proper alt texts. In fact, "Ask-me-anything" is an incredibly poor design pattern in AI interfaces. Users can ask anything, but they never know what exactly to ask — and more specifically, how to articulate it efficiently. A task builder can help bring structure around AI input, along with higher speed and accuracy (attached). One thing to note is the "inverted navigation nightmare". Chat moves down the page, but keyboard navigation works from bottom up. And on the way to the conversation, there are always UI controls that aren’t easy to skip. Grouping all UI controls and allowing users to skip them at once would help. If you'd like to dive deeper, I can wholeheartedly recommend a series of articles by Michael Gower — https://lnkd.in/eQNCHf7M — an important yet often overlooked area that deserves attention and good UX work, but is unexplored yet.

Your AI-generated code is probably excluding many people. "a11y" is shorthand for accessibility — building digital products that anyone can use, including people with visual, motor, cognitive, or hearing disabilities. Over 1 billion people worldwide. But lots of existing websites aren't taking them into consideration. In 2025, WebAIM found that 94.8% of the top one million home pages have detectable accessibility failures. Sadly, AI does not fix this. Because AI coding tools learn from existing code on the web. And 95% of that code is already inaccessible. The models are reproducing a broken baseline. A 2025 study from Carnegie Mellon found three problems when developers use AI coding assistants: → AI doesn't give you accessible code by default (if you don't ask, AI won't prioritize it) → AI omits many important a11y attributes → AI doesn't verify compliance. Many a11y flows have to be verified at runtime The result is missing keyboard navigation, broken focus management, ARIA attributes sprinkled in for show but wired up wrong — which is actually worse than no ARIA at all. This isn't about AI being bad. It's about a knowledge gap that AI inherits rather than solves. As AI generates more of our frontend code, inaccessible patterns are scaling faster than ever. Every vibe-coded app shipped without accessibility review is another site that excludes people. If you're building for the web, start with these basics: → Use semantic HTML. A button should be a <button>, not a styled div. → Test with your keyboard. Tab through your page. Can you reach everything? → Use headless UI components like Radix, Ariakit, Base UI, etc., they have a11y features built in. → Run a11y checkers like axe DevTools or WAVE. They catch the low-hanging fruit in seconds. → Don't trust AI output blindly. Review it specifically for accessibility. Accessibility isn't charity, it's quality engineering. It should not be an afterthought.

The Complete Guide to Building with Google AI Studio Google launched an AI prototyping tool and I decided to provide a hands-on, step-by-step guide to prototyping with it —from your first chatbot to production-ready multimodal apps that combine text, images, video, voice, and real-time data. Whether you’re prototyping ideas, a founder testing concepts, or a developer exploring rapid engineering, this guide will show you how to leverage Google’s AI stack without writing a single line of code (unless you want to). I think this works well when you: - You want to prototype multimodal apps (text + image + video + voice) - Need location-aware or grounded apps (Maps/Search) - You’re experimenting on a budget - You’re a PM/non-technical founder testing ideas. Lowest learning curve with highest ceiling. You can export to code when ready. - You want one-click deployment for demos Cloud Run deployment is frictionless. Competitors require more setup. - You need 1M token context for complex projects 📌 my guide: https://lnkd.in/gx6wFT4b

Anyone can become a designer with AI. But how do you avoid designing slop? I got a masterclass from the man behind the newsletter Designing with AI, Xinran Ma. 🎬 Watch Now: https://lnkd.in/gjfmqJn6 Available Everywhere: Spotify: https://lnkd.in/eyt7agKj Apple: https://lnkd.in/eVZf64gB ✍️ Some of my favorite takeaways: 1. AI Design Is More Than Prompts Designing with AI covers five areas: prompting, ideation, design/prototyping, workflows, and staying conscious. Most people stop at prompts. That's just 20% of the skill. The rest is understanding systems, constraints, and behaviors. 2. Match Tools To Use Cases Custom GPT → effective prompts Lovable → high-quality prototypes Magic Patterns → design variations Google AI Studio → free exploration Cursor → full-stack experiences Claude Code → all-purpose 3. Good Design Passes Four Layers Visual representation, problem-solving, design principles, and implementation feasibility. Most people stop at layer one. They see something pretty and think they're done. Great design works at all four layers. 4. Context Matters More Than Prompt Length Include who the users are, what problem you're solving, what constraints matter, and where this fits in the product. More context equals better outputs. Don't just say "design a button." 5 Add Visual References To Prompts Text alone isn't enough. Upload 2-4 screenshots showing the aesthetic you want. These references anchor AI's output. The difference in quality is massive compared to text-only prompts. 6. Iterate Fast To Get Better Results The magic isn't in the first output. It's in the 10th iteration after you've refined and tweaked. Review, identify what's wrong, tell AI how to fix it, repeat. Speed comes from practice. 7. Always Validate With Real Users AI makes it easy to generate designs. Only users tell you if those designs actually help. Talk to users. Watch them use your prototypes. Listen to their frustrations. Don't skip this step. 8. The Workflow Changed From Linear To Parallel Before AI: sketch, wireframe, design, connect screens, prototype. Sequential. Slow. After AI: describe what you want, generate proof of concept, iterate freely. Parallel. Fast. This is how top designers work now. 🏆 Thanks to our sponsors: 1. NayaOne: The fastest way to test AI and fintech solutions - https://nayaone.com/ 2. Pendo: The #1 software experience management platform - http://www.pendo.io/aakash 3. Maven: Get 15% off Xinran’s course with my link - https://bit.ly/3Y2FUZn 4. Bolt: Ship AI-powered products 10x faster - https://lnkd.in/gyy3VB7Z 5. Gamma: Turn customer feedback into product decisions with AI - https://lnkd.in/g7YNKrJY Don't miss the episode for his live workflows.

Over the past few months, I’ve noticed a pattern in our system design conversations: they increasingly orbit around audio and video, how we capture them, process them, and extract meaning from them. This isn’t just a technical curiosity. It signals a tectonic shift in interface design. For decades, our interaction models have been built on clickstreams: tapping, typing, selecting from dropdowns, navigating menus. Interfaces were essentially structured bottlenecks, forcing human intent into machine-readable clicks and keystrokes. But multimodal AI removes that bottleneck. Machines can now parse voice, gesture, gaze, or even the messy richness of a video feed. That means the “atomic unit” of interaction may be moving away from clicks and text inputs toward speech, motion, and visual context. Imagine a world where the UI is stripped to its essence: a microphone and a camera. Everything else, navigation, search, configuration, flows from natural human expression. Instead of learning the logic of software, software learns the logic of people. If this plays out, the implications are profound: UX shifts from layouts to behaviors: Designers move from arranging buttons to choreographing multimodal dialogues. Accessibility and inclusion take center stage: Voice and vision can open doors, but also risk excluding unless designed with empathy. Trust and control must be redefined: A camera-first interface is powerful, but also deeply personal. How do we make it feel safe, not invasive? We may be on the cusp of the first truly post-GUI era, where screens become less about control surfaces and more about feedback canvases, reflecting back what the system has understood from us.

A Director of UX at a SaaS company recently shared a painful calculation with me: Their team of 3 researchers spent 75% of their time on manual analysis. At an average salary of $150K, that's nearly $300K annually spent on analyzing data. But the bigger cost? Critical product decisions made without insights because "we can't wait for research." Most UX and product teams are trapped in a costly cycle of inefficiency: Conduct user interviews → Spend 30+ hours manually analyzing → Create a report → Make decisions based on gut feeling before the report is ready. After watching UX teams struggle with this for years, I've identified the core problem: research insights are treated as artifacts, not conversations. This is why we built AI Wizard into Looppanel - a conversational research companion that transforms how teams extract value from user research. Instead of static reports and manual analysis, AI Wizard allows anyone to simply ask: "What pain points did users mention about the onboarding process?" "Summarize the key recommendations users suggested for improving the checkout flow." "What were the main differences in how novice users versus power users approached this task?" You start by selecting from templates like Pain Points, Recommendations, or Summary. AI Wizard instantly analyzes your project data and engages in a natural conversation - complete with follow-up questions to dig deeper into specific areas. The way I see it, AI Wizard helps solve 3 critical problems: 1. The speed-to-decision problem Waiting weeks for analysis means missing decision windows. AI Wizard delivers TLDR overviews in seconds, not days. 2. The iteration problem No more spending time on data again because of a follow-up question. Answer unexpected stakeholder questions on the spot instead of scheduling another week of analysis 3. The tailored communication problem Automatically format the same insights for different audiences: executives get metrics, designers get details, all without rebuilding presentations. With AI Wizard, your team can: → Start conversations with templates like Pain Points, Recommendations, or Summary → Ask follow-up questions to dig deeper → Get insights from across your entire research repository in seconds → Democratize access to insights throughout your organization Will your team be leading this transformation or catching up to it? If you want to make the shift, sign up for a personalized demo here: https://bit.ly/42PEOlX

The Truth About AI and Digital Accessibility. "The AI will sound very confident. It will say, 'I have coded this website. It's completely accessible.' And then I look at it and go, 'No, it's not.'" That's Eugene Woo, CEO of Venngage, during a fascinating conversation Debra Ruh and I had on the latest AXSChat episode. Here's the hard truth: +90% of websites aren't accessible. So when AI models train on existing data, they're learning from inaccessible examples. The result? Confident hallucinations about accessibility that don't hold up under scrutiny. The real challenge isn't AI itself—it's the training data. AI is incredibly helpful for scaffolding, efficiency, and speed. But right now, when it comes to color contrast, semantic structure, and proper layering for screen readers? It still falls short. But here's the optimistic part: This is fixable. We need to: - Feed AI models with truly accessible examples - Train algorithms on accessibility standards, not just existing (flawed) websites   - Combine AI intelligence with human expertise and built-in accessibility features - Stop treating accessibility as an afterthought—it needs to be baked into the foundation The future isn't about choosing between AI or accessibility. It's about building AI with accessibility from the start. If you're building products, hiring vendors, or working in tech: Hold AI accountable. Don't accept confident-sounding claims about accessibility. Demand better training data. Support tools and platforms that prioritize accessible design from day one. The web's accessibility crisis won't be solved by AI alone—but AI trained on accessible data, combined with human judgment and vendor commitment? That's where the real transformation happens. Tune into AXSChat to hear the full discussion with Eugene on how companies are rethinking accessibility in the AI era. What's your experience been with AI-generated content and accessibility? I'd love to hear your thoughts. #Accessibility #AI #DigitalInclusion #ArtificialIntelligence #InclusiveDesign #AXSChat