User Experience

Invisible UX is coming 🔥 And it’s going to change how we design products, forever. For decades, UX design has been about guiding users through an experience. We’ve done that with visible interfaces: Menus. Buttons. Cards. Sliders. We’ve obsessed over layouts, states, and transitions. But with AI, a new kind of interface is emerging: One that’s invisible. One that’s driven by intent, not interaction. Think about it: You used to: → Open Spotify → Scroll through genres → Click into “Focus” → Pick a playlist Now you just say: “Play deep focus music.” No menus. No tapping. No UI. Just intent → output. You used to: → Search on Airbnb → Pick dates, guests, filters → Scroll through 50+ listings Now we’re entering a world where you guide with words: “Find me a cabin near Oslo with a sauna, available next weekend.” So the best UX becomes barely visible. Why does this matter? Because traditional UX gives users options. AI-native UX gives users outcomes. Old UX: “Here are 12 ways to get what you want.” New UX: “Just tell me what you want & we’ll handle the rest.” And this goes way beyond voice or chat. It’s about reducing friction. Designing systems that understand intent. Respond instantly. And get out of the way. The UI isn’t disappearing. It’s mainly dissolving into the background. So what should designers do? Rethink your role. Going forward you’ll not just lay out screens. You’ll design interactions without interfaces. That means: → Understanding how people express goals → Guiding model behavior through prompt architecture → Creating invisible guardrails for trust, speed, and clarity You are basically designing for understanding. The future of UX won’t be seen. It will be felt. Welcome to the age of invisible UX. Ready for it?

🤖 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 ↓]

After 1,000 hours of prompt engineering, these 6 patterns work best. Here's the framework: --- ✦ I saw it here: https://lnkd.in/dj8Ax6BT. ✦ I tested it, and it's quite effective! ✦ I wrote numerous blogs on prompt engineering. ✦ 7 Sins of prompting: https://lnkd.in/duP3Za5W. ✦ What do people prompt: https://lnkd.in/dGYgcQ_7. ✦ How to search: https://lnkd.in/dxzSBEjW. ✦ ChatGPT-5: https://lnkd.in/gVx_ZPh3. --- K - Keep it simple Bad: 500 words of context Good: One clear goal Example: Instead of "I need help writing something about Redis," use "Write a technical tutorial on Redis caching" Result: 70% less token usage, 3x faster responses E - Easy to verify Your prompt needs clear success criteria Replace "make it engaging" with "include 3 code examples" If you can't verify success, AI can't deliver it My testing: 85% success rate with clear criteria vs 41% without R - Reproducible results Avoid temporal references ("current trends", "latest best practices") Use specific versions and exact requirements Same prompt should work next week, next month 94% consistency across 30 days in my tests N - Narrow scope One prompt = one goal Don't combine code + docs + tests in one request Split complex tasks Single-goal prompts: 89% satisfaction vs 41% for multi-goal E - Explicit constraints Tell AI what NOT to do "Python code" → "Python code. No external libraries. No functions over 20 lines." Constraints reduce unwanted outputs by 91% L - Logical structure Format every prompt like: Context (input) Task (function) Constraints (parameters) Format (output) Real example from my work last week: Before KERNEL: "Help me write a script to process some data files and make them more efficient" Result: 200 lines of generic, unusable code After KERNEL: Task: Python script to merge CSVs Input: Multiple CSVs, same columns Constraints: Pandas only, <50 lines Output: Single merged.csv Verify: Run on test_data/ Result: 37 lines, worked on first try Actual metrics from applying KERNEL to 1000 prompts: First-try success: 72% → 94% Time to useful result: -67% Token usage: -58% Accuracy improvement: +340% Revisions needed: 3.2 → 0.4 Advanced tip: Chain multiple KERNEL prompts instead of writing complex ones. Each prompt does one thing well, feeds into the next. The best part? This works consistently across GPT-5, Claude, Gemini, even Llama. It's model-agnostic.

Are you solving the right problem? Now that probability and uncertainty is creeping into previously deterministic systems, it's time to talk about errors -- those bad conclusions you're about to jump to. Everyone in data science knows about Type I and Type II errors: 1️⃣ Type I Error = False positive. You thought you found something actionable, but it was noise. 2️⃣ Type II Error = False negative. You missed a real signal and failed to change course. But the one that should really keep you up at night is the Type III Error: ✔️ All the right math, beautiful dashboards, flawless execution… ❌ Solving the wrong problem. 3️⃣ Type III Error = Wrong positive. It's... The boardroom high-five that shouldn’t have happened. The KPI that looks impressive, but delivers no actual value. Organizations love to ask: “What does the data say?” But often they're skipping the more important question: “Are we asking the right question?” The most dangerous AI/ML system isn’t the one that breaks. It’s the one that works perfectly—on a goal that shouldn't exist in the first place. That’s why I keep saying: “Skilled decision-making is a must-have for effective AI and data science.” Decision intelligence is how you elevate the judgment and framing skills required to turn information into better action. And that’s where most organizations are weakest. They hire technical folk before the leaders have done their homework and properly clarified the decisions worth making. And the more your systems scale, the more dangerous this becomes. Want to reduce Type III errors? Here’s what that takes: ✅ Start with the decision/action/vision, not the data. ✅ Define what “better” means before you look for insights. ✅ Think through the alternatives before automating anything. ✅ Bring in decision scientists—don’t expect everyone to be one without training. ✅ Watch out for technically flawless projects that deliver suspiciously little impact. Data-driven decisions aren’t the same as data-decorated decisions. Your turn: Have you ever seen a Type III error in the wild? What helped you catch it? If you found this useful, a repost ♻️ makes my heart happy. And a subscription to my newsletter makes my day. decision.substack.com #DecisionIntelligence #DataScience #Leadership #AI #DecisionMaking *Footnote for my fellow statisticians in the room: We statisticians shudder unless the meaning is exactly right, so here's the more proper set of definitions: Type I Error: Incorrectly rejecting the null hypothesis. Leaving a good default action. Type II Error: Incorrectly failing to reject the null hypothesis. Staying with a bad default action. Type III Error: Correctly rejecting the wrong null hypothesis. Wasting your life. If you read this far and were cheered by that footnote, you're the best kind of nerd -- definitely repost ♻️ keep the good stuff alive. Join my newsletter where sensible leaders go for AI and decision science: decision.substack.com

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 👇

𝟔𝟔% 𝐨𝐟 𝐀𝐈 𝐮𝐬𝐞𝐫𝐬 𝐬𝐚𝐲 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐜𝐨𝐧𝐜𝐞𝐫𝐧. What does that tell us? Trust isn’t just a feature - it’s the foundation of AI’s future. When breaches happen, the cost isn’t measured in fines or headlines alone - it’s measured in lost trust. I recently spoke with a healthcare executive who shared a haunting story: after a data breach, patients stopped using their app - not because they didn’t need the service, but because they no longer felt safe. 𝐓𝐡𝐢𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐥𝐢𝐯𝐞𝐬 - 𝐭𝐫𝐮𝐬𝐭 𝐛𝐫𝐨𝐤𝐞𝐧, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐬𝐡𝐚𝐭𝐭𝐞𝐫𝐞𝐝. Consider the October 2023 incident at 23andMe: unauthorized access exposed the genetic and personal information of 6.9 million users. Imagine seeing your most private data compromised. At Deloitte, we’ve helped organizations turn privacy challenges into opportunities by embedding trust into their AI strategies. For example, we recently partnered with a global financial institution to design a privacy-by-design framework that not only met regulatory requirements but also restored customer confidence. The result? A 15% increase in customer engagement within six months. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐫𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭’𝐬 𝐥𝐨𝐬𝐭? ✔️ 𝐓𝐮𝐫𝐧 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧𝐭𝐨 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭: Privacy isn’t just about compliance. It’s about empowering customers to own their data. When people feel in control, they trust more. ✔️ 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: AI can do more than process data, it can safeguard it. Predictive privacy models can spot risks before they become problems, demonstrating your commitment to trust and innovation. ✔️ 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐄𝐭𝐡𝐢𝐜𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Collaborate with peers, regulators, and even competitors to set new privacy standards. Customers notice when you lead the charge for their protection. ✔️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐭𝐲: Techniques like differential privacy ensure sensitive data remains safe while enabling innovation. Your customers shouldn’t have to trade their privacy for progress. Trust is fragile, but it’s also resilient when leaders take responsibility. AI without trust isn’t just limited - it’s destined to fail. 𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐠𝐚𝐢𝐧 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧? 𝐋𝐞𝐭’𝐬 𝐬𝐡𝐚𝐫𝐞 𝐚𝐧𝐝 𝐢𝐧𝐬𝐩𝐢𝐫𝐞 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 👇 #AI #DataPrivacy #Leadership #CustomerTrust #Ethics

I've been reflecting on one major trend from last year that I feel will be hard to ignore in 2025: Gen Z’s relationship with brands and social media. This generation doesn’t just consume content, they drive it. And they do so with a level of authenticity and transparency that demands our attention. For Gen Z, brand loyalty isn’t built on flashy ads or influencer endorsements alone. It’s about values. It’s about knowing what the brand stands for and aligning with causes they care about: be it sustainability, inclusivity, or social justice. Here’s how I’ve been thinking about this shift as an entrepreneur: For Gen Z, being true to themselves is really important. They want brands that embrace uniqueness and support personal expression. To connect with them, we need to be authentic and offer products and messages that let them express who they really are. Social Media is the New Word of Mouth: If you’re not engaging in the conversations Gen Z is having on social media, you’re missing out. They trust their peers and online communities more than traditional advertising, and their feedback is immediate and powerful. Experience Over Projection: For this generation, it’s not just about seeing an ad but engaging with a brand in a meaningful way. Whether through personalized experiences, interactive campaigns, or exclusive content, creating a connection is more valuable than ever. Gen Z is not just shaping the future of business but is redefining what it means to build loyalty and trust. Is your brand ready for this shift?

Loyalty is failing. Gen Z & long-term commitment. 22% of Gen Z consumers consider themselves loyal to one brand is a clear warning for legacy loyalty strategies. Unlike previous generations, Gen Z doesn’t see brand loyalty as a long-term commitment, they’re loyal to moments, not just names. +43% increase in engagement and sales conversions among Gen Z Beauty brands offering "limited-edition drops" and collaborative experiences. +71% Gen Z say they would rather spend money on an experience than a product. >>Loyalty is FAILING, but why<< +Transactional systems feel outdated: Point-based rewards for repeat purchases don’t excite this audience. They expect more than discounts or free samples. +They’re brand-agnostic but experience-driven: Gen Z freely switches between brands if the experience, aesthetic, or values feel fresher or more aligned with their identity. +They buy into stories, not just products: They want to align with brands that represent something, social causes, cultural movements, or communities they relate to. >>DYNAMIC LOYALTY<< What’s this? as it name indicates its a system that rewards interaction, aligns with their values, and constantly evolves. And that is what your brand needs. → Create experience-driven loyalty programs: Offer early access to limited drops, invite-only events, or backstage content. Think like a fan club, not a punch card. +Example: A loyalty tier that unlocks tickets to a pop-up experience or an exclusive AR filter. →Let them co-create: Invite Gen Z customers to co-develop product ideas, designs, or campaign themes. Give them ownership in your brand’s creative journey. +Example: Voting on packaging designs or joining beta tester groups. →Align with their values: Sustainability, inclusivity, and social good aren’t nice-to-haves. they’re expectations. Use loyalty programs to reward actions too, like recycling, sharing causes, or supporting small creators. +Example: “Earn loyalty points by returning empties or attending a sustainability workshop.” →Deliver constant novelty: Rotate limited editions regularly. Use scarcity and surprise to create FOMO and buzz. +Gen Z doesn’t commit to a single brand, but they’ll keep returning if each visit feels fresh and share-worthy. →Go omnichannel but social-first. Should live across TikTok, Instagram, pop-ups, and web. Let them earn or unlock rewards through social engagement, not just purchases. +Example: A user gets exclusive content or perks for creating UGC with your brand. Bottom Line. Loyalty must be earned over and over through experience, relevance, and emotional connection. Think dynamic loyalty: a system that rewards interaction and go for it. Find my curated search of examples and get ready for your next HIT. Featured Brands: Balmain Benefit Chanel Charlotte tilbury Cerave Fennty L’Oreal OGX YSL #beautypackaging #beautybusiness #beautyprofessionals #experienceretail #luxuryexperiences #genz

𝗔 𝗱𝗮𝘁𝗮 𝗰𝗼𝗻𝘁𝗿𝗮𝗰𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝘁𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗮𝗿𝘁𝗶𝗳𝗮𝗰𝘁. It is a governance decision dressed in JSON and most teams skip the decision part. They define schemas, configure SLAs and pick the tooling. Then every week, the same arguments come back. ↳ Which “active customer” is the right one? ↳ Who can approve a schema change? ↳ Who validates data quality alerts? The contract exists. Trust does not. Because a data contract is not only technical, it is also: ↳ decision ownership ↳ trust signals ↳ accountability between producers and consumers And it does not replace governance. It extends it. Your stewardship program, glossary and policies become operational agreements teams actually use. The hardest part is rarely the document. It is the change management around it: ↳ getting producer teams to own their contracts ↳ handling breaking changes without slowing delivery ↳ making escalation forums lead to actual decisions Now the pressure is higher. AI agents consume datasets nobody fully owns. Self-service analytics creates more local definitions. Cross-domain dependencies keep growing. Without clear agreements, every pipeline change becomes a governance issue. A data contract should reduce friction. Not become another document nobody reads. The best ones make decisions faster because responsibilities are already clear. ——— 📌 Save it for later. 👋 Follow Mathieu Le Guével to fix Data Governance.

Toyota's competitors thought Taiichi Ohno was insane. He was letting line workers stop production over a scratched fender. They stopped laughing when Toyota dominated the industry for the next 50 years. Here's what happened: After World War II, Ohno visited auto factories in Detroit. He expected to learn from the best. Instead, he found rework areas filled with broken pieces and leftover parts. Waste everywhere, hiding in plain sight. He went back to Toyota City and did something nobody had tried before. At every station on the assembly line, he hung a rope called the Andon cord. The instruction was simple: If you see a defect pull the cord. The line slows or stops. Engineers, workers, and suppliers huddle up and fix it on the spot. Detroit thought he was crazy. "How can you build thousands of cars a day if you stop the line for every little scratch?" Ohno's answer was simple: A scratched fender is an early warning that a piece of equipment is failing. Fix the scratch today, and you prevent 500 defective fenders next week. Ignore it, and you're building rework areas just like Detroit. That's the difference between firefighting and fire prevention. Most operations leaders I talk to are stuck in firefighting mode. They walk past small defects every day because the line has to keep moving. But those small defects are talking to you. They're telling you exactly where your next big quality failure will come from. How many "minor" defects is your team walking past today that are actually early warning signs?