AI For Enhancing User Experience

AI is changing lives far beyond chatbots and data centers. What do you think about this the most inspiring examples? Helping children hear the world more clearly. According to the World Health Organization, more than 1.5 billion people globally live with some degree of hearing loss, and tens of millions are children. Without early intervention, hearing challenges can impact language development, academic performance, social interaction, and future career opportunities. Today, AI-powered hearing technologies are rewriting that story. 🔹 AI can distinguish speech from background noise in real time. 🔹 Advanced hearing devices can automatically adapt to classrooms, playgrounds, restaurants, and busy public spaces. 🔹 Machine learning algorithms continuously optimize hearing profiles based on individual listening patterns. 🔹 Edge AI enables many of these decisions to happen instantly on-device, reducing latency and improving the user experience. 🔹 Connected ecosystems allow hearing devices to integrate with smartphones, streaming services, and educational tools. For a child sitting in a noisy classroom, this isn't just a technology upgrade. It's the difference between hearing a teacher's instructions clearly or missing a critical part of the lesson. It's the difference between participating in conversations or feeling isolated. And this is just the beginning. As AI models become more sophisticated and power-efficient, we will see hearing devices evolve from simple amplification tools into intelligent personal assistants that understand context, prioritize important sounds, translate languages, and provide personalized support throughout the day. While much of the AI conversation focuses on trillion-parameter models, GPUs, and data centers, some of the most meaningful innovations are happening at the human level—helping children learn, communicate, and reach their full potential. The true measure of technology isn't how powerful it becomes. It's how many lives it improves. ❤️ This is AI at its best. #AI #ArtificialIntelligence #Healthcare #DigitalHealth #HearingTechnology #EdgeAI #MachineLearning #Innovation #TechForGood #Accessibility #FutureOfAI #Education #HealthTech #HumanCenteredAI #Technology

🤖 How To Design Better AI Experiences. With practical guidelines on how to add AI when it can help users, and avoid it when it doesn’t ↓ Many articles discuss AI capabilities, yet most of the time the issue is that these capabilities either feel like a patch for a broken experience, or they don't meet user needs at all. Good AI experiences start like every good digital product by understanding user needs first. 🚫 AI isn’t helpful if it doesn’t match existing user needs. 🤔 AI chatbots are slow, often expose underlying UX debt. ✅ First, we revisit key user journeys for key user segments. ✅ We examine slowdowns, pain points, repetition, errors. ✅ We track accuracy, failure rates, frustrations, drop-offs. ✅ We also study critical success moments that users rely on. ✅ Next, we ideate how AI features can support these needs. ↳ e.g. Estimate, Compare, Discover, Identify, Generate, Act. ✅ Bring data scientists, engineers, PMs to review/prioritize. 🤔 High accuracy > 90% is hard to achieve and rarely viable. ✅ Design input UX, output UX, refinement UX, failure UX. ✅ Add prompt presets/templates to speed up interaction. ✅ Embed new AI features into existing workflows/journeys. ✅ Pre-test if customers understand and use new features. ✅ Test accuracy + success rates for users (before/after). As designers, we often set unrealistic expectations of what AI can deliver. AI can’t magically resolve accumulated UX debt or fix broken information architecture. If anything, it visibly amplifies existing inconsistencies, fragile user flows and poor metadata. Many AI features that we envision simply can’t be built as they require near-perfect AI performance to be useful in real-world scenarios. AI can’t be as reliable as software usually should be, so most AI products don’t make it to the market. They solve the wrong problem, and do so unreliably. As a result, AI features often feel like a crutch for an utterly broken product. AI chatbots impose the burden of properly articulating intent and refining queries to end customers. And we often focus so much on AI that we almost intentionally avoid much-needed human review out of the loop. Good AI-products start by understanding user needs, and sparkling a bit of AI where it helps people — recover from errors, reduce repetition, avoid mistakes, auto-correct imported files, auto-fill data, find insights. AI features shouldn’t feel disconnected from the actual user flow. Perhaps the best AI in 2025 is “quiet” — without any sparkles or chatbots. It just sits behind a humble button or runs in the background, doing the tedious job that users had to slowly do in the past. It shines when it fixes actual problems that it has, not when it screams for attention that it doesn’t deserve. Useful resources: AI Design Patterns, by Emily Campbell https://www.shapeof.ai AI Product-Market-Fit Gap, by Arvind Narayanan, Sayash Kapoor https://lnkd.in/duEja695 [continues in comments ↓]

As consumers seek more individual experiences and interactions, companies turn to #AI to deliver 𝙥𝙚𝙧𝙨𝙤𝙣𝙖𝙡𝙞𝙯𝙚𝙙 𝙥𝙧𝙤𝙢𝙤𝙩𝙞𝙤𝙣𝙨 𝙖𝙩 𝙨𝙘𝙖𝙡𝙚. For some time now, companies have been trying to address customer needs through #personalization, using data and analytics to craft more relevant consumer experiences. Using improved analytics models, brands and retailers can better provide valuable offers to micro-communities wherever they want to engage. Meanwhile, #genAI enables marketers to create tailored content that is relevant to those groups. According to McKinsey & Company, marketers should unlock personalization at scale, by upgrading five areas of their #martech stack and processes: 1. Data: by improving #data collection and analysis, marketers can gain deeper insights into customer behaviors and preferences. 2. Decisioning: to develop personalized promotions and content through more robust targeting, companies can also benefit from refreshing their #decision engines with new AI models. 3. Design: a sophisticated design layer that oversees offer management and #content production helps manage the process, fueling both operational excellence and agility. 4. Distribution: achieving true, real-time personalization requires a sophisticated #marketing architecture that delivers seamless and consistent messaging to the right audiences at the right time on the right channel. 5. Measurement: to validate the #ROI of personalization efforts, rigorous incrementality testing, standardized performance metrics, and measurement playbooks are essential. Are there other capabilities or technologies required for marketers to better target promotions and deliver individual content?

Exciting AI + accessibility news for the blind community! Be My Eyes has partnered with OpenAI/ChatGPT to create a groundbreaking accessibility tool that uses AI. Users can point their phone at the scenery in front of them, and the phone will provide a visual description and speak back to them in real time for tasks such as hailing down a taxi, reading a menu, or describing a monument. This could be a gamechanger for many blind people, enhancing independence and making the world more accessible for them. As a deafblind woman, it excites me to see a new accessibility tool emerging. This innovation holds great promise, and I’m eager to witness how it empowers the blind community by offering real-time descriptions of their surroundings. Imagine the freedom and confidence this could instill in daily life for blind people, from navigating new places to simply enjoying the beauty of nature. However, blindness varies widely, so this tool might be more suitable for some people than for others. For example, there are still limitations for the deafblind community. As blindness is a spectrum, many blind people still have remaining vision. If they're deafblind like me, they need captions to have full access when receiving auditory information. I'm curious about what blind users will think of the tool once they start to adopt it. While this is a fantastic advancement, there’s always need for continued improvements and iteration. I also care deeply about preventing the harmful impacts of AI so I hope that this is also being thought about. Accessibility technology is crucial for the disability community. It not only enhances our ability to engage with the world but also promotes independence and equity. What are your thoughts on this new development? P.S. Here’s a cool video on it: https://lnkd.in/etfHehCh #Accessibility #AI #DisabilityInclusion

Memory & personalization might be the real moat for AI we’ve been looking for. But where that moat forms is still up for grabs: •App level •Model level •OS level •Enterprise level Each has very different dynamics. 🧵 ⸻ 1. App-level personalization Apps build their own memory & context for users. Examples: •Harvey remembering firm-specific legal knowledge for law firms •Abridge capturing patient conversations & generating notes for doctors •Perplexity building long-term search profiles for individual users ➡️ Most likely in vertical applications with focused use cases and domain-specific data. This is where Eniac Ventures is currently doing most of our investing ⸻ 2. Model-level personalization The model itself becomes personalized and portable across apps. Examples: •ChatGPT memory & custom instructions •Meta’s LLaMa fine-tuned on personal embeddings ➡️ Most likely in general-purpose assistants and broad horizontal use cases where user context needs to travel across apps. ⸻ 3. OS-level personalization Personalization happens at the OS level, shared across apps & devices. Examples: •Google Gemini native to Android •Apple (maybe) embedding Claude via Anthropic ➡️ Most likely in consumer devices and mobile ecosystems where platforms control distribution. ⸻ 4. Enterprise-level personalization Each enterprise owns and controls its own personalization layer for employees & customers. Examples: •Microsoft Copilot trained on company data •OSS models (LLaMa, Mistral) deployed on private infra with platforms like TrueFoundry •OpenAI GPTs fine-tuned & hosted in secure enterprise environments ➡️ Most likely in highly regulated industries (healthcare, financial services) where data privacy and compliance are critical. ⸻ Why it matters: Where memory & personalization “land” may define who captures AI value. Different layers may win in different sectors. Where AI memory lives may reshape who captures the next decade of value.

AI is creating new opportunities for individuals across all walks of life to excel in their roles.   In Hong Kong, Lawrence Fong, Director of Digital & IT at Cathay Pacific, used to move emails to a "Follow Up" folder and hope to revisit them later. Now, with Copilot, he responds faster, drafts speeches with ease, and his team can summarize proposals and meetings in minutes – not hours.   In Australia, Julian Ockford, a Rail Operations Planner at GHD, with dyslexia, faced extra challenges in writing. With Copilot, he’s now able to write with clarity and confidence while keeping his unique voice. AI is also helping employees with temporary disabilities, like those recovering from surgery, get back to work more quickly.   For Australia Post, AI is reimagining accessibility. Anthony Moufarrege, Diversity & Inclusion Coordinator, knows firsthand how workplace adjustments can make all the difference. He’s also seen Copilot break down communication barriers for those who are deaf or hard of hearing - enhancing both virtual and in-person interactions.   The question is no longer if AI will change the way we work - it’s how we will use it to create more opportunity for everyone.   Read more on Lawrence’s story here: https://lnkd.in/e4uTRgFf Read more on how GHD and Australia Post are leveraging AI for inclusion and empowerment here:

Your marketing playbook just expired. AI has rewritten every rule while most brands are still playing by 2019 strategies. The companies adapting fastest aren't the ones with bigger budgets or better tech teams. They're the ones who understand how AI has fundamentally changed customer behaviour. Here's what the winners are doing differently: 1. The New Search Landscape: SEO meets LLM Traditional keywords are the past. Conversational queries are everything. Example: REI shifted from keyword-stuffed descriptions to contextual content addressing specific use cases, increasing AI-summarised results visibility by 47%. Reality check: Google's AI Overviews now appear in nearly half of all search results. 2. AI Assistants as Gatekeepers Your brand must be recognised by AI as a category leader to enter consideration sets. Example: Best Buy organised product attributes to match natural customer questions, achieving 35% increase in organic traffic from voice searches. The shift: AI now filters options before consumers see them. 3. Attention Compression Consumer attention spans shrink as AI summarises everything instantly. Action point: Front-load your value proposition in all communications. The pattern: Customers want to digest information about products quickly, not hunt to understand what’s in it for them. 4. Hyper-Personalisation Without Creepiness AI enables true 1:1 marketing at scale, but only if you balance customisation with transparency. Example: Sephora's Skin IQ tool provides personalised skincare recommendations, driving 35% growth in skincare sales. The principle: Use preference-based content sequencing with full transparency about data usage. 5. Multi-Modal Content Strategy AI-driven consumers expect seamless experiences across text, voice, and visual channels. Example: Domino's "AnyWare" approach allows ordering through voice assistants, text, social media, and apps. The requirement: Build centralised content hubs ensuring consistent messaging across all channels. 6. The Human Advantage As AI handles transactions, authentic human connection becomes your competitive edge. Example: Lululemon's in-store community events resulted in 25% higher repeat purchase rates compared to online-only shoppers. The opportunity: Community-building programs generate 23% higher customer lifetime value. The brands that thrive won't be those with the most sophisticated AI tools. They'll be the ones that use AI to enhance human connection rather than replace it. Which of these shifts will you implement first? ♻️ Found this helpful? Repost to share with your network.  ⚡ Want more content like this? Hit follow Maya Moufarek.

Understanding user intent is foundational to improving any AI-driven product experience. In this tech blog, Udemy’s engineering team shares how they evolved their intent-understanding system by incorporating LLMs, ultimately improving the user experience of the Udemy AI Assistant. - For the Assistant to work well, the very first step is figuring out what a learner actually means so that the system can take the right action. Early versions relied on a lightweight sentence-embedding model: user messages were mapped to a vector space and matched against example utterances to identify the closest intent. This approach worked reasonably well at the start, but as the Assistant grew to support more features and nuanced intents, it began to struggle, leading to more misclassifications and weaker responses. - To improve accuracy, the team explored larger embedding models and eventually tested using LLMs directly for intent classification. While this LLM-only approach significantly improved understanding by leveraging full conversational context, it also came with higher latency and cost. The key was a hybrid strategy: use embeddings when confidence is high, and fall back to a smaller LLM only when intent is ambiguous. This delivered a strong balance between accuracy and efficiency in production. What stands out is how real-world constraints shaped the final design. In production systems, there are always trade-offs between quality, speed, and cost—and the “best” architecture is rarely the most complex one. Udemy’s approach is a useful reminder that combining lightweight methods with LLMs in the right places can meaningfully improve user experience without over-engineering the solution. #DataScience #MachineLearning #LLM #ProductAI #AppliedML #MLSystems #IntentUnderstanding #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/ga5JJuzN

Today, David Edelman joins the Outthinkers Podcast to discuss how to use personalization as a competitive advantage in the age of AI. David is a Harvard Business School Fellow and Executive Advisor at Boston Consulting Group (BCG). He spent over 30 years as a chief marketing officer at Aetna/CVS, as well as building consultancy businesses in digital and marketing transformation while with McKinsey, Digitas, and BCG. He now teaches marketing at Harvard Business School and serves as an advisor to top executives in startups, private equity, and larger enterprises. In the episode, we dive into David’s book, "Personalized: Customer Strategy in the Age of AI", coauthored with Mark Abraham, which helps executives learn how to put personalization at the center of their #strategy, accelerate growth, and capture their share of the value personalization creates. He shares:  -> How #personalization has radically shifted in the past decades to create unique value for customers, going beyond just marketing.  -> How #data and #AI play a pivotal role in this shift, governed by ecosystems where companies collaborate to deliver solutions rather than just products  -> The five promises businesses should focus on to seize the personalization advantage: empower me, know me, reach me, show me, and delight me  -> How to measure your solutions’ effectiveness with a custom Personalization index tool Listen here: https://lnkd.in/etxGUTEN

AI is helping visually-impaired athletes navigate races. Not in theory. In practice. Right now. Wearables like the Wayband from WearWorks have been tested in real race environments, including an attempted navigation of the New York City Marathon by blind runner Simon Wheatcroft, using vibration based cues instead of sight. Not flawless. Not finished. But a real step toward more independent movement. Researchers at MIT, Carnegie Mellon, and other Universities are developing AI systems that detect obstacles, identify safe paths, and translate the environment into non- visual guidance for people with low or no vision. Early-stage. But promising work that’s already reshaping what’s possible. Accessibility tools for color-blind athletes Focus on clearer visual design, contrast improvements, and better visibility of boundaries and signals. Practical upgrades. Real-world impact. This is what accessibility should look like. Not an afterthought. A direction. Because when technology increases, So does someone’s independence on the course, That’s not disruption. That’s progress. The kind that opens the starting line to more people, not fewer. And that same belief drives everything We’re building at Ruley, the E-Referee. Tech that makes understanding fair play universal.