AI For Enhancing User Experience

Over the last year, I’ve seen many people fall into the same trap: They launch an AI-powered agent (chatbot, assistant, support tool, etc.)… But only track surface-level KPIs — like response time or number of users. That’s not enough. To create AI systems that actually deliver value, we need 𝗵𝗼𝗹𝗶𝘀𝘁𝗶𝗰, 𝗵𝘂𝗺𝗮𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 that reflect: • User trust • Task success • Business impact • Experience quality    This infographic highlights 15 𝘦𝘴𝘴𝘦𝘯𝘵𝘪𝘢𝘭 dimensions to consider: ↳ 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆 — Are your AI answers actually useful and correct? ↳ 𝗧𝗮𝘀𝗸 𝗖𝗼𝗺𝗽𝗹𝗲𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲 — Can the agent complete full workflows, not just answer trivia? ↳ 𝗟𝗮𝘁𝗲𝗻𝗰𝘆 — Response speed still matters, especially in production. ↳ 𝗨𝘀𝗲𝗿 𝗘𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁 — How often are users returning or interacting meaningfully? ↳ 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗥𝗮𝘁𝗲 — Did the user achieve their goal? This is your north star. ↳ 𝗘𝗿𝗿𝗼𝗿 𝗥𝗮𝘁𝗲 — Irrelevant or wrong responses? That’s friction. ↳ 𝗦𝗲𝘀𝘀𝗶𝗼𝗻 𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻 — Longer isn’t always better — it depends on the goal. ↳ 𝗨𝘀𝗲𝗿 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 — Are users coming back 𝘢𝘧𝘵𝘦𝘳 the first experience? ↳ 𝗖𝗼𝘀𝘁 𝗽𝗲𝗿 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻 — Especially critical at scale. Budget-wise agents win. ↳ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗗𝗲𝗽𝘁𝗵 — Can the agent handle follow-ups and multi-turn dialogue? ↳ 𝗨𝘀𝗲𝗿 𝗦𝗮𝘁𝗶𝘀𝗳𝗮𝗰𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲 — Feedback from actual users is gold. ↳ 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 — Can your AI 𝘳𝘦𝘮𝘦𝘮𝘣𝘦𝘳 𝘢𝘯𝘥 𝘳𝘦𝘧𝘦𝘳 to earlier inputs? ↳ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 — Can it handle volume 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 degrading performance? ↳ 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 — This is key for RAG-based agents. ↳ 𝗔𝗱𝗮𝗽𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗦𝗰𝗼𝗿𝗲 — Is your AI learning and improving over time? If you're building or managing AI agents — bookmark this. Whether it's a support bot, GenAI assistant, or a multi-agent system — these are the metrics that will shape real-world success. 𝗗𝗶𝗱 𝗜 𝗺𝗶𝘀𝘀 𝗮𝗻𝘆 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗼𝗻𝗲𝘀 𝘆𝗼𝘂 𝘂𝘀𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀? Let’s make this list even stronger — drop your thoughts 👇

Some technologies don’t just solve problems — they give people their independence back. I rediscovered Liftware, and I was genuinely moved by what it can do. It looks simple: a smart handle connected to everyday utensils. But inside, it’s a powerful piece of engineering designed for people with hand tremors (Parkinson’s, essential tremor, and more). Here’s how it works: 🔹 Sensors detect tiny hand movements in real time 🔹 Micro-motors instantly counteract the tremor 🔹 The spoon or fork stays stable — even if the hand doesn’t The result? Up to 70% less shaking. And for many people, that means eating soup again… without help. This is technology at its best: invisible, intelligent, and deeply human. 💡 My take Most people don’t know this, but Liftware was developed by a small startup before being acquired by Google’s life sciences division (now Verily). What makes it remarkable is the engineering challenge: the device doesn’t try to stop the tremor — it predicts and cancels it. It’s basically a tiny real-time AI system… hidden inside a spoon. This is the future I love: not just smarter devices, but more compassionate ones. If you’ve seen other innovations that genuinely improve people’s lives, I’d love to discover them. What’s one piece of tech-for-good that inspired you recently? #techforgood #innovation #technology #healthtech #accessibility #assistivetechnology #futureofhealth #inclusiveDesign #AI #impact

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.

In the world of AI, most products today lean towards pull-based experiences like you ask a question, and the system responds. These experiences feel intuitive, empowering users to be in control. But while they create a solid foundation for usability, the real wow factor emerges when AI shifts to push-based use cases. Imagine AI anticipating your needs: suggesting edits to your document as you write, proposing new paragraphs to enhance clarity, or even offering tailored deals across portals as you browse a product attached to your shopping list, unlike standard recommendations. Push-based AI doesn’t wait to be called upon, but it’s there, actively delivering value in real time. This proactive intelligence becomes feasible with agentic AI systems across systems. These agents not only automate tasks but also enhance user workflows by making smart decisions on their behalf. For instance, writing an ad copy becomes seamless when AI not only generates ideas but also conducts market research, optimizes for SEO, and aligns with the latest trends that too are all in the background. It’s no longer about searching for insights but having them delivered at the right moment. The value is in timing and relevance, making AI feel more like a trusted assistant than a tool. This shift from pull to push in AI is why agentic systems are gaining so much momentum. It’s not just a race for computing power; rather, it’s a race for attention. By meeting users where they are and anticipating their needs, AI applications can elevate user experiences and redefine expectations. The future of AI isn’t just about solving problems when asked; it’s about solving problems before you even realize they exist. #ExperienceFromTheField #WrittenByHuman #EditedByAI

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

Everyone’s excited to launch AI agents. Almost no one knows how to measure if they’re actually working. Over the last year, we’ve seen brands launch everything from GenAI assistants to support bots to creative copilots but the post-launch metrics often look like this: • Number of chats • Average latency • Session duration • Daily active users Useful? Yes. But sufficient? Not even close. At ALTRD, we’ve worked on AI agents for enterprises and if there’s one lesson it’s this: Speed and usage mean nothing if the agent isn’t solving the actual problem. The real performance indicators are far more nuanced. Here’s what we’ve learned to track instead: 🔹 Task Completion Rate — Can the AI go beyond answering a question and actually complete a workflow? 🔹 User Trust — Do people come back? Do they feel confident relying on the agent again? 🔹 Conversation Depth — Is the agent handling complex, multi-turn exchanges with consistency? 🔹 Context Retention — Can it remember prior interactions and respond accordingly? 🔹 Cost per Successful Interaction — Not just cost per query, but cost per outcome. Massive difference. One of our clients initially celebrated their bot’s 1 million+ sessions - until we uncovered that less than 8% of users actually got what they came for. That 8% wasn’t a usage issue. It was a design and evaluation issue. They had optimized for traffic. Not trust. Not success. Not satisfaction. So we rebuilt the evaluation framework - adding feedback loops, success markers, and goal-completion metrics. The results? CSAT up by 34% Drop-off down by 40% Same infra cost, 3x more value delivered The takeaway: Don’t just measure what’s easy. Measure what matters. AI agents aren’t just tools - they’re touchpoints. They represent your brand, shape user experience, and influence business outcomes. P.S. What’s one underrated metric you’ve used to evaluate AI performance? Curious to learn what others are tracking.

🔮 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.

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! 👀

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.

Let’s be honest, the “Listen, Analyze, Act” model just isn’t enough anymore. CX teams need to move faster, focus sharper, and deliver results everyone in the business can see. That means making outcomes core to your approach, and making sure you energize the entire organization around what matters. How do you deliver on “Outcomes > Action” as the new mantra over “Listen—> Analyze —> Act?” First, unify your data. Easier said than done, but you have to pull together every signal from surveys, tickets, chats, ops data, and social feedback. Use AI to create a real-time, connected customer view, so you’re not just looking at snapshots, but seeing the bigger story as it unfolds. Second, interpret what you find. AI can surface intent, risk, and opportunity in ways traditional methods miss. Zero in on what actually drives the experience and impacts the business. This is where you separate noise from the signals that count. You should also be thinking about how this impacts revenue, cost-to-serve, and your company’s culture (not just customers). Third, orchestrate targeted action. AI can help you prioritize and automate interventions, whether that’s routing cases, suggesting next-best actions (or product), or personalizing experiences at scale. Every action should have a clear line of sight to the business outcome you’re after. Measurable. Fourth, focus on the outcome. Set non-negotiable, measurable goals: revenue, retention, cost to serve, or employee engagement. Every initiative, every improvement, should be traced back to these metrics. Celebrate when you move the needle and be honest about what didn’t work. Finally, energize the business. Change only sticks when you bring others with you. CX leaders have to rally stakeholders, share early wins, and make progress visible. This is about building belief and momentum so everyone feels ownership of the results. How does this look in real life? Imagine that renewal rates among small business customers are falling. You unify data across channels and use AI to interpret that a recent product change is causing confusion. You orchestrate a fix by launching in-app tutorials or targeted outreach, and equip the frontlines with talking points. You measure the outcome by tracking renewal rates, then energize the business by celebrating the improvement, sharing the story, and holding teams accountable for continued results. Listening, Analyzing, and Acting are important. But the framework is what, 15 years old or more at this point? It needs to evolve given businesses, technology, and customers have evolved. Don’t keep following the same old script. Challenge the status quo. Action with purpose, a business energized around outcomes, and AI as the catalyst for lasting impact is the start. #customerexperience #leadership #ai #changemanagement #outcomesoveraction