How AI Shapes User Experience

🔮 Design Patterns For AI Interfaces (https://lnkd.in/dyyMKuU9), a practical overview with emerging AI UI patterns, layout considerations and real-life examples — along with interaction patterns and limitations. Neatly put together by Sharang Sharma. One of the major shifts is the move away from traditional “chat-alike” AI interfaces. As Luke Wroblewski wrote, when agents can use multiple tools, call other agents and run in the background, users orchestrate AI work — there’s a lot less chatting back and forth. In fact, chatbot widgets are rarely an experience paradigm that people truly enjoy and can fall in love with. Mostly because the burden of articulating intent efficiently lies on the user. It can be done (and we’ve learned to do that), but it takes an incredible amount of time and articulation to give AI enough meaningful context for it to produce meaningful insights. As it turned out, AI is much better at generating prompt based on user’s context to then feed it into itself. So we see more task-oriented UIs, semantic spreadsheets and infinite canvases — with AI proactively asking questions with predefined options, or where AI suggests presets and templates to get started. Or where AI agents collect context autonomously, and emphasize the work, the plan, the tasks — the outcome, instead of the chat input. All of it are examples of great User-First, AI-Second experiences. Not experiences circling around AI features, but experiences that truly amplify value for users by sprinkling a bit of AI in places where it delivers real value to real users. And that’s what makes truly great products — with AI or without. ✤ Useful Design Patterns Catalogs: Shape of AI: Design Patterns, by Emily Campbell 👍 https://shapeof.ai/ AI UX Patterns, by Luke Bennis 👍 https://lnkd.in/dF9AZeKZ Design Patterns For Trust With AI, via Sarah Gold 👍 https://lnkd.in/etZ7mm2Y AI Guidebook Design Patterns, by Google https://lnkd.in/dTAHuZxh ✤ Useful resources: Usable Chat Interfaces to AI Models, by Luke Wroblewski https://lnkd.in/d-Ssb5G7 The Receding Role of AI Chat, by Luke Wroblewski https://lnkd.in/d8xcujMC Agent Management Interface Patterns, by Luke Wroblewski https://lnkd.in/dp2H9-HQ Designing for AI Engineers, by Eve Weinberg https://lnkd.in/dWHstucP #ux #ai #design

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.

GenAI is rapidly changing how people navigate the digital world with AI-driven traffic to U.S. retail, travel, and banking sites surging over 1,000% in recent months — doubling every two months since late 2024. What’s notable isn’t just the volume, but the quality. AI-driven users are more engaged — spending more time on site, viewing more pages, and bouncing less. While conversions still trail slightly, they’re improving fast as trust in AI grows. Consumers are now using AI for everything from product discovery and deal hunting to travel planning and financial advice. It’s becoming the new starting point for digital journeys. The next wave is already forming: agentic AI. These tools won’t just assist — they’ll act. From filling out forms to completing transactions, AI will increasingly execute tasks on behalf of users, pushing further into the commerce layer. This shift is rapidly reshaping traditional search. As AI captures intent earlier and takes action, the front door to the internet moves. Businesses must rethink how they show up — not just in search, but inside the AI itself. #ai

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! 👀

Your wireframes just got 10x faster, but are they any better? AI is transforming the field of UX design, introducing new opportunities for personalization, efficiency, and decision-making. We’re entering what we call UX 3.0, a shift from Human-Centered Design (HCD) to Human-Centered AI (HCAI). The AI-Augmented UX Shifts thats happening as I write this post: - Designing for Transparency (XAI) - Ethical design in AI means helping users trust the system. Use explainability patterns like: “Because you…” (input-based explanation) or “If you had…” (counterfactual explanation). Transparency is not a feature, it’s the new usability. - Prompt Engineering as the New Design Language - Good prompts are the new design briefs. Designers who can translate intent into structured context will outperform those chasing tool hacks. - Hyper-Personalization at Scale: AI enables real-time, adaptive experiences, interfaces that adjust to each user’s behavior and context. Personalization done right can boost impact and retention. - Accelerated Ideation & Research: AI tools like ChatGPT, Gemini, Perplexity, Uizard and others slash manual effort from synthesizing research to generating first drafts. They can make us 10x faster. But speed ≠ depth. Human intervention and validation via tested methods are still a must. My take and what drives us to deliver experience design impact at Publicis Sapient are: → AI’s speed is addictive, but it’s easy to mistake motion for meaning. → If we let AI think for us, we lose the intentionality and empathy that define great design. → Every AI output is a draft, not a decision. → The human designer is and must remain the final check against bias and blind spots. We’re all learning this in real time. What’s the single most important skill a designer should learn today to design responsibly for Human-Centered AI systems? I’d love to hear your thoughts 👇 RethinkingUX #ai

AI in healthcare isn’t just about intelligence—it’s about trust, usability, and collaboration. The best AI tools don’t feel like black boxes, they feel like partners. Transparency builds trust. Users shouldn’t have to guess why AI made a decision. The best tools show their work step by step, let users ask, “Why did AI do that?” and use visuals to explain decisions. Advancing AI is important, but so is improving how humans and AI work together. The best experiences help users guide AI, not just receive its output. That means providing multiple ways to interact and designing AI that helps refine inputs before execution. AI should work with you, not just for you. Collaboration beats automation. The best AI tools feel interactive, not one-and-done. They offer different collaboration modes and let users refine and iterate on results. Users should see and edit AI’s impact before it’s final. Trust grows when people stay in control. That means previewing changes before committing, offering undo options when needed, and creating a try-before-you-buy experience, often without needing an account. AI should fit into workflows, not disrupt them. Good AI feels seamless. The best designs let users quickly accept or reject AI suggestions, make transitions between AI and manual work effortless, and keep the user’s context in focus without unnecessary interruptions. AI alone isn’t the differentiator anymore. Great user experience is.

𝗜𝗻𝘁𝗲𝗻𝘁 𝗯𝘆 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆: Designing the AI User Experience AI is not just a better chat box. It changes the user’s role from operator to supervisor, which forces UX to move from command-based interaction toward intent-based delegation, new usability metrics, orchestration layers, calibrated friction, and ultimately exploration-based interaction to clarify the user’s needs. As software shifts from apps to AI agents, mature intent-based systems will settle into a triple-layered design model: 🎯 𝗜𝗻𝘁𝗲𝗻𝘁 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: Where users state outcomes. Context-aware and multimodal, this layer increasingly infers implicit intent from ambient signals: drafting the prompt so users just confirm. 🔍 𝗢𝗿𝗰𝗵𝗲𝘀𝘁��𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: The negotiation layer. Agents reveal plans, seek consent, and provide post-action receipts. In enterprises, it resolves collaborative intent: flagging conflicts, enforcing policies, and showing who's affected before execution. 🖐️ 𝗗𝗶𝗿𝗲𝗰𝘁 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗦𝘂𝗿𝗳𝗮𝗰𝗲: The GUI lives on as a fallback for inspection, correction, and override. But users now manipulate plans, not raw controls: retaining hands-on agency at a higher level of abstraction. My full article 👉 https://lnkd.in/grRVAhTe

When UX Becomes Human Something fascinating is happening in the world of user experience. After years of perfecting clicks and swipes, we are witnessing a fundamental shift - UX is learning to feel. AI powered platforms should be able to understand frustration in typing patterns, adapt its approach in real-time, and turn what could have been a poor customer experience into a positive interaction. Not through better button placement, but through better understanding. The evolution we are seeing is - From user journeys to emotional journeys From touch points to trust points From interface design to emotion design From user personas to human relationships The best UI is sometimes no UI at all. As we move toward ambient computing, with smart glasses, AR interfaces, and whatever comes next, the line between digital and human experience will continue to blur. The winners won't be those with the slickest interfaces, but those who create the most emotionally intelligent ecosystems. Are you ready for the era where UX isn't just about user experience, but human experience? #HumanExperience #AIInnovation #FutureOfUX #cio #ceo #cto #cdo #cfo #caio #EmotionalIntelligence #DigitalTransformation All opinions are my own and not those of my employer. 

AI isn’t here to replace UX professionals—it’s here to enhance our work. In my recent conversation with Jorge Arango on the User Research Strategist podcast, we explored how AI is transforming UX, from content audits to research workflows, and how to integrate it effectively without losing our human edge. 🔗 Listen here: https://lnkd.in/dEPdw_Sm Here’s what we discussed and how you can apply it: 1. Use AI to supercharge your research - AI can help with competitor analysis, data synthesis, and research summaries, but only if you ask the right questions. Instead of broad prompts, focus on specific trends and sources. - Ask AI to summarize industry trends based on reports, then validate with your expertise. 2. Simplify large-scale content audits - Taxonomy creation and content analysis can be tedious and AI can help process large datasets quickly without losing structure. - Use AI to automate content categorization and identify gaps in existing site structures. 3. Make AI your “second brain” for UX reports - Think of AI as your collaborative assistant. It won’t replace your expertise, but it can help refine your work and offer alternative perspectives. - Ask AI, “What am I missing?” to uncover new angles for your reports. 4. Overcome AI’s limitations - AI has limits like context windows, lack of domain expertise, and potential bias. - How to work around it: Break large research data into chunks and provide structured, focused input for better results. - Use AI to enhance, not replace, your decision-making process. 5. Stay curious and experiment - AI goes beyond chatbots. There are powerful tools for usability test analysis, journey mapping, and more. - Dedicate time to experimenting with AI in different phases of your UX process. AI isn’t a threat. It’s an opportunity. By embracing it with curiosity, we can leverage it to enhance our UX workflows and deliver greater impact.

Generative AI systems designed to influence human beliefs and actions are becoming increasingly pervasive. El et al. (2026) call the concern cognitive security: protecting human cognitive processes from hazardous influence. I think this framing deserves much more attention in UX and human AI interaction. The problem is not influence itself. Education influences people. Therapy influences people. Public health campaigns influence people. Good design influences people too. The question is whether an AI system supports the user’s ability to think, or takes over parts of the thinking process. That difference can be subtle. A helpful AI might say: “Slow down. What evidence shaped that decision?” A manipulative AI might say: “Responsible people support this. Act now.” Both are interactive. Both may feel personalized. Both may produce a smooth experience. But cognitively, they are doing very different things. This is why I think traditional UX metrics are becoming incomplete for AI products. Trust, satisfaction, engagement, and task completion can all look positive while hiding deeper risks. A user may trust the system because it is accurate, or because it has become socially persuasive. A user may keep returning because the product is useful, or because the interaction has become emotionally self reinforcing. A user may accept a recommendation because it is well justified, or because the system removed the moment where reflection would normally happen. For AI systems, we need to measure cognitive impact more directly. Did the interaction change the user’s belief or decision? Did that change last? Did repeated use create dependence? Does the user understand how the AI shaped their thinking? These questions matter for AI tutors, AI companions, mental health chatbots, shopping assistants, workplace agents, recommendation systems, and civic information tools. These systems shape attention, trust, memory, emotion, and action over time. A good AI system should make people better thinkers. It should help them compare evidence, notice uncertainty, consider alternatives, and recognize when they are being influenced. Sometimes that means adding friction. Sometimes it means showing uncertainty. Sometimes it means asking a reflective question instead of giving an immediate answer. The future of responsible AI design will not be defined only by fluency or convenience. It will be defined by whether the system protects human judgment.