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

✍️ Golden Rules For UX Writing. With practical guidelines on how to avoid confusion and help people understand better ↓ ✅ Always write with respect, for people as smart as you. ✅ Write mobile-first: short, plain language, bite-sized chunks. ✅ Decide what to say, then find the shortest way to say it. 🚫 Avoid long buttons: use 2–4 words, max. 25 characters. 🚫 Avoid long links: at least 8 chars, max. 8 words (55 chars). ✅ Use sentence case by default, Title Case only for headings. ✅ Use progress anchors for long forms: “Next: Payment details”. 🚫 Don’t use placeholders as replacement for labels or hints. 🚫 Don’t hide critical details or guidelines behind a tooltip. 🚫 Don’t hide frequently used filters/nav behind a button. ✅ Front-load keywords in headings and text summary. ✅ Make people hunt for destructive buttons to avoid mistakes. ✅ Leave room for translation. Expect your text to grow by 40%. 🚫 Avoid more than 20 words/sentence, 50 words/paragraph. 🚫 Never mix 2+ type treatments (color, bold, indents, italic). Good writing is an incredible opportunity. Not only to help people get work done faster and with confidence, but also to build a strong and lasting relationships. To be charming when users get started. To help without a fuss when things go wrong. To show respect and sincerity, but also understanding and care when it’s needed. One little technique that has helped me is to imagine a real person speaking to the customer before I choose words to communicate something to them. I think about how they speak — from voice and tone to speed and intonation. How casual or formal they are dressed. What their personality is. And, most importantly, what traits, values, beliefs and principles they uphold. A product then needs to match that personality, and adapt tone based on user’s context. Once we have it, we write down all the questions users might have. We re-arrange them in order of importance and severity. We decide what to say, and find the shortest way to say it. And then we test, by reading out a piece of content loud. And if it doesn’t sound right, it doesn’t read right either. ✤ Content Design in Design Systems Atlassian: https://lnkd.in/eGpzQqm4 Amplitude: https://lnkd.in/eaB85T7n 👍 DHL: https://lnkd.in/eF494fkT 👍 Duolingo: https://lnkd.in/egCSX9At Girlguiding: https://lnkd.in/eZ8zMyC3 👍 Gov.uk: https://lnkd.in/ekRadXad Intuit: https://lnkd.in/eGyBUrZ2 👍 JSTOR: https://lnkd.in/eAnyrtcu 👍 MetLife: https://lnkd.in/evVE8sqf Progressive’s: https://lnkd.in/evx_8bzY 👍 Shopify: https://lnkd.in/eAKgEHNW Skrill: https://lnkd.in/e2HGTq4q 👍 Zendesk: https://lnkd.in/euxijT5m 👍 Wise: https://lnkd.in/eWk-Mvf9 ✤ Books – Strategic Writing for UX, by Torrey Podmajersky – Content Design, by Sarah Winters – Nicely Said, by Nicole Fenton, Kate Kiefer Lee – Everybody Writes, by Ann Handley – Conversational Design by Erika Hall – Writing Is Designing, by Michael Metts, Andy Welfle ✏️ [continues in the comments ↓ ] #ux #writing

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

Wearable Minis, packaging as lifestyle accessories. What was once driven by sampling, travel, and affordability is now being reshaped by a more culturally charged and design-led movement: wearable minis. +70% of Gen Z consumers discover beauty products through social platforms Heavily influenced by Asian markets,particularly South Korea, Japan, and China. beauty packaging is becoming portable, attachable, and visibly part of everyday styling. Products are no longer just carried; they are displayed, clipped, and worn. → From mini to wearable object. The traditional mini served convenience. The new mini serves identity and expression. In Asia, beauty products are increasingly designed for it. This shift transforms packaging into a hybrid between product and accessory, blurring the line between beauty and fashion. +Clip onto bags or belts +Function as charms or accessories +Integrate into keychains or tech cases +Be visible as part of an outfit → Social virality as a design driver. Platforms like TikTok, Xiaohongshu, and Instagram have accelerated this evolution. Their success is not only based on function, but on how they circulate digitally. A product that can hang from a bag or be styled in an outfit becomes content-ready by design. +Shareable +Collectible +Visually distinctive → Micro-luxury, always on hand. Wearable minis reinforce a new kind of luxury: constant proximity. Instead of being stored in a makeup bag, products are instantly accessible. Part of daily rituals on-the-go and embedded into lifestyle rather than routine +50% of consumers reapply beauty products during the day. This aligns with a broader shift toward fluid, mobile beauty habits, where reapplication and touch-ups happen throughout the day. While minis have always been an accessible entry point, wearable formats add a new layer: emotional durability. Consumers are more likely to: +Keep the object after use +Reuse or refill it +Develop attachment beyond the formula → The sustainability tension, reframed Wearable minis don’t eliminate the sustainability challenge, but they change the equation. Instead of disposable sampling formats, the focus shifts toward: Longevity through reuse. Higher-quality materials. Objects designed to be kept, not discarded. The challenge is to ensure that “wearability” doesn’t become just another layer of consumption, but rather a path toward designing minis with purpose and lifespan. Conclusion Miniature packaging is no longer just about size, it’s about presence. As the influence of Asian beauty culture continues to shape global markets, wearable minis are emerging as a new standard: products that move with the user, integrate into personal style, and exist beyond the beauty routine. Featured Brands: Beauty of Joseon Carolina Herrera Dott Entropy Frilca Kyoot Mari Maria OxygenCeuticals Tamburins The Crème Thryve Daily Gummies Tiny Wonder Touchland SOME BY MI WHIPPED #beautybusiness #beautyprofessionals #beautypackaging #genz

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

Stop pasting interview transcripts into ChatGPT and asking for a summary. You’re not getting insights—you’re getting blabla. Here’s how to actually extract signal from qualitative data with AI. A lot of product teams are experimenting with AI for user research. But most are doing it wrong. They dump all their interviews into ChatGPT and ask: “Summarize these for me.” And what do they get back? Walls of text. Generic fluff. A lot of words that say… nothing. This is the classic trap of horizontal analysis: → “Read all 60 survey responses and give me 3 takeaways.” → Sounds smart. Looks clean. → But it washes out the nuance. Here’s a better way: Go vertical. Use AI for vertical analysis, not horizontal. What does that mean? Instead of compressing across all your data… Zoom into each individual response—deeper than you usually could afford to. One by one. Yes, really. Here’s a tactical playbook: Take each interview transcript or survey response, and feed it into AI with a structured template. Example: “Analyze this response using the following dimensions: • Sentiment (1–5) • Pain level (1–5) • Excitement about solution (1–5) • Provide 3 direct quotes that justify each score.” Now repeat for each data point. You’ll end up with a stack of structured insights you can actually compare. And best of all—those quotes let you go straight back to the raw user voice when needed. AI becomes your assistant, not your editor. The real value of AI in discovery isn’t in writing summaries. It’s in enabling depth at scale. With this vertical approach, you get: ✅ Faster analysis ✅ Clearer signals ✅ Richer context ✅ Traceable quotes back to the user You’re not guessing. You’re pattern matching across structured, consistent reads. ⸻ Are you still using AI for summaries? Try this vertical method on your next batch of interviews—and tell me how it goes. 👇 Drop your favorite prompt so we can learn from each othr.

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 👇

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 ]

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

🚨New year, new scam on LinkedIn I had just hit post this morning when a “LinkedIn security” comment appeared under my post minutes after it went live. It looked official (see screenshot) It said my post violated policy and my account might be deactivated permanently. And it hits a very real fear for many of us: 👉 “What if my account gets taken down?” I clicked 🤦♀️🤦♀️🤦♀️ It took me to what looked like a LinkedIn page… but it wasn’t. If you click on Verify your identity, it leads to a phishing page designed to steal passwords. That’s how they hack into your accounts. 🚩 What to look out for ❌ Links that are NOT linkedin.com ❌ Threats like “immediate deactivation” ❌ Accounts called “LinkedIn security” with 0 followers ❌ Urgent / fear-based language ❌ Claim they represent LinkedIn in the comments If you see this appear under your posts: 1. Don’t click 2. Report the comment If you DID click and entered your password, do this immediately: 1. Change your LinkedIn password 2. Check that no new email was added: Settings → Account preferences → Email addresses 👉 Remove any email you don’t recognise 3. Check who’s logged into your account and sign them out: Settings → Sign in & security → Where you’re signed in 👉 End any session that isn’t you (“Sign out of all sessions” if unsure) 4. Turn on two-step verification: Settings → Sign in & security → Two-step verification 5. Take other necessary steps to secure your device such as running a scan Scammers are getting smarter, and they know active LinkedIn users care about protecting their accounts. Stay safe out there in 2026 💙 🔄 Save this for future reference and repost to spread awareness to your network