Enhancing User Experience With AI-Driven Suggestions

One of the constant challenges in UI/UX design is creating websites that serve diverse user needs effectively. While development and research teams often aim for universal accessibility, end users arrive with vastly different objectives. Consider Apple's website - visitors might need MacOS update information, iPhone purchasing, technical support, laptop upgrades, or countless other Apple-related services. Yet their homepage prominently features only their latest phone model at the top. This one-size-fits-all approach, while efficient for high-traffic priorities, can now be fundamentally reimagined through AI-driven personalization. Large Language Models enable us to aggregate visitor context and dynamically generate user interfaces that adapt to individual needs in real-time. This shift from static layouts to Generative UI (GenUI) demonstrates a significant change in how we approach web experiences. To explore this concept, I built a demonstration using GenUI techniques - specifically implementing an LLM model to generate complete user interfaces based on user needs and context in a laptop purchasing e-commerce setting. By combining existing user information with guided conversation, the LLM is able to dynamically generate and modify webpage content to precisely match a user’s individual preferences. Rather than navigating through generic product pages, users experience interfaces explicitly tailored to their requirements at that exact moment. The technical implementation leverages several key components: 1. Real-time UI generation based on conversational context 2. Dynamic content adaptation using visitor data 3. Integration patterns that maintain responsive performance This approach fundamentally disrupts traditional UI/UX methodologies, where interfaces are often designed once for many users. Instead, GenUI enables interfaces that are generated uniquely for each user, each time. To watch how GenUI is reshaping web experiences, learn the specific techniques I used, and see this demo in action check out my latest video: https://lnkd.in/evXBq9wc

🤖 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 ↓]

AI products like Cursor, Bolt and Replit are shattering growth records not because they're "AI agents". Or because they've got impossibly small teams (although that's cool to see 👀). It's because they've mastered the user experience around AI, somehow balancing pro-like capabilities with B2C-like UI. This is product-led growth on steroids. Yaakov Carno tried the most viral AI products he could get his hands on. Here are the surprising patterns he found: (Don't miss the full breakdown in today's bonus Growth Unhinged: https://lnkd.in/ehk3rUTa) 1. Their AI doesn't feel like a black box. Pro-tips from the best: - Show step-by-step visibility into AI processes - Let users ask, “Why did AI do that?” - Use visual explanations to build trust. 2. Users don’t need better AI—they need better ways to talk to it. Pro-tips from the best: - Offer pre-built prompt templates to guide users. - Provide multiple interaction modes (guided, manual, hybrid). - Let AI suggest better inputs ("enhance prompt") before executing an action. 3. The AI works with you, not just for you. Pro-tips from the best: - Design AI tools to be interactive, not just output-driven. - Provide different modes for different types of collaboration. - Let users refine and iterate on AI results easily. 4. Let users see (& edit) the outcome before it's irreversible. Pro-tips from the best: - Allow users to test AI features before full commitment (many let you use it without even creating an account). - Provide preview or undo options before executing AI changes. - Offer exploratory onboarding experiences to build trust. 5. The AI weaves into your workflow, it doesn't interrupt it. Pro-tips from the best: - Provide simple accept/reject mechanisms for AI suggestions. - Design seamless transitions between AI interactions. - Prioritize the user’s context to avoid workflow disruptions. -- The TL;DR: Having "AI" isn’t the differentiator anymore—great UX is. Pardon the Sunday interruption & hope you enjoyed this post as much as I did 🙏 #ai #genai #ux #plg

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.

I went to an AI UX workshop last night expecting recycled LinkedIn advice about "building AI trust through transparency." Instead, Isabella Yamin tore down LinkedIn's job posting flow using her CarbonCopies AI framework in real-time, while founders shared raw implementation struggles. It completely changed how I'm rethinking Maibel's onboarding flow. Here's what I stole from B2B SaaS principles to redesign emotional AI for B2C: 1️⃣ Progressive disclosure with purpose LinkedIn's fatal flaw? Optimizing for completion ease > Outcome quality. Recruiters are drowning in irrelevant applications because AI never learns what "qualified" means. The personalization paradox: How do we give users enough control without overwhelming them? Users don't want "frictionless". They want INFORMED control. 📌 At Maibel: I was falling into the same trap, making emotional coaching setup so simple that the AI couldn't understand user context. Now? Progressive complexity with clear trade-offs. Show users how their choices impact outcomes. → Want deeper insights? Add more context. → Want faster setup? Here's what the AI can't personalize. 2️⃣ Closed-loop data intelligence: What Platfio gets right They've built a platform for software agencies where where every data point feeds back into the entire system. User preferences in marketing flows shape proposals. Campaign performance shapes future recommendations. Every interaction becomes intelligence for future recommendations. 📌 At Maibel: Most wellness apps store emotional check-ins like digital journals. I'm turning them into predictive feedback loops. Emotional intelligence isn’t static but COMPOUNDS. Today's reflections shift tomorrow's suggestions. Patterns fuel prevention. Users' inputs on Monday could predict AND prevent Friday's breakdown. 3️⃣  Multi-modal creativity: Wubble's transparency approach Translating images and files into music - who'd have thought? They've cracked multi-modal creativity where users become co-creators, not passive consumers. The breakthrough moment for me: What if users could see how their visual environment contributes to emotional context? 📌 At Maibel: Users upload images of their day and see how AI analyzes emotional cues: cluttered workspace = overwhelm, junk food = stress eating. Multi-modal understanding users can contribute to and influence. 💡 The bottom line? B2B Saas gets one thing right: Every interaction has to earn trust. In B2B, failed AI means churn. In emotional AI, failed trust breaks belief in tech entirely. 📌 Here's what we're doing differently at Maibel: → Progressive complexity → Context-aware feedback → Multi-modal participation → Intelligence that compounds with every input. It's not just about building WITH AI. I'm designing systems that learn understand YOU before you even need to explain yourself. Kudos to Isabella, Shivang Gupta The Generative Beings, Shaad Sufi Hayden Cassar and everyone who shared deep product insights.

User Feedback Loops: the missing piece in AI success? AI is only as good as the data it learns from -- but what happens after deployment? Many businesses focus on building AI products but miss a critical step: ensuring their outputs continue to improve with real-world use. Without a structured feedback loop, AI risks stagnating, delivering outdated insights, or losing relevance quickly. Instead of treating AI as a one-and-done solution, companies need workflows that continuously refine and adapt based on actual usage. That means capturing how users interact with AI outputs, where it succeeds, and where it fails. At Human Managed, we’ve embedded real-time feedback loops into our products, allowing customers to rate and review AI-generated intelligence. Users can flag insights as: 🔘Irrelevant 🔘Inaccurate 🔘Not Useful 🔘Others Every input is fed back into our system to fine-tune recommendations, improve accuracy, and enhance relevance over time. This is more than a quality check -- it’s a competitive advantage. - for CEOs & Product Leaders: AI-powered services that evolve with user behavior create stickier, high-retention experiences. - for Data Leaders: Dynamic feedback loops ensure AI systems stay aligned with shifting business realities. - for Cybersecurity & Compliance Teams: User validation enhances AI-driven threat detection, reducing false positives and improving response accuracy. An AI model that never learns from its users is already outdated. The best AI isn’t just trained -- it continuously evolves.

AI isn't just changing how we work—for years, it has been transforming how customers discover what they want before they even know they want it. And that's through the power of recommendation systems. Let me break it down with a simple example we've all experienced: Open any Amazon product page and you'll see: • "Frequently bought together" • "Customers who viewed this also viewed" • "Recommended based on your browsing history" These aren't just random suggestions. They're calculated revenue multipliers. When a customer lands on a product page, three things typically happen: 1. They buy the original item 2. They buy the original item PLUS recommended items 3. They skip the original item but purchase a recommended alternative Each scenario drives revenue that wouldn't exist without AI-powered recommendations. But here's what most businesses miss: This isn't just for e-commerce. Social platforms use the same concept to increase engagement. When Twitter recommends content that keeps you scrolling longer, they're not just being helpful—they're growing ad impressions and potential click-throughs. The beauty of recommendation systems? There's room for experimentation. Unlike many AI applications where precision is critical, recommendation systems can be effective even when partially accurate. If one of three suggestions resonates, that's still a win. This flexibility creates the perfect playground for companies to: 📌 Test different algorithms 📌 Experiment with recommendation placement 📌 Refine suggestion strategies without disrupting user experience Recommendation engines are a good starting point for AI experimentation—they provide a good combination of impact with minimal disruption risk. Is your business leveraging the power of "you might also like" yet?

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!

Marketing Automation & Customer Service is no longer just about sending emails or filling out contact forms. With AI these flows can become journeys: interactive and truly personalized - unlocking new levels of engagement and conversion in Whatsapp or Chat. But where to start? Here’s a breakdown of the top journeys most e-commerce brands have implemented and how I rank their AI potential and impact: 1️⃣ Product Recommendations | AI Potential: High Helping your customer to make a choice and find the product that fits their needs. > Move beyond static scripts! AI can find best fitting products with LLM powered semantic search, resolve blockers, compare products and provide tailored suggestions. 2️⃣ Welcome Flow | High You offer an incentive, collect and opt-in and further into > With AI, this flow can become interactive: No form like answering all extrated from a normal informal conversation. Enrich their profiles for future personalization (email, birthday, ...) 3️⃣ Customer Service | High Taking care when your customers have a problem: > AI Agents will provide 24/7 multilingual support. Collect the info you need before handing over to a human if the certain problems still need the human insight, access, or touch. Save costs while enhancing customer experience. 4️⃣ FAQ Automation | Medium Make it easy for customers to find answers. > AI ensures responses are nuanced and personalized. 5️⃣ Abandoned Cart | Medium Customer is (almost) ready to buy, but got interrupted or needs a little nudge > Send a(i) personalized message based on the exact product they have in their cart. Highlight how it fits their preferences or past purchases. 6️⃣ Cross-Sell / Up-Sell | Medium Encourage customers to buy complementary products. > AI can craft compelling arguments for upgrades, bundles or next product to buy. 7️⃣ Birthday or Special Day Campaigns | Medium Send wishes and a little gift > Let AI create a personalized message, image, or video and send it via WhatsApp. 8️⃣ Winback / Replenishment | Low Remind customers to repurchase or return. > Personalization helps, but the core is timing. 9️⃣ Review Collection | Low Gather feedback and build trust with REVIEWS.io or alike > AI can personalize requests and handle negative feedback gracefully avoiding bad reviews. 🔟 Back-In-Stock | Low Notify customers when the product they wanted to buy is available again. > AI can add a personalized touch to the reminder [don't want to get out of stock? Talk to VOIDS] 1️⃣1️⃣Referral Programs | Low Encourage word-of-mouth with incentives for sharing. > AI can personalize referral messages for higher trust and conversion. 1️⃣2️⃣Fulfilment Updates | Low Keep customers informed about their orders. > Let AI add a personal touch related to the product shipped. [Want to turn into an upsell opportunity: Karla is doing a great job here] The future of e-commerce is about conversations, not campaigns. Which flow or journey are you excited to tackle first? #conversationalai

✨ Ever stared at an empty AI chat box, unsure what to type? You’re not alone! The “blank page” problem is real. That’s why prompt suggestions matter: They’re system-generated hints that guide users in forming queries or commands for AI tools. When designed well, they reduce cognitive load, spark creativity, and make it easier to explore what the AI can actually do. But not all prompt suggestions are the same. In our Nielsen Norman Group article, Kate Moran and I describe 3 distinct types every AI designer should know: 🔹 𝗨𝘀𝗲-𝗰𝗮𝘀𝗲 𝗽𝗿𝗼𝗺𝗽𝘁𝘀 → Examples that showcase what the AI is capable of and inspire creativity (great for onboarding and learnability). 🔹 𝗣𝗿𝗼𝗺𝗽𝘁-𝗮𝘂𝘁𝗼𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲 → Just like search autocompletes, these save time and effort by helping users finish typing efficiently. 🔹 𝗙𝗼𝗹𝗹𝗼𝘄-𝘂𝗽 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 → Contextual suggestions that keep the conversation flowing and encourage deeper engagement. 👉 Read the full article with examples here: https://lnkd.in/eaJCHSdQ #UXStrategy #AIUX #PromptWriting #AI #UserExperience #DigitalProductDesign