User Experience Innovation

Everything we know about online brand discovery is about to break. People aren’t browsing pages anymore. They’re asking AI. And that means the entire digital value chain is being rebuilt. Users are no longer starting with a Google search, clicking on links, and browsing websites. Increasingly, they’re turning to AI tools like ChatGPT, Perplexity, and Gemini to get direct answers, summaries, and recommendations. No links. No websites. No page one rankings. That shift is forcing a rethink of how visibility works online - and where SEO (Search Engine Optimization) fits. Traditionally, SEO has been about helping businesses appear higher in Google results. That meant optimizing websites to match search terms, earn backlinks, load quickly, and convert well once the user arrived. But that model depends on one thing: people clicking on search results. AI tools don’t work that way. When users ask ChatGPT for help - “compare project management tools,” “what’s the best CRM for startups,” “find me a cheaper alternative to X” - they’re not browsing. They’re expecting a direct, summarized answer in the chat itself. That’s where the biggest shift is happening. Data from Profound shows how user intent is evolving in AI environments: 1. Generative intent now leads at 37.5%. These are prompts where users ask AI to create or do something directly: write an email, summarize a document, recommend a product. 2. Informational intent - traditionally the most common in Google - is down to 32%. These are questions looking for facts or explanations. 3. Navigational intent - looking for a specific website - has collapsed from 32% in traditional search to 2% in AI. In chat, people don’t say “take me to X.com.” 4. Transactional intent has jumped 9x (to over 6%). That includes prompts like “buy running shoes,” “find deals on laptops,” or “compare prices.” 12% of prompts are conversational: things like “thanks,” “make it shorter,” or “can you add a joke?” - which play a subtle but growing role in shaping how AI interprets tone, preferences, and even brands. Why does this matter? Because all of this happens before a user visits a website - if they visit at all. In this new model, there’s no clear click path. No landing page. No bounce rate. That makes most current marketing KPIs and tools largely obsolete. A new wave of startups is helping brands adapt - decoding how AI models reference products and content. The focus has shifted from rankings to being included in AI-generated responses. The move from SEO to “AI visibility” is early, but accelerating. The question now isn’t: “How do I get more search traffic?” but “What do AI systems say about the brand - and is it even part of the answer?” Because soon, it won’t just be users asking. It will be AI agents deciding - on their / our behalf. Are you ready? Opinions: my own, Graphic source: Profound 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg

You don’t have to log in to the workstation — because your phone 𝗜𝗦 the workstation. That’s the headline that knocked me sideways at this year’s UGM. Epic is famously tight-lipped and private, but one of the things they do really well is listen, and understand healthcare's problems. I’ll often think "I wish Epic did X" — and a year later, they've added it. So UGM usually feels like déjà vu: things I’ve wanted, and now they're delivering. Useful, but not always surprising to me. 𝘛𝘩𝘪𝘴 𝘰𝘯𝘦 𝘴𝘶𝘳𝘱𝘳𝘪𝘴𝘦𝘥 𝘮𝘦. Demo’d inside Epic’s “Hospital Room of the Future,” the concept is so simple it feels obvious in hindsight (which is a sign of great innovation): 1️⃣ Take your Android phone (they said Apple support is in the works). 2️⃣ Plug it into a monitor with a keyboard and mouse — but no computer. 3️⃣ Full Epic Hyperspace appears on the monitor. You’re logged in. It’s your exact session. It's not running on the cloud. The phone isn’t just a phone. It’s THE workstation. No generic logins. No typing passwords with sweaty gloves. No hijacking someone else’s session. You plug in, you chart, you unplug, you go. This is what real innovation looks like: not just shiny tech, but reimagined workflows. And that’s rare for companies this big. It’s built for nursing right now. But I want it for the ER, too. (If anyone knows the tech behind this, I'd love to hear details)

Product managers & designers working with AI face a unique challenge: designing a delightful product experience that cannot fully be predicted. Traditionally, product development followed a linear path. A PM defines the problem, a designer draws the solution, and the software teams code the product. The outcome was largely predictable, and the user experience was consistent. However, with AI, the rules have changed. Non-deterministic ML models introduce uncertainty & chaotic behavior. The same question asked four times produces different outputs. Asking the same question in different ways - even just an extra space in the question - elicits different results. How does one design a product experience in the fog of AI? The answer lies in embracing the unpredictable nature of AI and adapting your design approach. Here are a few strategies to consider: 1. Fast feedback loops : Great machine learning products elicit user feedback passively. Just click on the first result of a Google search and come back to the second one. That’s a great signal for Google to know that the first result is not optimal - without tying a word. 2. Evaluation : before products launch, it’s critical to run the machine learning systems through a battery of tests to understand in the most likely use cases, how the LLM will respond. 3. Over-measurement : It’s unclear what will matter in product experiences today, so measuring as much as possible in the user experience, whether it’s session times, conversation topic analysis, sentiment scores, or other numbers. 4. Couple with deterministic systems : Some startups are using large language models to suggest ideas that are evaluated with deterministic or classic machine learning systems. This design pattern can quash some of the chaotic and non-deterministic nature of LLMs. 5. Smaller models : smaller models that are tuned or optimized for use cases will produce narrower output, controlling the experience. The goal is not to eliminate unpredictability altogether but to design a product that can adapt and learn alongside its users. Just as much as the technology has changed products, our design processes must evolve as well.

Stop thinking innovation only comes from slick new gadgets.⁣ Real innovation addresses and alleviates genuine human challenges.⁣ While the majority of companies are pursuing the next trend, the ones with the most foresight use their resources to build products that help people reclaim their dignity and become self-sufficient. This intelligent car seat from China serves as a wonderful illustration. It slides out on its own to assist elderly individuals in sitting down without any exertion. There’s no bending, no lifting, and no striving, just ease and accessibility.⁠ 3 Takeaways From This Innovation: Technology + Compassion = Meaningful Change⁠ ↳ Daily-life technology should eliminate hassles, not add to them⁠ ↳ Great solutions come from deeply understanding everyday struggles⁠ The Future Is Accessibility⁠ ↳ Older generations require mobility assistance more urgently than ever⁠ ↳ Inclusive products open up larger markets⁠ Careful Design, Not Just Coding⁠  ↳ A minor design adjustment can change how millions move⁠  ↳ Real innovation is measuring how many lives you better⁠ Innovation doesn’t mean starting from scratch. It means enhancing the quality of life for the most vulnerable.⁠ Would you consider implementing this technology for your elderly family members?⁠ 📌 Remember to revisit this post when you want to draw inspiration from it!

Is your cybersecurity actually slowing innovation? (Edition 2: February 2026 Founders Feature Series) Most leaders think they face a “security problem.”   Ranbir B. starts by challenging that.   You don’t just have a security problem.   You have an alignment problem. CyberCulture exists in that gap between ambition and reality:   You want AI, cloud, automation, remote work.   You also want regulators, boards, and customers to sleep at night. Here is how Ranbir, as CEO and vCISO, changes the system, not just the tools: 1) He reframes security as an operating constraint, not a last-minute hurdle.   Security policies, access controls, and vendor choices are mapped into how your teams actually deliver products and services, so security supports delivery instead of blocking it at the 11th hour. 2) He treats AI as a strategic asset with risk boundaries, not as a toy or a threat.   That means advising which data should never go into AI models, how governance should work, and where AI can safely reduce manual security noise without creating new blind spots. 3) He uses compliance as a lens for prioritization, not a checkbox exercise.   FFIEC, PCI DSS, SOC 2, HIPAA, GDPR all become organizing frameworks for risk decisions, budgets, and sequencing, so your limited time goes into the controls that actually matter to your regulators and your customers. 4) He invests in human readiness as a daily habit, not a once-a-year awareness day.   Training, phishing simulations, and vendor reviews are built into operations so that people and partners become extensions of your security posture, not recurring weak links. Watch the video to learn more about CyberCulture LLC. Ranbir's very helpful weekly LinkedIn Newsletter: "CyberTips by Ranbir": https://lnkd.in/gmrxGPfc TLDR: CyberCulture helps answer the question, "if security became a clear enabler instead of a recurring blocker, what would your organization finally feel safe to build?" -Dr. Kruti Lehenbauer #CyberSecurity, #PostitStatistics, #DataScience, #FounderFebruary Content obtained from: CyberCulture Website: https://lnkd.in/g8eq_pDJ Carousel and video created in Ryza Content Creator.

From GenAI to GenUI We’re witnessing a shift as significant as the leap from MS-DOS to graphical user interfaces. The AI era marks our latest upgrade in how we interact with technology. For decades, we designed for workflows and specific actions. Everything was deterministic. Behind every interface sat a flowchart, with logic carefully coded. The backend made decisions, and the frontend rendered them. This model worked because we could predict every path a user might take. With agents, this paradigm breaks down. Text alone isn’t sufficient anymore. Chat works for conversation, but interaction demands something more. We need to engage with agents, not just talk to them. Reasoning state and intent become critical factors in the exchange. LLMs can now generate UI, and this capability feels like natural progression. Model Context Protocol enables mini-apps to emerge on the fly, no longer bound by deterministic rules. This opens the door to genuine hyper-personalization. We’ve moved from designing screens to designing for outcomes. Agents now dynamically assemble workflows based on intent, available data, and accessible tools. The fact that agents can create interfaces without traditional designers and developers is revolutionary. We can finally shift from UI-centric thinking to truly user-centered experience design. This fundamentally transforms the designer’s role. We’re no longer pixel pushers or interface assemblers. The work of arranging buttons, spacing elements, and crafting individual screens can now be handled by agents. Instead, designers become architects of experience, defining the principles, guardrails, and intent that shape how agents respond. We set the boundaries of possibility, orchestrate the logic of interaction, and ensure coherence across dynamic, personalized experiences. Our canvas expands from static screens to adaptive systems. We design the intelligence behind the interface, the relationships between user needs and agent capabilities, the quality standards that govern generated UIs. We curate outcomes rather than outputs. The ability to adapt, reorganize, and respond to both user intent and application context is transformative. With reasoning and action combined, agents can generate dynamic artifacts that enable interaction, not merely conversation. What a time to be alive as a designer! 🫶 #ai

When empathy meets design, magic happens. Doug Dietz's story is proof. Discover how he did it. As product managers, we are constantly looking for ways to improve user experiences and create meaningful results. At GE Healthcare, Doug Dietz transformed the MRI experience for paediatric patients, providing a compelling example. The Problem Despite building a cutting-edge MRI scanner, Dietz noticed a young patient's tremendous anxiety while using it. This revealed a key flaw in the machine's design: it did not account for children's emotional needs. The Use of Design Thinking Dietz used design thinking to redesign the MRI experience. 1/ Empathise: He spoke with kids in daycare centres and sought advice from child life experts to understand their viewpoints. 2/ Define: It was shown that 80% of young children needed anaesthesia because they were afraid of the MRI process. 3/ Ideate: To generate creative ideas, a varied team comprising volunteers, hospital employees, and specialists from a nearby children's museum worked together. 4/ Prototype: Developed the "Adventure Series," which turned MRI rooms into spaceships and pirate ships. 5/ Test: The Children's Hospital of Pittsburgh piloted the updated experience, which resulted in notable enhancements. The Results ↳Patient satisfaction scores increased by 90% ↳The need for sedation dropped from 80% to 10% ↳Anxiety levels in children decreased, making it easier for them to remain still during procedures ↳The reduced need for anesthesiologists allowed more patients to be scanned each day, improving efficiency and reducing costs The Key Takeaways for Product Managers 1/ Innovation Is Driven by Empathy: A thorough comprehension of user experiences can reveal unmet requirements and stimulate game-changing solutions. 2/ Reframe the problem: Dietz switched from focussing on the machine to developing the complete patient experience. 3/ Holistic Problem-Solving: More thorough solutions result from addressing the user experience's emotional and functional elements. 4/ Collaborative Ideation: Including a range of stakeholders encourages innovation and reveals fresh viewpoints. 5/ Iterative prototyping: Creating and testing prototypes in real-world contexts to validate ideas and inform necessary refinements. 6/ Measurable impact: The redesign enhanced operational effectiveness and patient experience. Doug Dietz's case study highlights how effective design thinking leads to transformative solutions for challenging problems in healthcare and beyond. Dietz and his colleagues developed a solution that not only soothed children's anxieties but also enhanced operational effectiveness and medical results by prioritising empathy and rethinking the entire process. Your Turn: ↳ How have you applied design thinking principles in your projects?  Share your thoughts in the comments below! 👍 LIKE this post, 🔄 REPOST this to your network and follow me, Monica Jasuja

The Post-Smartphone Customer: Beyond the Screen 📲 Jony Ive and Sam Altman are betting against 17 years of digital strategy. Every marketing leader needs to pay attention. For over a decade, we built customer experience (CX) for the smartphone: tiny screens, quick taps, and app isolation. This new AI-powered companion is "contextual, continuous, and outcome-oriented." This is not just a hardware change; it is a fundamental disruption to marketing and CX design. 👉🏻 The shift is from "Screen-First" to "Experience-First." When the phone disappears, so does your app icon. Companies can no longer rely on visual real estate to win. The goal shifts from getting a tap to delivering an outcome seamlessly. Impact on Customer Experience: 1️⃣ The Zero-Click Economy: Your product interaction must be conversational and automated. If a customer needs to book a flight, the AI should handle it based on context ("I need a flight to Paris next week") without opening your airline app. Success is defined by an immediate, automated solution. 2️⃣ Brand Voice is Your New Interface: In a screenless world, your brand's personality, tone, and reliability are the interface. Marketers must invest heavily in defining the AI persona that represents their brand. The voice of your bank will handle sensitive transactions; it needs to be trustworthy and precise. 3️⃣ Data Strategy must be Proactive: The AI companion operates based on a continuous flow of context. Brands must design systems that feed relevant, real-time data to the AI before the customer asks. This requires moving beyond simple purchase history to predicting intent based on external context. For instance, a retailer needs to know the user's upcoming holiday plans to proactively suggest packing lists via the AI. This moment is the strategic window to define the winners of the post-mobile era. The best brands will redesign their entire service layer to integrate with an intelligence-driven companion. The losers will be stuck chasing clicks on a screen that no longer matters. #customerexperience #AI #futureofmarketing

Shampoo and conditioner bottles - they look the same, feel the same, and it's so easy to grab the wrong one in the shower. But in Japan, they've cracked this problem with a simple yet effective solution. Shampoo bottles there have dots or ridges on the sides, making it easy to distinguish them from conditioner bottles just by touch. It got me thinking about the research that must have gone into identifying this problem and coming up with a solution. I can imagine the team observing people in their daily routines, noting their frustrations with mixing up bottles. They probably interviewed a ton of people, asking about their hair-washing habits and pain points. And then, eureka! A small tactile difference could solve the problem entirely. No more confusion, no more wasted product. What I love about this solution is its simplicity. It's not some high-tech feature or expensive material. Just a small change to the packaging design, and bang - a better user experience for everyone. It's a great reminder that sometimes, the best innovations come from paying close attention to the little details of everyday life. By observing how people interact with products and listening to their frustrations, we can find opportunities for improvement that might seem small but can make a big difference.

The secret to company success is deep-customer understanding. And no one did it better than Gillette. How? By literally living with their customers and seeing how they use Gillette products. When Gillette wanted to expand to India, they realized that Indians didn't shave the same way as Americans. To understand Indian customers better, one of Gillette's executives, Chip Bergh, asked his team to go to India and live with the customers there. They wanted to observe how people shaved and how it fit into their lives. This concept is called ethnographic market research. One scientist from the UK thought they simply could talk to Indian men living nearby, but Chip said it wouldn't be enough. They needed to see and experience things firsthand. In India, the team discovered that many people in India didn't have access to a big sink with hot running water like in the West. They used a small cup of cold water to shave. This made shaving with regular razors difficult because the small hairs clogged the blades. So, they innovated a razor called the Gillette Guard: it had a single blade with a safety comb to prevent cuts and was easy to rinse. Perfect for Indian customers. This way, they could make razors that people needed and loved. The lesson: The key to unlocking consumer experience lies in understanding the consumer’s needs in-depth. #consumerresearch #customersatisfaction #startups #entrepreneurship