User Experience

Empathy Isn’t Missing — It’s Misframed I’ve watched this video countless times. Every time, I don’t see generosity. I see design. I used to believe people ignore the truth because they don’t care. Now I realize it’s because they don’t see what I see. Empathy isn’t a lack of compassion — it’s a lack of perspective. And perspective can be designed. The words didn’t change the man’s story — they changed our frame of perception. When language shifts from description to contrast, it activates awareness. That’s the mechanism behind empathy — it’s not emotional contagion, it’s cognitive reframing. → We respond to difference, not repetition. → We act when a message bridges our world with someone else’s. → We feel when language turns distance into proximity. Here’s how I try to apply that lesson in my own work: ✅ Reveal contrast, not condition. Don’t describe pain — expose the gap between what is and what could be. ✅ Design for awareness before emotion. Help people notice first; feeling follows naturally. ✅ Make others participants, not observers. Use framing that transfers perspective, not pity. ✅ Use silence strategically. Leave room for the reader to complete the meaning. Because empathy doesn’t start with emotion — it starts with architecture. The right words don’t tell people what to feel. They help them feel what was already true. 💭 The Question 👉 When you communicate — are you trying to make people care, or helping them notice what they’ve been blind to all along? #LeadershipDesign #FramingEffect #CommunicationStrategy #CognitiveEmpathy #BehavioralPsychology #PerceptionDesign Video credits: Dr. Marcell Vollmer

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?

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

🌎 Designing Cross-Cultural And Multi-Lingual UX. Guidelines on how to stress test our designs, how to define a localization strategy and how to deal with currencies, dates, word order, pluralization, colors and gender pronouns. ⦿ Translation: “We adapt our message to resonate in other markets”. ⦿ Localization: “We adapt user experience to local expectations”. ⦿ Internationalization: “We adapt our codebase to work in other markets”. ✅ English-language users make up about 26% of users. ✅ Top written languages: Chinese, Spanish, Arabic, Portuguese. ✅ Most users prefer content in their native language(s). ✅ French texts are on average 20% longer than English ones. ✅ Japanese texts are on average 30–60% shorter. 🚫 Flags aren’t languages: avoid them for language selection. 🚫 Language direction ≠ design direction (“F” vs. Zig-Zag pattern). 🚫 Not everybody has first/middle names: “Full name” is better. ✅ Always reserve at least 30% room for longer translations. ✅ Stress test your UI for translation with pseudolocalization. ✅ Plan for line wrap, truncation, very short and very long labels. ✅ Adjust numbers, dates, times, formats, units, addresses. ✅ Adjust currency, spelling, input masks, placeholders. ✅ Always conduct UX research with local users. When localizing an interface, we need to work beyond translation. We need to be respectful of cultural differences. E.g. in Arabic we would often need to increase the spacing between lines. For Chinese market, we need to increase the density of information. German sites require a vast amount of detail to communicate that a topic is well-thought-out. Stress test your design. Avoid assumptions. Work with local content designers. Spend time in the country to better understand the market. Have local help on the ground. And test repeatedly with local users as an ongoing part of the design process. You’ll be surprised by some findings, but you’ll also learn to adapt and scale to be effective — whatever market is going to come up next. Useful resources: UX Design Across Different Cultures, by Jenny Shen https://lnkd.in/eNiyVqiH UX Localization Handbook, by Phrase https://lnkd.in/eKN7usSA A Complete Guide To UX Localization, by Michal Kessel Shitrit 🎗️ https://lnkd.in/eaQJt-bU Designing Multi-Lingual UX, by yours truly https://lnkd.in/eR3GnwXQ Flags Are Not Languages, by James Offer https://lnkd.in/eaySNFGa IBM Globalization Checklists https://lnkd.in/ewNzysqv Books: ⦿ Cross-Cultural Design (https://lnkd.in/e8KswErf) by Senongo Akpem ⦿ The Culture Map (https://lnkd.in/edfyMqhN) by Erin Meyer ⦿ UX Writing & Microcopy (https://lnkd.in/e_ZFu374) by Kinneret Yifrah

Is THIS the best ad campaign ever? In 2015, Sport England challenged ad agency FCB Global to close the 2 million strong gender gap by getting women more active. The agency used the insight that women often feel 'fear of judgement' in exercise, to create the campaign 'This Girl Can'. The campaign is a rallying cry to women to get active in THEIR own way by replacing fear with a 'don't give a damn' attitude. This is shown with bold copywriting, relatable casting, REAL moments (the make-up smudged under the eyes, normal jiggling bodies, menopausal sweat, period cramps, tampon string hanging out your pants) and a true sense of female camaraderie. Since it's launch: - 3 million women were inspired to exercise as a direct result of seeing the campaign - 1000+ social media mentions each day - 37m views across social media - 500,000 active members in the This Girl Can community - Cannes Lions award The campaign is evidence that advertising can make great impact and drive change in many little corners of the world. THIS is the result of a clear brief, unifying insight and - in this case - a dedicated female creative team who truly 'understand' their audience. But more than that, it's the result of a LONG-TERM campaign that has been running for almost decade, and continues to re-engage the audience in various different ways, globally. I think there is such a short-term mindset in advertising nowadays. Mainly due to the fast-paced nature of social media, the need to 'go viral' and the economic need for performance marketing tactics to generate cashflow. But without the longer-term brand campaigns, we are missing the ability to build strong narratives and make REAL change in the world. And with that, stronger brand salience, brand love and LEGACY. This is an element of advertising that I fell in love with years ago. And an element that I see really defining which brands stand the test of time, an which fall apart years down the line.

𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗺𝗶𝘀𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝘁𝗼𝗽𝗶𝗰𝘀 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲. Because most people explain it from the inside out: policies, councils, standards, stewardship. But the business does not buy any of that. The business buys outcomes: → trustworthy KPIs → vendor and partner data you can actually use → faster financial close → fewer reporting escalations → smoother M&A integration → AI you can deploy without creating risk debt Most AI programs fail for boring reasons: nobody owns the data, quality is unknown, access is messy, accountability is missing. 𝗦𝗼 𝗹𝗲𝘁’𝘀 𝘀𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗶𝘁. 𝗗𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗶𝘀 𝗳𝗼𝘂𝗿 𝘁𝗵𝗶𝗻𝗴𝘀: → ownership → quality → access → accountability 𝗔𝗻𝗱 𝗶𝘁 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 𝘃𝗲𝗿𝘆 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝘄𝗵𝗲𝗻 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝗶𝗻 𝟰 𝗹𝗮𝘆𝗲𝗿𝘀: 1. Data Products (what the business consumes) → a named dataset with an owner and SLA → clear definitions + metric logic → documented inputs/outputs and intended use → discoverable in a catalog → versioned so changes don’t break reporting 2. Data Management (how products stay reliable) → quality rules + monitoring (freshness, completeness, accuracy) → lineage (where it came from, where it’s used) → master/reference data alignment → metadata management (business + technical) → access controls and retention rules 3. Data Governance (who decides, who is accountable) → data ownership model (domain owners, stewards) → decision rights: who can change KPI definitions, thresholds, and sources → issue management: triage, escalation paths, resolution SLAs → policy enforcement: what’s mandatory vs optional → risk and compliance alignment (auditability, approvals) 4. Data Operating Model (how you scale across the enterprise) → domain-based setup (data mesh or not, but clear domains) → operating cadence: weekly issue review, monthly KPI governance, quarterly standards → stewardship at scale (roles, capacity, incentives) → cross-domain decision-making for shared metrics → enablement: templates, playbooks, tooling support If you want to start fast: Pick the 10 metrics that run the business. Assign an owner. Define decision rights + escalation. Then build the data products around them. ↓ 𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝗮𝘀 𝗔𝗜 𝗿𝗲𝘀𝗵𝗮𝗽𝗲𝘀 𝘄𝗼𝗿𝗸 𝗮𝗻𝗱 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀, 𝘆𝗼𝘂 𝘄𝗶𝗹𝗹 𝗴𝗲𝘁 𝗮 𝗹𝗼𝘁 𝗼𝗳 𝘃𝗮𝗹𝘂𝗲 𝗳𝗿𝗼𝗺 𝗺𝘆 𝗳𝗿𝗲𝗲 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿: https://lnkd.in/dbf74Y9E

Designed as the lightest single-person flying vehicle, this air scooter represents a major shift in how we think about urban transport and personal movement. Would you use it? 📊 Why this matters: Reaches speeds of up to 100 km/h in open conditions Operates at low altitude with advanced stabilization systems Requires minimal take-off space compared to helicopters or eVTOLs Designed for single-rider efficiency and rapid point-to-point travel Potential use cases: emergency response, remote access, security patrol, and premium urban mobility 🌍 With cities projected to house 68% of the world’s population by 2050 (UN), vertical mobility is moving from concept to necessity. Ground congestion already costs major economies 2–3% of GDP annually due to lost productivity and delays. This isn’t just a new vehicle. It’s a signal that transport is shifting from roads to air corridors. The future of mobility is no longer horizontal. It’s vertical. via @supercarblondie #FutureOfTransport #UrbanMobility #AerialInnovation #SmartCities #MobilityTech #DisruptiveInnovation #Zapata #VerticalTransport #NextGenMobility

New! We’ve published a new set of automated evaluations and benchmarks for RAG - a critical component of Gen AI used by most successful customers today. Sweet. Retrieval-Augmented Generation lets you take general-purpose foundation models - like those from Anthropic, Meta, and Mistral - and “ground” their responses in specific target areas or domains using information which the models haven’t seen before (maybe confidential, private info, new or real-time data, etc). This lets gen AI apps generate responses which are targeted to that domain with better accuracy, context, reasoning, and depth of knowledge than the model provides off the shelf. In this new paper, we describe a way to evaluate task-specific RAG approaches such that they can be benchmarked and compared against real-world uses, automatically. It’s an entirely novel approach, and one we think will help customers tune and improve their AI apps much more quickly, and efficiently. Driving up accuracy, while driving down the time it takes to build a reliable, coherent system. 🔎 The evaluation is tailored to a particular knowledge domain or subject area. For example, the paper describes tasks related to DevOps troubleshooting, scientific research (ArXiv abstracts), technical Q&A (StackExchange), and financial reporting (SEC filings). 📝 Each task is defined by a specific corpus of documents relevant to that domain. The evaluation questions are generated from and grounded in this corpus. 📊 The evaluation assesses the RAG system's ability to perform specific functions within that domain, such as answering questions, solving problems, or providing relevant information based on the given corpus. 🌎 The tasks are designed to mirror real-world scenarios and questions that might be encountered when using a RAG system in practical applications within that domain. 🔬 Unlike general language model benchmarks, these task-specific evaluations focus on the RAG system's performance in retrieving and applying information from the given corpus to answer domain-specific questions. ✍️ The approach allows for creating evaluations for any task that can be defined by a corpus of relevant documents, making it adaptable to a wide range of specific use cases and industries. Really interesting work from the Amazon science team, and a new totem of evaluation for customers choosing and tuning their RAG systems. Very cool. Paper linked below.

Last week, I described four design patterns for AI agentic workflows that I believe will drive significant progress: Reflection, Tool use, Planning and Multi-agent collaboration. Instead of having an LLM generate its final output directly, an agentic workflow prompts the LLM multiple times, giving it opportunities to build step by step to higher-quality output. Here, I'd like to discuss Reflection. It's relatively quick to implement, and I've seen it lead to surprising performance gains. You may have had the experience of prompting ChatGPT/Claude/Gemini, receiving unsatisfactory output, delivering critical feedback to help the LLM improve its response, and then getting a better response. What if you automate the step of delivering critical feedback, so the model automatically criticizes its own output and improves its response? This is the crux of Reflection. Take the task of asking an LLM to write code. We can prompt it to generate the desired code directly to carry out some task X. Then, we can prompt it to reflect on its own output, perhaps as follows: Here’s code intended for task X: [previously generated code] Check the code carefully for correctness, style, and efficiency, and give constructive criticism for how to improve it. Sometimes this causes the LLM to spot problems and come up with constructive suggestions. Next, we can prompt the LLM with context including (i) the previously generated code and (ii) the constructive feedback, and ask it to use the feedback to rewrite the code. This can lead to a better response. Repeating the criticism/rewrite process might yield further improvements. This self-reflection process allows the LLM to spot gaps and improve its output on a variety of tasks including producing code, writing text, and answering questions. And we can go beyond self-reflection by giving the LLM tools that help evaluate its output; for example, running its code through a few unit tests to check whether it generates correct results on test cases or searching the web to double-check text output. Then it can reflect on any errors it found and come up with ideas for improvement. Further, we can implement Reflection using a multi-agent framework. I've found it convenient to create two agents, one prompted to generate good outputs and the other prompted to give constructive criticism of the first agent's output. The resulting discussion between the two agents leads to improved responses. Reflection is a relatively basic type of agentic workflow, but I've been delighted by how much it improved my applications’ results. If you’re interested in learning more about reflection, I recommend: - Self-Refine: Iterative Refinement with Self-Feedback, by Madaan et al. (2023) - Reflexion: Language Agents with Verbal Reinforcement Learning, by Shinn et al. (2023) - CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing, by Gou et al. (2024) [Original text: https://lnkd.in/g4bTuWtU ]

Growth marketing is in shambles. Paid is saturated and expensive. Building viral loops is near impossible. In response, Maven is going hard on community-led growth… I got my start in tech by doing highly data-driven, deterministic growth marketing. At Udemy, we started by hacking Facebook and YouTube to siphon users. Then, we heavily optimized our signup flow to encourage sharing. Next, we explored paid marketing and SEO. Ahh, the good old days. Today most of those channels are played out. AI is coming in hot but hasn’t yet emerged into a new growth opportunity (I did a talk with Andrew Chen and neither of us could come up with a great AI growth hack yet). So, Maven is investing heavily in community-led growth. It's a big shift for me. After spending years in data-driven marketing, I'm now a believer in "faith-based marketing." Community-led growth means investing in connecting your users to each other through informal and formal communities. - Informal: Creating pockets on social media where users talk about you - Formal: Building Slack or Discord communities for your users As connections between users grow, they feed off each other, get inspired, and feel stronger affinity for your brand. This transforms them into ambassadors. At Maven, our informal efforts happen by encouraging instructors and students to talk about us on social media. We create "themed" launches that instructors can rally around, provide assets to make posting easy, and coach them on how to succeed on social. Our formal efforts are two separate Slack communities - one for instructors and a new one for students - where we are slowly building a buzzing forum for people to trade notes and discuss their business needs. Why is community-led growth so attractive? It's incredibly efficient (minimal ad spend), scalable (networks can grow exponentially), and flexible (once established, communities can serve multiple goals). Out of our 5 go-to-market team members, 2 are focused on community-led growth. It's by far our biggest marketing bet. I’m also asking the team to take mallory contois’s Community-led growth course so they can learn the best practices of the space and then apply them to Maven. She leads community-led growth at Mercury, one of my favorite products and brands in tech right now. To join my team members in taking Mallory’s course, check it out here (it starts next week): https://bit.ly/4bz5bjZ This is a huge bet and shift in thinking for someone who used to live and die by data-driven marketing. I believe it's happening across the industry: community/brand/social marketers are gaining momentum, and I'm determined to make Maven best-in-class.