UX Design And Artificial Intelligence

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data.  ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose representative tasks and see how successful users are. It won’t be right the first time, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/d6yvKS6G 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush Charts + Tables: https://lnkd.in/dnDRtG32 👍 UI Charts: https://lnkd.in/eJkyB6zS UKO: https://lnkd.in/ehvcSnuV 👍 Wireframes: https://lnkd.in/e-m3VQqs 👍 [continues in comments]

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.

I've tested over 20 AI agent frameworks in the past 2 years. Building with them, breaking them, trying to make them work in real scenarios. Here's the brutal truth: 99% of them fail when real customers show up. Most are impressive in demos but struggle with actual conversations. Then I came across Parlant in the conversational AI space. And it's genuinely different. Here's what caught my attention: 1. The Engineering behind it: 40,000 lines of optimized code backed by 30,000 lines of tests. That tells you how much real-world complexity they've actually solved. 2. It works out of the box: You get a managed conversational agent in about 3 minutes that handles conversations better than most frameworks I've tried. 3. Conversation Modeling Approach: Instead of rigid flowcharts or unreliable system prompts, they use something called "Conversation Modeling." Here's how it actually works: 1. Contextual Guidelines: ↳ Every behavior is defined as a specific guideline. ↳ Condition: "Customer wants to return an item" ↳ Action: "Get order number and item name, then help them return it" 2. Controlled Tool Usage: ↳ Tools are tied to specific guidelines ↳ No random LLM decisions about when to call APIs ↳ Your tools only run when the guideline conditions are met. 3. Utterances Feature: ↳ Checks for pre-approved response templates first ↳ Uses those templates when available ↳ Automatically fills in dynamic data (like flight info or account numbers) ↳ Only falls back to generation when no template exists What I Really Like: It scales with your needs. You can add more behavioral nuance as you grow without breaking existing functionality. What's even better? It works with ALL major LLM providers - OpenAI, Gemini, Llama 3, Anthropic, and more. For anyone building conversational AI, especially in regulated industries, this approach makes sense. Your agents can now be both conversational AND compliant. AI Agent that actually does what you tell it to do. If you’re serious about building customer support agents and tired of flaky behavior, try Parlant.

Human conversation is interactive. As others speak you are thinking about what they are saying and identifying the best thread to continue the dialogue. Current LLMs wait for their interlocutor. Getting AI to think during interaction instead of only when prompted can generate more intuitive and engaging Humans + AI interaction and collaboration. Here are some of the key ideas in the paper "Interacting with Thoughtful AI" from a team at UCLA, including some interesting prototypes. 🧠 AI that continuously thinks enhances interaction. Unlike traditional AI, which waits for user input before responding, Thoughtful AI autonomously generates, refines, and shares its thought process during interactions. This enables real-time cognitive alignment, making AI feel more proactive and collaborative rather than just reactive. 🔄 Moving from turn-based to full-duplex AI. Traditional AI follows a rigid turn-taking model: users ask a question, AI responds, then it idles. Thoughtful AI introduces a full-duplex process where AI continuously thinks alongside the user, anticipating needs and evolving its responses dynamically. This shift allows AI to be more adaptive and context-aware. 🚀 AI can initiate actions, not just react. Instead of waiting for prompts, Thoughtful AI has an intrinsic drive to take initiative. It can anticipate user needs, generate ideas independently, and contribute proactively—similar to a human brainstorming partner. This makes AI more useful in tasks requiring ongoing creativity and planning. 🎨 A shared cognitive space between AI and users. Rather than isolated question-answer cycles, Thoughtful AI fosters a collaborative environment where AI and users iteratively build on each other’s ideas. This can manifest as interactive thought previews, real-time updates, or AI-generated annotations in digital workspaces. 💬 Example: Conversational AI with "inner thoughts." A prototype called Inner Thoughts lets AI internally generate and evaluate potential contributions before speaking. Instead of blindly responding, it decides when to engage based on conversational relevance, making AI interactions feel more natural and meaningful. 📝 Example: Interactive AI-generated thoughts. Another project, Interactive Thoughts, allows users to see and refine AI’s reasoning in real-time before a final response is given. This approach reduces miscommunication, enhances trust, and makes AI outputs more useful by aligning them with user intent earlier in the process. 🔮 A shift in human-AI collaboration. If AI continuously thinks and shares thoughts, it may reshape how humans approach problem-solving, creativity, and decision-making. Thoughtful AI could become a cognitive partner, rather than just an information provider, changing the way people work and interact with machines. More from the edge of Humans + AI collaboration and potential coming.

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.

Predicting user behavior is key to delivering personalized experiences and increasing engagement. In mobile gaming, anticipating a player’s next move, like which game table they’ll choose, can meaningfully improve the user journey. In a recent tech blog, the data science team at Hike shares how transformer-based models can help forecast user actions with greater accuracy. The blog details the team's approach to modeling behavior in the Rush Gaming Universe. They use a transformer-based model to predict the sequence of tables a user is likely to play, based on factors like player skill and past game outcomes. The model relies on features such as game index, table index, and win/loss history, which are converted into dense vectors with positional encoding to capture the order and timing of events. This architecture enables the system to auto-regressively predict what users are likely to do next. To validate performance, the team ran an A/B test comparing this model with their existing statistical recommendation system. The transformer-based model led to a ~4% increase in Average Revenue Per User (ARPU), a meaningful lift in engagement. This case study showcases the growing power of transformer models in capturing sequential user behavior and offers practical lessons for teams working on personalized, data-driven experiences. #DataScience #MachineLearning #Analytics #Transformers #Personalization #AI #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gJR88Rnp

UX Designers, So, you've started using AI to see if you can leverage it to amplify what you can do. The answer is yes, but... If you've never been part of the (SDLC) or (PDLC). You'll get through it, but it won't be easy and not to fun at first. If you're in a well established company with a huge design system. Suddenly adding in AI might make life a real pain. It depends on how adaptive the company and others are. If you're starting something from scratch. Well, now you can do whatever you want to. This is where the fun, frustration and learning comes in. Buckle Up.. To give you an example. I've been working on something and it's almost ready for people to test. I was going through and manually testing the user flows. As something was found. Claude inside of Cursor would find the issue after I point it out. It suggests a fix. I review and approve and continue from there. This was taking a lot of time as you might imagine. So, this morning at 2am with what felt like sand in my eyes. "There has to be a way I can automate this..?" Prompt: As you know. I've been testing the user flows manually, and we've been fixing the issues along the way. Do you know if a way that we can automate this without having to send out various emails, and just do this internally? When you find an issue it gets documented in a backlog and we then work those, and run the test again? I got answers. I selected one I liked (playwright) and combined it with ReactFlow so it was visual. Created a dashboard for it. Long story short. I can now run 100% automated user flow tests, see them in action in real-time, see where the issues are and then go fix them. All done in less than 6 hours and at $0 except for my time. So, can you build something like this with the help of AI? Yes, I did and it fully works. #ux #uxdesigner #uxdesign

Smart CRM Basics Predictive Customer Behavior Modeling The Advantages of Predictive Behavior Modeling When Marketers can target specific customers with a specific marketing action – you are likely to have the most desirable campaign impact. Every marketing campaign and retention tactic will be more successful. The ROI of upsell, cross-sell, and retention campaigns will be more significant. For example, imagine being able to predict which customers will churn and the particular marketing actions that will cause them to remain long-term customers. Customers will feel the greater relevance of the company’s communications with them – resulting in greater satisfaction, brand loyalty, and word-of-mouth referrals. Enhancing Customer Segmentation for Personalization Predictive analytics refines customer segmentation by identifying patterns within data. By understanding customer segments on a deeper level, businesses can personalize their interactions, marketing messages, and product recommendations. This tailored approach fosters a stronger connection with customers, leading to increased loyalty. Anticipating Customer Needs Through Lead Scoring Lead scoring becomes more accurate with the integration of predictive analytics. By evaluating customer data, such as interactions with emails, website visits, and social media engagement, businesses can prioritize leads based on their likelihood to convert. This ensures that sales teams focus their efforts on leads with the highest potential. Optimizing Sales Forecasting Accurate sales forecasting is crucial for effective resource allocation and business planning. Predictive analytics in CRM analyzes past sales data, market trends, and customer behaviors to generate more accurate sales forecasts. This empowers businesses to make informed decisions, allocate resources efficiently, and capitalize on emerging opportunities. Transforming CRM with Predictive Analytics Predictive analytics is revolutionizing CRM by providing invaluable insights into customer behaviors. From personalized marketing campaigns to proactive churn prevention, businesses can leverage these predictions to enhance customer relationships and drive growth. As technology continues to advance, integrating predictive analytics into CRM systems is not just a strategy for staying competitive; it's a key component in building lasting customer-centric businesses in the digital age. #PredictiveAnalytics #CRMInsights #CustomerBehavior #DataDrivenDecisions #BusinessIntelligence #CustomerRetention #SalesForecasting #MarketingStrategy #EthicalCRM #DynamicPricing

How do materials fail, and how can we design stronger, tougher, and more resilient ones? Published in #PNAS, our physics-aware AI model integrates advanced reasoning, rational thinking, and strategic planning capabilities models with the ability to write and execute code, perform atomistic simulations to solicit new physics data from “first principles”, and conduct visual analysis of graphed results and molecular mechanisms. By employing a multiagent strategy, these capabilities are combined into an intelligent system designed to solve complex scientific analysis and design tasks, as applied here to alloy design and discovery. This is significant because our model overcomes the limitations of traditional data-driven approaches by integrating diverse AI capabilities—reasoning, simulations, and multimodal analysis—into a collaborative system, enabling autonomous, adaptive, and efficient solutions to complex, multiobjective materials design problems that were previously slow, expert-dependent, and domain-specific. Wonderful work by my postdoc Alireza Ghafarollahi! Background: The design of new alloys is a multiscale problem that requires a holistic approach that involves retrieving relevant knowledge, applying advanced computational methods, conducting experimental validations, and analyzing the results, a process that is typically slow and reserved for human experts. Machine learning can help accelerate this process, for instance, through the use of deep surrogate models that connect structural and chemical features to material properties, or vice versa. However, existing data-driven models often target specific material objectives, offering limited flexibility to integrate out-of-domain knowledge and cannot adapt to new, unforeseen challenges. Our model overcomes these limitations by leveraging the distinct capabilities of multiple AI agents that collaborate autonomously within a dynamic environment to solve complex materials design tasks. The proposed physics-aware generative AI platform, AtomAgents, synergizes the intelligence of LLMs and the dynamic collaboration among AI agents with expertise in various domains, incl. knowledge retrieval, multimodal data integration, physics-based simulations, and comprehensive results analysis across modalities. The concerted effort of the multiagent system allows for addressing complex materials design problems, as demonstrated by examples that include autonomously designing metallic alloys with enhanced properties compared to their pure counterparts. We demonstrate accurate prediction of key characteristics across alloys and highlight the crucial role of solid solution alloying to steer the development of alloys. Paper: https://lnkd.in/enusweMf Code: https://lnkd.in/eWv2eKwS MIT Schwarzman College of Computing MIT Civil and Environmental Engineering MIT Department of Mechanical Engineering (MechE) MIT Industrial Liaison Program MIT School of Engineering

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?