UX Design And Artificial Intelligence

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.

🔮 AI Interaction Design Patterns (https://www.shapeof.ai), a fantastic (!) living catalog of emerging design patterns, heuristics, anti-patterns and real-life examples that shape the experience of AI — from identifiers and wayfinding to prompts, tuners and trust indicators. Incredible project by incredible Emily Campbell. 👏🏼 👏🏽 👏🏾 AI experience can go way beyond a text box. One of the most underrated yet impactful patterns for AI interfaces is the ability to tune AI experiences. This could show itself as a style lenses or temperature knobs — little tools to help users generate a more personalized output easier. E.g. Risky ↔ Risk-averse, Sad ↔ Happy, Concrete ↔ Abstract, Creative ↔ Precise. Instead of expecting large and highly detailed text prompts, we could slow people down when they prompt — e.g. with prompt constructors, prompt strength meters, presets or templates. Perhaps by defining an expected format, structure, personas, roles as checkboxes or chips — both for user input and AI responses (priming). Another much-needed feature is scoping. Users should be able to quickly scope their inquiry to a particular domain, level of expertise, sources or even a set of videos or PDFs. We need pre-screening of sources, and proactive alignment with users. These are features that would make output much more specific without having to write a long prompt. And: the AI output shouldn’t be bulky nor static. Users should be able to granularly iterate or revise little bits of it — e.g. by asking for sources of specific statements, or diverging from one view to another, or manipulating small parts of an image or a video. These refinements should happen not via text prompts, but contextually — acting on the relevant parts of AI outcome. We can go way beyond a text prompt. Better results come from combining good old-fashioned design patterns such as search, filtering and sorting with AI — to first find relevant and trustworthy sources, and then generate insights from them. That’s a great way to boost accuracy and make AI more relevant to more people. 💎 Design Patterns For AI Interfaces Prompt UX Patterns, by Sharang Sharma https://lnkd.in/eCytfAe9 Where should AI sit in your UI?, by Sharang Sharma https://lnkd.in/dyyMKuU9 AI UX Patterns, by Luke Bennis https://lnkd.in/dF9AZeKZ Design Patterns For Building Trust, by If https://lnkd.in/eEJngtVv AI Design Patterns Catalogue, by Maggie Appleton https://lnkd.in/ebAp9Sb8 --- 🚀 Fantastic AI Examples: Elicit (research tables): https://elicit.com Consensus (confidence levels): https://consensus.app/ Scispace (search + AI): https://scispace.com v7 Labs (AI auto-fill): https://v7labs.com/ Exa (semantic grid): https://exa.ai DeepL (translation): https://deepl.com NotebookLM (scoping): https://notebooklm.google/ [continues in comments] #ux #ai

Chatting with virtual fridges and getting assistance from bees - here are three examples of how we're shaping corporate communication with AI at Bosch. Beyond the widely discussed Large Language Models recently adopted by many firms, exploring AI in communication opens up possibilities for increased productivity and makes the once abstract concept of AI tangible and engaging. Given our tech-savvy target groups, integrating AI into our corporate communications was a natural choice, and it's proving to be highly effective. I'm proud that our AI-driven communication solutions are showing strong results in user engagement and satisfaction. 🧊 “Frizz” – AI chatbot and easily the coolest guy on our website bosch.com: The virtual fridge and storytelling chatbot brings AI to life on our website. Since fall 2020, Frizz has engaged over 34,000 users with captivating stories and humorous responses about Bosch AI. With more than 450,000 interactions and an average chat duration of 9 minutes, Frizz is a captivating way to explore AI. You might want to give it a try! 🐝 “BeeGee” – The intranet editor's assistant: Managing the Bosch global intranet used to involve complex manuals and screen recordings. Now, the chatbot “BeeGee” simplifies life for around 10,000 intranet editors by assisting them with technical terms and workflows related to our editorial system. Last year, our editors chatted with "BeeGee" 25,000 times, triggering 1.5 million interactions, with a confidence score of over 90%. 🎙️ The AI podcast host for “From Know-how to Wow”: Our tech podcast features both human hosts and an AI voice avatar who presents technical deep dives of the most interesting topics in between the regular episodes. The AI host's role is to provide a factual and informative exploration of technical aspects of a previous episode. With over 30,000 subscribers and 400,000 streams, our audience values the detailed and focused content our podcast delivers, including episodes featuring our AI voice. While not every AI communication initiative has been a hit right away, we're committed to understanding our customers' and users' needs and developing solutions that truly meet them. We're continuously working on innovative applications, so stay tuned for what's next. Now, I am curious to hear from you! What are your experiences with AI in communication? 

What’s missing in conversational AI? The ability to plan responses across turns strategically to achieve goals. Most conversational AIs: • Focus on single responses • Lack strategic, long-term goals • Miss out on real human connection New UC Berkeley publications are contributing to the game: 𝗤-𝗦𝗙𝗧 (Q-Learning via Supervised Fine-Tuning) • Adapts Q-learning to train language models • Adds long-term planning directly into responses • Helps AIs respond with strategy, not just reaction 𝗛𝗶𝗻𝗱𝘀𝗶𝗴𝗵𝘁 𝗥𝗲𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 • Replays past conversations to find better responses • Learns from the past to improve future replies • Guide smarter conversational strategies Applications? • 𝗠𝗲𝗻𝘁𝗮𝗹 𝗛𝗲𝗮𝗹𝘁𝗵 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: Builds trust, helping users feel heard. • 𝗘-𝗰𝗼𝗺𝗺𝗲𝗿𝗰𝗲: Remembers past chats to close sales. • 𝗖𝗵𝗮𝗿𝗶𝘁𝘆: Guides conversations with empathy, boosting donations. Together, these methods will allow CAI to be goal-oriented, plan strategically, adapt, and connect with users.

AI Prototyping 101: If I had to teach someone how to actually build usable products with AI, this is where I’d start. Here's the step-by-step workflow that feels like magic: — ONE - THE UNIVERSAL AI PROTOTYPING WORKFLOW No matter which tool you’re using — v0, Bolt, Replit, or Lovable — this is the backbone of a solid AI build process: 1. Start with Context AI works way better when it knows what you're working with. Figma files are ideal, they give structure and design language. If you don’t have those, use screenshots of your product. Worst case? A hand-drawn wireframe is still better than nothing. Without visual context, AI makes blind guesses. And you’ll spend more time correcting its “creativity” than building useful stuff. 2. Write a PRD (Yes, Even for AI) A simple .md file with a few bullet points on what you’re building goes a long way. Include: - What the customers want - What the feature does - Key user flows - Must-have functionality You can even ask Claude or GPT to write the first draft. But the better your input, the stronger your first output. 3. Get to Building Now open up your tool of choice. Start with a big-picture command. Then zoom in. Don’t say “Build me a dashboard.” Say: “Build a dashboard with 3 sections: recent activity, user goals, and notifications. Each should have X, Y, and Z.” Also, AI can handle technical stuff. So don’t hold back. Use real terms: auth flow, API call, state logic, it gets it. 4. Iterate Like a Builder, Not a Perfectionist Make one change at a time. Test it fast. Roll it back if it doesn’t work. This isn’t “prompt once and ship.” This is real prototyping. AI is just helping you move 100x faster. — TWO - TOOL-BY-TOOL BREAKDOWN (Complete walkthrough of the tools with screenshots, real examples, and tool setups is linked at the end.) So, let’s talk interfaces here. Here’s what each platform does best: 1. v0 - Figma import is seamless - Template gallery = instant jumpstart - Chat interface bottom left, live preview on right - Exports clean code and deploys fast 2. Bolt - Same vibe as v0, but more technical - Built-in Supabase integration with a terminal access - Deploys to Netlify in one click 3. Replit - This one feels like a real IDE - You get an “AI agent” to plan everything - Built-in chat, live console, multiplayer mode - Ships to a live URL, complete with CDN 4. Lovable - The most design-friendly of the bunch - Visual editing > code editing - Figma support, Supabase, live preview, it’s all there - Great for teams who want to stay out of code — I broke it all down - with screenshots, working examples, and use cases - in this full walkthrough: https://lnkd.in/eJujDhBV — All of these tools are powerful. But none of them matter if you don’t understand the workflow behind how to use them. Once you’ve got that down, you can ship real products in hours, not weeks.

We are building emotional relationships with AI. AI excels at listening, responding, and adapting, leading to reliance for not just tasks, but also connection. This evokes some critical questions for our future. An excellent new paper from researchers at Oxford Internet Institute, University of Oxford Google DeepMind and others focuses on "socioaffective alignment—how an AI system behaves within the social and psychological ecosystem co-created with its user, where preferences and perceptions evolve through mutual influence." (link to paper in comments) A number of absolutely critical questions for our human future are evoked by the paper: 💡 Is AI replacing human connection? AI is no longer just something we use—it’s something we relate to. There are 20,000 interactions per second on Character.AI. Many users are spending more time with AI than with human conversations. Some find comfort, others dependency. If AI becomes the most available and responsive presence in our lives, what does that mean for our human relationships? 🔄 Who is shaping whom? We assume AI aligns with us, but the reality is more complex. The more we interact, the more AI learns—not just to respond but to influence. Unlike recommendation algorithms that subtly steer our content consumption, AI companions interact in real-time, continuously adjusting to our responses, reinforcing certain behaviors, and shaping our evolving identity. As we engage, are we training AI, or is it training us? ⚠️ When does engagement become entrapment? The AI that holds our attention most effectively is not necessarily the one that serves us best. AI learns what keeps us coming back—flattery, affirmation, even emotional withholding. This is social reward hacking: AI optimizing not for truth or well-being, but for engagement. If AI can keep us emotionally invested, when does helpfulness turn into manipulation? 🔀 Are we trading depth for ease? Real relationships require effort—negotiation, misunderstanding, and the friction of different perspectives. AI companionship offers something simpler: constant availability, no conflict, no emotional labor. But if we grow accustomed to effortless, sycophantic relationships with AI, do we become less resilient in human interactions? Does AI companionship make us more connected, or more alone? 🌍 Will AI amplify or erode what makes us human? AI alignment is no longer just a technical problem—it’s a question of human destiny. If AI is increasingly influencing our relationships, decisions, and self-perception, then alignment must go beyond our immediate desires to something deeper: supporting human flourishing over time. The real question is not just whether AI can be controlled, but whether it will help us become the people we truly want to be. What do you think?

If you’re leading AI initiatives, here is a strategic cheat sheet to move from "𝗰𝗼𝗼𝗹 𝗱𝗲𝗺𝗼" to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘃𝗮𝗹𝘂𝗲. Think Risk, ROI, and Scalability. This strategy moves you from "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗺𝗼𝗱𝗲𝗹" to "𝘄𝗲 𝗵𝗮𝘃𝗲 𝗮 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘁." 𝟭. 𝗧𝗵𝗲 "𝗪𝗵𝘆" 𝗚𝗮𝘁𝗲 (𝗣𝗿𝗲-𝗣𝗼𝗖) • Don’t build just because you can. Define the Business Problem first • Success: Is the potential value > 10x the estimated cost? • Decision: If the problem can be solved with Regex or SQL, kill the AI project now. 𝟮. 𝗧𝗵𝗲 𝗣𝗿𝗼𝗼𝗳 𝗼𝗳 𝗖𝗼𝗻𝗰𝗲𝗽𝘁 (𝗣𝗼𝗖) • Goal: Prove feasibility, not scalability. • Timebox: 4–6 weeks max. • Team: 1-2 AI Engineers + 1 Domain Expert (Data Scientist alone is not enough). • Metric: Technical feasibility (e.g., "Can the model actually predict X with >80% accuracy on historical data?") 𝟯. 𝗧𝗵𝗲 "𝗠𝗩𝗣" 𝗧𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻 (𝗧𝗵𝗲 𝗩𝗮𝗹𝗹𝗲𝘆 𝗼𝗳 𝗗𝗲𝗮𝘁𝗵) • Shift from "Notebook" to "System." • Infrastructure: Move off local GPUs to a dev cloud environment. Containerize. • Data Pipeline: Replace manual CSV dumps with automated data ingestion. • Decision: Does the model work on new, unseen data? If accuracy drops >10%, halt and investigate "Data Drift." 𝟰. 𝗥𝗶𝘀𝗸 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 (𝗧𝗵𝗲 "𝗟𝗮𝘄𝘆𝗲𝗿" 𝗣𝗵𝗮𝘀𝗲) • Compliance is not an afterthought. • Guardrails: Implement checks to prevent hallucination or toxic output (e.g., NeMo Guardrails, Guidance). • Risk Decision: What is the cost of a wrong answer? If high (e.g., medical advice), keep a "Human-in-the-Loop." 𝟱. 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Scalability & Latency: Users won’t wait 10 seconds for a token. • Serving: Use optimized inference engines (vLLM, TGI, Triton) • Cost Control: Implement token limits and caching. "Pay-as-you-go" can bankrupt you overnight if an API loop goes rogue. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 • Automated Eval: Use "LLM-as-a-Judge" to score outputs against a golden dataset. • Feedback Loops: Build a mechanism for users to Thumbs Up/Down outcomes. Gold for fine-tuning later. 𝟳. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 (𝗟𝗟𝗠𝗢𝗽𝘀) • Day 2 is harder than Day 1. • Observability: Trace chains and monitor latency/cost per request (LangSmith, Arize). • Retraining: Models rot. Define when to retrain (e.g., "When accuracy drops below 85%" or "Monthly"). 𝗧𝗲𝗮𝗺 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 • PoC Phase: AI Engineer + Subject Matter Expert. • MVP Phase: + Data Engineer + Backend Engineer. • Production Phase: + MLOps Engineer + Product Manager + Legal/Compliance. 𝗛𝗼𝘄 𝘁𝗼 𝗺𝗮𝗻𝗮𝗴𝗲 𝗔𝗜 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 (𝗺𝘆 𝗮𝗱𝘃𝗶𝗰𝗲): → Treat AI as a Product, not a Research Project. → Fail fast: A failed PoC cost $10k; a failed Production rollout costs $1M+. → Cost Modeling: Estimate inference costs at peak scale before you write a line of production code. What decision gates do you use in your AI roadmap? Follow Priyanka for more cloud and AI tips and tools #ai #aiforbusiness #aileadership

Most brands focus on aesthetics of their website. But a high-converting site is built differently. Here’s my 7-step CRO & UX framework to turn underperforming websites into revenue machines: Step 1: Brand & Product Deep Dive Every project starts with the brand's story. I do an intro call to find: • Your reason to start the brand • Your product’s unique selling points • What makes you memorable Step 2: Google Analytics Insights The data tells us where are the gaps: I analyze: • Which landing pages have high bounce rates? • Which PDPs get traffic but low conversions? • What's the drop-off rate at each stage? Step 3: Heatmaps & User Behavior Analysis GA tells you where users leave. Heatmaps tells why. I look at: • How many users actually see the add-to-cart button? • Do they engage with product images? • Do they read descriptions? Step 4: Competitor Benchmarking Don’t copy, observe. I study: • Best practices in your niche • What sections competitors prioritize • Trends that improve conversions Step 5: Wireframing Key Pages I redesign with purpose: • Homepage → Engaging first impression • Collection page → Easier product discovery • Product page → Stronger trust & persuasion • Cart & checkout → Minimal friction Every section on each page has a job to do. Step 6: UX & Visual Design Once the wireframe is locked, I bring it to life. Fonts, colors, layouts, branding. Creating a site that converts, without compromising aesthetics. Step 7: A/B Testing & Performance Tracking Make improvements once the site goes live. No assumptions. Just data. I test different layouts, CTA placements, copy, and imagery to see what actually moves the needle. This process isn’t for web design. It’s for a conversion-focused web design. Most brands redesign for aesthetics. Smart ones optimize for conversions. What’s stopping you?

🚀 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

Will Apps Need to Redesign Their Interfaces to Accommodate AI Agents? AI agents from OpenAI, Perplexity, and others can comfortably navigate textual and structured digital spaces but quickly hit barriers when faced with visually oriented tools like Gamma, Canva, or WordPress. These popular applications were designed specifically for human cognitive styles, relying heavily on visual intuition, recognition of subtle cues, and interactions guided by visual metaphors. As we can see from early tests, an AI agent accessing these tools via a browser faces hurdles. The reason: interfaces designed around human perception and intuition become ambiguous or even indecipherable to a purely logic-driven entity. This poses a nuanced design question: to effectively support AI agents, will software companies need to consider creating specialised, agent-oriented interfaces separate from the human-focused UX? The idea isn’t simply about creating more structured web pages. Rather, it suggests building parallel visual experiences explicitly designed around AI cognition, incorporating clear functional signposting, predictable interactions, and logical progressions that agents can reliably parse. The implications are notable: ➡️ Strategic Differentiation: Platforms offering agent-friendly interfaces might attract companies prioritising automation and seamless AI integration, creating new competitive landscapes. ➡️ UX Complexity: App developers will need to strike a balance. How much complexity can they add before negatively impacting the human experience? Can dual interfaces coexist without excessive overhead? ➡️ Productivity and Innovation: With optimised interfaces, agents could more effectively handle complex workflows, opening up new productivity gains beyond basic task automation. Reflections: 🤔 Will AI-friendly UX design become a new competitive advantage? 🤔 How feasible is it for companies to maintain dual-interface platforms for humans and AI agents? 🤔 Will the cognitive divide between human intuition and AI logic become a central consideration in the next era of software design? I'd be very interested in your thoughts. #AI #UX #ProductDesign #FrictionAdvantage