User Experience

🌻 Designing For Trust and Confidence in AI (Google Doc) (https://smashed.by/trust), a free 1.5h-deep dive into how trust emerges, how to design for autonomy, risk, confidence, guardrails — with all videos, slides and examples in one single place. Share with your friends and colleagues — no strings attached! ♻️ Google Doc (slides, videos, links): https://smashed.by/trust All slides (PDF): https://lnkd.in/dsq2BAJJ Full 1.5h-video recording: https://lnkd.in/d72b66Qa Zoom video backup: https://lnkd.in/dZJzCnZh Key takeaways: 1. Trust doesn’t emerge by default — it must be earned. 2. Trust means strong believing, despite uncertainty. 3. It’s when system is competent, predictable, aligned. 4. It also means transparency about its limitations / capabilities. 5. AI feature retention often plummets due to lack of confidence. 6. Trust isn’t linear: takes time to be built, drops rapidly in failures. 7. Most products don’t want users to fully rely on them → complacency. 8. Trust requires Understanding + Success moments + Habit-Building. 9. It thrives at intersection of Perceived value + Low cognitive effort. 10. We need to “calibrate” trust to avoid over-reliance and aversion. 11. Transparency only builds trust if users can verify the output. 12. User must feel in control: to validate, shape and override output. 13. Users have low tolerance for mistakes if AI acts on their behalf. 14. High-autonomy + High-risk → human intervention is non-negotiable. 15. Start with human oversight, increase autonomy as trust grows. 16. Perceived usefulness + ease of use are primary drivers of AI adoption.  17. Biggest risk to effort is a blank page → leads to open-intent paralysis. 18. Confidence builds through frequent use, not through “blind” trust. 19. Confidence scores are insufficient to help people make a decision. 20. AI might absorb cognition, but humans inherit the responsibility. Design patterns: 1. Link to specific fragments, not general sources. 2. Show the distribution of opinions, not a final answer. 3. Use structured presets to help articulate complex intents. 4. Rely on buttons/filters for a precise control or tweaking. 5. Show sandbox previews to help understand outcomes. 6. For high-stakes scenarios, design approval steps and flows. 7. Explicitly label the assumptions made during processing. 8. Replace confidence scores with actions, requests for review. 9. Embed AI features into existing workflows where work happens. 10. Proactively ask for context around the task a user wants to do. 11. Reduce effort for articulation with prompt builders/tasks. Recorded by yours truly with the wonderful UX community last week. And a huge *thank you* to everybody sharing their work and their findings and insights for all of us to use. 🙏🏼 🙏🏾 🙏🏾 ↓

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

𝗡𝗮𝗶𝘃𝗲 𝗥𝗔𝗚 𝘄𝗼𝗿𝗸𝘀 𝗶𝗻 𝗮 𝗱𝗲𝗺𝗼. 𝗜𝘁 𝗳𝗮𝗶𝗹𝘀 𝘁𝗵𝗲 𝗺𝗼𝗺𝗲𝗻𝘁 𝗿𝗲𝗮𝗹 𝘂𝘀𝗲𝗿𝘀 𝘀𝗵𝗼𝘄 𝘂𝗽. Embed → retrieve → generate looks clean in a notebook. Real requirements break it: → Questions whose answer is spread across many documents → Industry terms that embeddings get wrong → Bad chunks the pipeline never catches → Answers that live in how things connect, not in any single chunk → PDFs full of tables and images a text-only index cannot read These 5 architectures are how serious teams stay ahead in the agentic AI era: 𝟬𝟭 𝗛𝘆𝗯𝗿𝗶𝗱 𝗥𝗔𝗚 → Dense vectors find meaning. BM25 finds exact words. → Reciprocal Rank Fusion combines both ranked lists. → A safe baseline for almost every team. 𝟬𝟮 𝗚𝗿𝗮𝗽𝗵𝗥𝗔𝗚 → Pull entities and their relationships into a knowledge graph. → Retrieve subgraphs and community summaries, not chunks. → Best when the answer lives in how things connect. 𝟬𝟯 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 → A planner agent picks the right tool: vector, web, or SQL. → A reasoner agent keeps trying until the answer is solid. → Retrieval becomes a plan, not a single step. 𝟬𝟰 𝗖𝗼𝗿𝗿𝗲𝗰𝘁𝗶𝘃𝗲 𝗥𝗔𝗚 (𝗖𝗥𝗔𝗚) → Grade every retrieval before you trust it. → Correct → answer. Unclear → rewrite the query. Wrong → search the web. → This is what production RAG actually looks like. 𝟬𝟱 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗥𝗔𝗚 → One embedding model (CLIP, ColPali) for text, images, and tables. → One vector index. One multimodal LLM. → No more separate pipelines for PDFs with charts. I built a runnable example for each of the five patterns. GitHub link in the first comment. The best teams in 2026 do not pick one. They combine them — hybrid retrieval inside an agentic loop, with a corrective grader, over a multimodal index. Naive RAG is a starting point, not a finish line. That is why most enterprise GenAI projects stall at the demo. Which of these five becomes the default RAG stack in the next 18 months — and which stays a specialized tool?

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 ]

I can’t stop thinking about this. If you invest in your people from day 1, they’ll invest their talents in your company tenfold. It sounds obvious, but I’ve seen firsthand how often this gets missed. I joined companies and startups with zero training: - no documentation - unclear processes - no real onboarding I was expected to figure it out as I went, and honestly, it was brutal 😭 So here’s what *actually* sets people up for success: —— 1️⃣ What does a new hire need to know but feels awkward asking? Think back to your first 30 days. ↳ How do things actually work here? ↳ Where do I go for answers? ↳ What mistakes should I avoid early on? If the answers live only in someone’s head, that’s the gap. ✅ Document anything you explain more than once. —— 2️⃣ Where are people guessing instead of being guided? When training doesn’t exist, people improvise. ↳ Clicking the wrong thing ↳ Following outdated steps ↳ Copying work that isn’t quite right That’s how errors and rework happen. Tools like Tango make this easy by turning workflows into step-by-step guides. ✅ Record one common task this week and turn it into a reusable guide. —— 3️⃣ What tribal knowledge needs to be documented? You know it’s a systems problem when there are: ↳ Constant pings ↳ Repeating the same answers ↳ Little time for deep work ✅ Have your strongest team member document one core process they own. —— 4️⃣ Are you onboarding people or overwhelming them? More information doesn’t mean better onboarding. People need: ↳ Clear priorities ↳ Time to practice ↳ Space to build confidence ✅ Use a simple 30-60-90 day framework for all new hires —— 5️⃣ Are expectations clear or just assumed? When expectations are vague: ↳ People second-guess themselves ↳ Feedback comes too late ↳ Performance feels personal instead of fixable ✅ Check in early and often and schedule 20-minute check-ins with your manager or onboarding buddy in the first 8 weeks. —— When you give people the right tools, training, and support, you get: → Faster onboarding → More consistent processes → Fewer mistakes and support tickets → Happier, more confident employees 💙 You can’t expect people to thrive without setting them up properly. Set people up to win and they will 🫶 Do you agree? #TangoPartner

How the Humble American Diner Became the Stage for Brand Storytelling.... When we think of a diner, we think nostalgia. Neon lights, checkered floors, milkshakes, and the smell of fries drifting through the air. But today, brands aren’t just serving nostalgia, they’re serving story, theatre, and tangible brand experiences that make people stop, engage, and remember. Take Tesla’s Cybertruck “Tesla Diner & Drive-In.” It’s not just about the Superchargers. It’s about a retro-futuristic diner and drive-in theatre that transforms a functional stop into a multi-sensory moment. The diner becomes the stage where Tesla’s narrative, 'innovation meets Americana' comes alive. It’s tactile, it’s playful, and it’s a perfect example of a brand turning necessity into experience. Luxury and lifestyle brands are doing the same. CHANEL, SKIMS, and Jellycat have used pop-up diners to reinforce their brand DNA while giving consumers a physical, sensory connection. Think soft tactile displays, curated menus, neon signs echoing campaign aesthetics, and social moments built into every corner. The diner becomes a theatrical playground: consumers don’t just buy a product, they inhabit it. They sip, they snap, they share. So why does this work so well? It taps into the experience economy and Gen-Z’s appetite for moments that feel real, tangible, and shareable. A diner is both familiar and fantastical, it’s something people already know how to navigate, yet it can be transformed into a brand’s universe. Retro cues spark nostalgia, playful design encourages interaction, and the combination of taste, touch, and sight delivers multi-sensory engagement that static campaigns can’t match. They also offer collaboration potential; menus, merch, even limited-edition treats become vehicles for storytelling and co-creation. Social content writes itself: photo-booths, milkshake moments, and a drool inducing aesthetic, all make for irresistible feed fodder. And because diners are inherently communal, they naturally create micro-communities around the brand experience. For me, the power of the pop-up diner is that it’s more than just activation, it’s a physical manifesto of a brand’s values and aesthetics, inviting consumers to live the story, not just consume it. It’s theatre, tactility, and sensory engagement all rolled into one. Brands today aren’t just launching products, they’re designing worlds. So, are you still marketing products, or are you serving experiences with a side of storytelling? ________________ *Hi, I am Tim Nash. I help global brands build connected campaigns that resonate across every touchpoint. 🚀 #BrandExperience #ExperientialMarketing #RetailInnovation #GenZTrends #StorytellingInRetail #CulturalStrategy #BrandActivations #ExperienceEconomy Pictures courtesy of Glossier, Inc. / Skims / Chanel / Tesla / Benefit Cosmetics

This Stanford study examined how six major AI companies (Anthropic, OpenAI, Google, Meta, Microsoft, and Amazon) handle user data from chatbot conversations.  Here are the main privacy concerns. 👀 All six companies use chat data for training by default, though some allow opt-out 👀 Data retention is often indefinite, with personal information stored long-term 👀 Cross-platform data merging occurs at multi-product companies (Google, Meta, Microsoft, Amazon) 👀 Children's data is handled inconsistently, with most companies not adequately protecting minors 👀 Limited transparency in privacy policies, which are complex and hard to understand and often lack crucial details about actual practices Practical Takeaways for Acceptable Use Policy and Training for nonprofits in using generative AI: ✅ Assume anything you share will be used for training - sensitive information, uploaded files, health details, biometric data, etc. ✅ Opt out when possible - proactively disable data collection for training (Meta is the one where you cannot) ✅ Information cascades through ecosystems - your inputs can lead to inferences that affect ads, recommendations, and potentially insurance or other third parties ✅ Special concern for children's data - age verification and consent protections are inconsistent Some questions to consider in acceptable use policies and to incorporate in any training. ❓ What types of sensitive information might your nonprofit staff  share with generative AI?  ❓ Does your nonprofit currently specifically identify what is considered “sensitive information” (beyond PID) and should not be shared with GenerativeAI ? Is this incorporated into training? ❓ Are you working with children, people with health conditions, or others whose data could be particularly harmful if leaked or misused? ❓ What would be the consequences if sensitive information or strategic organizational data ended up being used to train AI models? How might this affect trust, compliance, or your mission? How is this communicated in training and policy? Across the board, the Stanford research points that developers’ privacy policies lack essential information about their practices. They recommend policymakers and developers address data privacy challenges posed by LLM-powered chatbots through comprehensive federal privacy regulation, affirmative opt-in for model training, and filtering personal information from chat inputs by default. “We need to promote innovation in privacy-preserving AI, so that user privacy isn’t an afterthought." How are you advocating for privacy-preserving AI? How are you educating your staff to navigate this challenge? https://lnkd.in/g3RmbEwD

Do you sometimes feel frustration, as you are building a product to get the management off your back, rather than address the users? Here are 6 ways to become user-centric again: 1) Prioritize in a transparent way This is a great place to start. If your backlog is prioritized based on data and potential opportunity, risk, and cost, it will be easier to put forth user-centric initiatives ahead of those that came from upstairs. At the very least, you will have a good basis for an educated discussion. 2) Utilize users' perspective using user stories and personas If your team understands the users and their problems, it will be easier to craft something great that will later appeal to the same users. Just keep up the empathy of creating something by people for other people, and not get some metric magically go up! 3) Make user feedback public If everyone in the company can see the themes that come from user feedback, it will be way harder to ignore it in favor of some corporate nonsense. Let those voices be heard by everyone! 4) Have the NPS and user ratings at the forefront The same goes for a single metric representing the general product sentiment. If the number is low or, worse, is going down and everyone can see that, the responsible Product Manager has to react. 5) Focus on your product goals Now, upstairs mandates might not be the only distraction you face when trying to improve your product. To survive them all, focus on one thing: your product goals. This will allow you to demonstrate you are doing what you are asked for and you can use user feedback and points 1-4 to pursue those goals. Thus, it's like killing 2 birds with 1 stone. However, you can also simply: 6) Have the confidence to say "No" Not all company/legal/management requests can be ignored. Sometimes changing the law or a wider company initiative will require you to comply and that is OK! However, there will also be times when someone will try to force your compliance. This is where you need to be confident, and exercise your Product Manager's independence, especially when there is no data to support a specific request. There you go! My 6 ways you can become a user-centric Product Manager. How about you? Do you address your users or your management first and foremost when developing your product? Sound off in the comments! #productmanagement #productmanager #usercentricity

Gen Z seems to have a trust problem. They don't trust institutions, corporations, or experts. Research shows: Gen Z consistently reports lower trust in institutions than any other generation. And it's framed as a flaw, like something went wrong with them. But they do trust. Just not the way older generations did. They trust the person who builds in public over the one who speaks from authority. A YouTuber who shows how to do it over a professor who lectures about it. A creator using it on camera over a brand claiming it works. A founder they've watched build over a CEO they've only read about. There's a pattern here. They trust people who show their work. Researchers call this "distributed trust". Instead of placing confidence in a single authority, they triangulate across sources. They cross-reference. They check the comments. They look for patterns before deciding what's real. They put their trust in people who can be held accountable, who show their proof of work, whose claims are verifiable. Authority alone is not enough anymore. This generation learned something older generations are still catching up to: transparency is the new credential. You can't buy their trust. You can't inherit it from your title. You earn it by building where people can see you. #rajshamani #figuringout

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