UX Design And User Adoption

The Brutal Truth About Consumer Trust in Home Care Why do some brands inspire trust effortlessly while others struggle to convince consumers? Home care isn’t like beauty or food, where customers instinctively check labels. For decades, legacy brands have relied on familiarity over transparency—building trust through big advertising spends rather than real ingredient disclosures. But that’s changing. Consumer trust is now shifting toward brands that disclose, educate, and take a stand. 1️⃣ The Parle-G Effect: Legacy Trust vs. New-Age Transparency For years, people have trusted brands like Surf Excel, Vim, and Harpic—not because they knew what was inside, but because they were always there on shelves and TV screens. This is the "Parle-G effect"—familiarity breeds trust. But today, trust is no longer inherited; it’s earned. The rise of brands like Kapiva (Ayurveda transparency), The Whole Truth (ingredient honesty) shows how modern brands build trust differently—by being upfront about what’s inside. 2️⃣ The Johnson & Johnson Shock: When Legacy Trust Breaks For decades, J&J was the gold standard for baby care. But lawsuits over talcum powder contamination with asbestos shattered consumer confidence worldwide. Even in India, brands like Mother Sparsh surged because young parents started reading labels—they no longer assumed safety just because a product was from a heritage brand. 3️⃣ The Patanjali vs. FSSAI Scandal: Why Trust Must Be Backed by Proof Consumers initially believed in Patanjali’s “natural” positioning. But repeated quality violations (like the recent FSSAI crackdown on misleading claims) eroded trust. The lesson? Trust cannot be built on slogans alone. If a brand claims toxin-free, natural, or safe—it must prove it consistently. 4️⃣ The Decathlon & Ikea Strategy: Trust Through Radical Transparency Decathlon shares detailed product breakdowns—how much polyester is used, where a product is made, and even the carbon footprint. Customers trust them because they don’t have to “guess” what they’re buying. Ikea lists every material, every environmental impact, and even assembly instructions upfront. No surprises. Just facts. In home care, Koparo is taking the same approach—putting ingredients front and center. Not just saying "toxin-free," but explaining why certain ingredients matter for better or worse (like the bioaccumulation of harmful chemicals in traditional cleaners). So What’s Next for Consumer Trust in Home Care? ✅ Brands that educate will win over brands that advertise. ✅ Ingredient transparency will become a non-negotiable (just like food labels). ✅ Consumers will demand not just safe products—but proof of safety. At Koparo, we’re all in on radical transparency. No vague claims. No marketing gimmicks. Just home care that’s safe, effective, and backed by science. The real question is—do you know what’s inside your cleaning products? #ToxinFree #Koparo #HomeCareRevolution 🚀

One of the single most important issues is the impact of AI on human thought. This extensive and very interesting paper dives deep. I fully agree with its thesis that “Ultimately, harmonious coexistence with AIs will depend on revaluing cognitive diversity, designing interfaces that foster reflection, and making AI an augmentative partner of human thought, not its replacement.” Some key insights: ⚠️ Cognitive shortcuts weaken reasoning. Heavy reliance on AI showed a strong negative correlation with critical thinking, with cognitive offloading as the key driver. 🌍 Standardization narrows cultural and cognitive horizons. Generative systems trained on Anglo-American corpora nudged writers worldwide toward Western norms, reducing local nuance and expression. Algorithmic personalization reinforced echo chambers, creating “closed-circuit thinking” where diversity of perspective is dulled. 🎭 Manipulation risks bypass human reasoning. AI systems can exploit biases, tailor hypernudges, and generate synthetic personas—shaping decisions without awareness or consent. 🛡️ Safeguards must protect autonomy. The paper highlights transparency through internal logs, bans on subliminal techniques, neurorights for cognitive privacy, and “cognitive hygiene” education. These measures aim to secure epistemic plurality before opacity and automation erode mental sovereignty. 🚀 Design AI as a copilot, not a pilot. Positive potential emerges when AI is built to extend human cognition rather than replace it. Keeping humans “in the loop” ensures that AI serves as an augmentation tool instead of a substitute for thought. 🧑🏫 Pedagogy keeps humans thinking. Thoughtful integration in education—where AI outputs are paired with active reasoning exercises—preserves critical faculties. Training users to engage, verify, and question helps prevent erosion of independent judgment. 🤝 Interfaces should invite reflection. Instead of providing instant answers, AI can be designed to pose questions back to the user, prompting active engagement. This preserves cognitive effort while still supporting exploration and discovery. 🌱 Flourishing requires cognitive diversity. A healthy AI–human partnership means valuing diverse perspectives, fostering reflection, and designing systems that amplify—not homogenize—human creativity and judgment. ⚖️ Human–AI balance redefines collaboration. Individuals using AI performed at the same level as human-only teams, but AI-enabled teams dramatically outperformed both—showing that the deepest gains come from synergy, not substitution. 🌟 Augmentation as the true measure of success. The future of AI will not be decided by raw efficiency but by whether it strengthens or weakens human autonomy. Systems that expand reasoning, preserve diversity, and nurture reflection will be the ones that truly advance human flourishing.

If you've followed my latest posts, you'll already know that machines are getting faster, more precise, more powerful. Yet when it comes to truly understanding us (our tone, emotions, intent...) they still fall short. This is the central theme we explored in our latest research report: “When machine precision meets human intuition.” This is the frontier of Human-Machine Understanding: the ability for machines not only to process data, but to sense, interpret, and adapt to human context in real time. Why does this matter? Because as #AI and #robotics become more embedded in our daily lives, the difference between a system that simply executes tasks and one that understands the person in front of it will be the difference between frustration and trust. Between inefficiency and real value. Think of a doctor supported by AI that senses stress and adapts its interface accordingly. Or a collaborative robot that adjusts to a worker’s gestures and fatigue. Or a customer interaction that feels seamless because the system has understood not only what you said, but how you meant it. There is still a lot to improve in this facet of human-machine collaboration. But this topic has extraordinary potential and equally important ethical, safety, and trust questions to solve. I invite you to explore the full report here: 👉 https://lnkd.in/e2sagdCR Franck Greverie Alexandre Embry Kary Bheemaiah Ali Shafti Sally Epstein Keith Williams Tim Ensor Matthew Rose

brands initiate less than 1% of conversations about them online… the other 99%? that's where your reputation gets built (or destroyed). Brandwatch released their state of social 2026 report (analysed 910 million mentions ) and the findings are eye-opening for brands still treating social as a megaphone: 𝟭/ 𝘁𝗿𝘂𝘀𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗮𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 hidden fees mentions up 40%. de-influencing up 79%. boycott calls surged 95% in h1 2025. consumers aren't just leaving bad brands - they're actively warning others away (read: your futurec cusotmers). fired a customer on social? 100k people see it. pricing changed overnight? your ICP is already discussing alternatives in reddit threads you'll never see. the flip side? brands getting transparency right are turning customers into advocates. real examples. honest pricing. actual behind-the-scenes. that's what's driving word-of-mouth now. 𝟮/ 𝗔𝗜 𝗻𝗲𝗲𝗱𝘀 𝘁𝗼 𝗮𝘀𝘀𝗶𝘀𝘁 𝗵𝘂𝗺𝗮𝗻𝘀, 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲 𝘁𝗵𝗲𝗺 people want AI that solves problems without compromising trust. the report shows rental car AI falsely flagging damage. customer service bots that can't resolve real issues. but when AI delivers genuine value? sentiment follows. the key is positioning it as empowerment, not replacement. because in b2b, that "human touch" in your customer success motion? still matters more than your automation metrics. 𝟯/ 𝘁𝗵𝗶𝘀 𝗼𝗻𝗲 𝘄𝗮𝘀 𝘀𝘂𝗿𝗽𝗿𝗶𝘀𝗶𝗻𝗴 𝘁𝗼 𝗺𝗲: 𝗺𝗶𝗰𝗿𝗼-𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲𝗿𝘀 𝗮𝗿𝗲 𝗼𝘂𝘁𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗰𝗲𝗹𝗲𝗯𝗿𝗶𝘁𝘆 𝗽𝗮𝗿𝘁𝗻𝗲𝗿𝘀𝗵𝗶𝗽𝘀 authenticity mentions in influencer conversations grew 66%. finance influencer discussions were 40% negative (scams, misleading promotions). but brands working with micro-influencers? 24% positive sentiment. for b2b: your customer advocates, your power users posting on linkedin, your employees sharing real stories - those are your influencers. not the growth guru with 500k followers selling a course. the report's clearest finding? customers control the conversation now. your job isn't to dominate it - it's to listen to the 99% and show up where it actually matters. what's one way you're planning to rebuild trust with your audience in 2026? ps: full report here 👉 https://lnkd.in/ghu3B4qx

Data privacy and ethics must be a part of data strategies to set up for AI. Alignment and transparency are the most effective solutions. Both must be part of product design from day 1. Myths: Customers won’t share data if we’re transparent about how we gather it, and aligning with customer intent means less revenue. Instacart customers search for milk and see an ad for milk. Ads are more effective when they are closer to a customer’s intent to buy. Instacart charges more, so the app isn’t flooded with ads. SAP added a data gathering opt-in clause to its contracts. Over 25,000 customers opted in. The anonymized data trained models that improved the platform’s features. Customers benefit, and SAP attracts new customers with AI-supported features. I’ve seen the benefits first-hand working on data and AI products. I use a recruiting app project as an example in my courses. We gathered data about the resumes recruiters selected for phone interviews and those they rejected. Rerunning the matching after 5 select/reject examples made immediate improvements to the candidate ranking results. They asked for more transparency into the terms used for matching, and we showed them everything. We introduced the ability to reject terms or add their own. The 2nd pass matches improved dramatically. We got training data to make the models better out of the box, and they were able to find high-quality candidates faster. Alignment and transparency are core tenets of data strategy and are the foundations of an ethical AI strategy. #DataStrategy #AIStrategy #DataScience #Ethics #DataEngineering

We are no longer living in a purely human society. We are entering a hybrid system where humans and machines continuously interact and influence each other. Where does this system evolve? In a new perspective piece, we brought together leading experts to address this using the lens of evolutionary game theory. We outline six core research directions: 1) Evolution of social behaviour. How cooperation, fairness, and trust evolve in mixed human–AI populations. 2) Machine culture. How AI systems generate, transmit, and select cultural traits. 3) Language–behaviour co-evolution. How LLMs, by framing decisions, reshape preferences, norms, and actions. 4) Delegation dynamics. How control, responsibility, and agency shift between humans and machines. 5) Epistemic pipelines. How different cognitive processes generate human vs AI judgments, and how these co-evolve. 6) AI–regulation co-evolution. How firms, institutions, and users strategically shape—and are shaped by—AI development. We hope this framework sparks new work at the intersection of AI, behaviour, and society. * Joint with The Anh Han, Joel Leibo, Tom Lenaerts, Iyad Rahwan, Fernando P. Santos, Matjaz Perc Paper in the first comment

You paid $400,000 for NetSuite. And you’re still closing the books in Excel. Be honest—is it really a “training issue”… or is it an adoption failure? Two years after implementation, I still see teams exporting data, rebuilding reports manually, and ignoring the automation they already own. Dashboards untouched. Workflows unused. Features collecting digital dust. This isn’t an ERP problem. It’s a behavior problem. The companies seeing real ROI don’t have the fanciest setups. They treat NetSuite like a living system—not a trophy purchase. Here’s how to fix it: 1���⃣ Audit what you’re not using. Most teams leverage ~30% of what they’re paying for. That’s a Tesla stuck in first gear. 2️⃣ Tie features to business pain. Faster close. Cleaner revenue recognition. Real-time cash visibility. If a feature isn’t solving a weekly headache, it won’t get adopted. 3️⃣ Train for outcomes, not clicks. Don’t show people where to click. Show them how this saves 10 hours a month. 4️⃣ Assign a real owner. Not IT. Not your implementation partner. An internal champion with authority and accountability. 5️⃣ Measure ROI relentlessly. Days to close. Error rates. Report time. Subscription bloat eliminated. What gets measured gets used. 6️⃣ Optimize quarterly. Your business evolves. Your ERP should too. That $400K implementation? They’re now spending $180K/year on manual workarounds and extra headcount. Meanwhile, the teams who commit to adoption cut close time by 40–60% and eliminate redundant tools in year one. The system you already own can likely solve 80% of what you’re trying to fix with more software. You don’t need another tool. You need to LEARN to use the one you bought. What’s one NetSuite feature you paid for—but never fully implemented? Charles #TheBaldNetSuiteWhisperer #ERP #CFO #PrivateEquity

For years, UX and HCI work centered around performance metrics, clicks, errors, time on task. Useful, yes, but they only skim the surface. They tell us what people did, not why they did it or how they felt. And emotion shapes everything. Stress can make a simple interface feel confusing. A small delay feels worse when someone is anxious. Confidence makes complex flows feel easy, while frustration makes even the simplest task feel impossible. When we measure emotion alongside behavior and perception, we finally see how people actually experience technology. Getting that full picture means looking at multiple layers at once. We pay attention to what users say they feel, the small facial cues they show without realizing it, the way their bodies react automatically, and the subtle behavioral patterns hidden in how they move, scan and navigate. Subjective ratings tell us how people frame their own experience. Facial patterns reveal early signs of confusion or relief. Physiological signals like arousal, cognitive load and micro-shifts in attention give us moment-by-moment emotional truth. And interaction traces, cursor paths, gaze shifts, hesitation, scrolling, show emotional friction at scale. In fact, the real insight comes from merging these signals, not treating them separately. Together, they create an emotional narrative that explains breakdowns, hesitation, engagement and delight far better than task metrics alone. Without emotional data, we miss early frustration, hidden cognitive load and the reason two users can have the same performance outcome but completely different experiences. And different projects call for different emotional toolkits. Sometimes self-reports and interaction logs are all you need. Other times you need deeper physiological measures or more detailed behavioral observation. Emotion is highly context dependent, so our methods have to be flexible. If you want to dive deeper into the full article and methods, you can read more here: https://lnkd.in/emeh_SGf

THE NEW COGNITION, IN A NUTSHELL AI is a form of technical cognition. I don't mean that metaphorically. Cognition, as defined by N. Katherine Hayles, is a “process that interprets information by connecting it to meaning within a particular context.” It's a simple concept, but one that I have found key to understanding our current epistemic moment. Here’s how this cognitive process plays out: When you prompt an AI, you provide it with information. The system interprets that information as input and generates an output—a response that assumes meaning in relation to its use, context, or reception—in this case, the human who reads it. Technologies have long connected information to meaning, but through fixed rules. A clock, for instance, links the position of its hands to a specific time of day. That output is predetermined, so no interpretation takes place. Cognition, by contrast, involves the selection of a meaning from among multiple possibilities. The meaning it produces is structured, but not fixed or predictable. This interpretive flexibility distinguishes cognition from mechanical processing. Both humans and AI systems enact this cognitive process: both interpret information in contexts that connect to meaning. Critically, this parallel does not imply equivalence. While both enact the same basic cognitive function, the material architectures of human and machine cognition are profoundly distinct, and so are their capacities. AI systems draw on statistical correlations across massive datasets to generate outputs. Their cognition excels at pattern recognition, probabilistic inference, and high-speed synthesis across vast stores of information. Human cognition engages in slower, embodied processes. It integrates affect, memory, intention, and ethical reasoning. It tolerates contradiction, navigates ambiguity, and draws on lived experience to produce meaning that is temporally layered, emotionally charged, and socially situated. Once we recognize both human and AI systems as cognitive agents, we can begin to understand their interaction as recursive and co-constitutive. A human prompt becomes the AI’s input. The AI’s output becomes the human’s new input. Each interpretation shapes the next. Meaning emerges across this exchange. This recursive dynamic forms a cognitive assemblage—a complex system of diverse cognitive agents whose interpretations co-produce meaning over time. Such assemblages may include other technical systems, other humans, and even organic forms of cognition—animal, biological, or environmental. Meaning, in these systems, is emergent. It feeds back into the assemblage, shifting and adapting with each interaction. In this process, cognition becomes relational, recursive, and distributed. These are the ingredients of a new epistemology—one no longer anchored in the sovereignty of the single human knower, but in the recursive, relational, and uneven emergence of meaning within complex, co-constituted systems.