Real-Life Examples Of AI Personalization In Retail

The search bar is dead. And most e-commerce platforms don’t even know it yet. After working closely with AI systems and recommendation engines, I’ve learned one thing: “Personalized shopping” was never truly personal. It was pattern matching. It was collaborative filtering. It was reactive logic pretending to be intelligence. Now we’re entering a different era. → From personalized to personal → From search-based discovery to proactive intelligence → From browsing endlessly to AI agents working for you This is agentic commerce. Traditional e-commerce makes you do the heavy lifting: Search → Filter → Scroll → Compare → Hope Agentic commerce flips the entire model: Describe what you want → AI delivers with context One of the most interesting examples I’ve seen is Glance. They are not building another shopping app. They’re building a contextual, agentic AI commerce layer powered by multiple specialised agents working together. Instead of one algorithm guessing what you like, Glance deploys multiple AI agents working for you in parallel: → Weather Agent analysing real-time climate and fabric suitability → Trends Agent tracking global shifts and micro-trends → Occasions Agent anticipating upcoming events → Physical Agent understanding your skin tone, undertones, and body type → Lifestyle Agent decoding your aesthetic preferences All coordinated by an orchestrator that synthesises everything into a unified styling strategy. That’s not basic personalization. That’s contextual intelligence. And the most powerful shift? You see yourself in the generated looks. Not stock visuals. Not generic models. You. Commerce becomes a conversation instead of a search box. From personalized to personal. AI agents working for you. Learning with every interaction. Refining your style instead of just tracking clicks. This is the rise of agentic commerce. #Glance #AICommerce #AgenticAI

Changing the Face of Luxury Retail As a long-time observer and participant in the world of luxury retail, I have been captivated by the transformative role that technology has played in personalizing the customer experience. Particularly in a city like Dubai, known for its luxury retail innovations, this blend of technology and personalized service is reshaping our industry. Let us take a moment to recognize some groundbreaking examples of technology meeting luxury retail: Burberry's Data-Powered Personalization: Burberry began transforming its approach to customer service in the early 2010s by leveraging data analytics. This initiative was aimed at creating more personalized shopping experiences, both online and in-store, by understanding and anticipating customer preferences. The North Face and IBM Watson's Collaboration (2015–2016): In a pioneering move, The North Face teamed up with IBM's Watson to create an AI-powered shopping assistant. This tool revolutionized how customers found products by intelligently responding to their needs and preferences, illustrating AI's potential in enhancing the retail experience. Gucci's Augmented Reality (2019): Gucci stepped into the world of AR with its app feature that allowed customers to virtually try on shoes. This innovative use of augmented reality brought a new dimension to online shopping, blending the convenience of digital browsing with a personalized touch. Innovations in Dubai's Retail Scene: Dubai, always at the forefront of luxury retail, has continually embraced new technologies. The Mall of the Emirates, with its 'Fashion Dome' and 'Luxury Wing', exemplifies this trend. The use of interactive directories, VR installations, and other digital innovations has set new standards for customer engagement in luxury shopping. These examples not only highlight the incredible potential of technology in enhancing the luxury retail experience but also signal a future where digital innovation and traditional luxury retail values coexist to create extraordinary customer journeys. Have you experienced these technological advancements in your shopping adventures? How do you envision technology further transforming luxury retail? Let me know in the comment

Singaporeans don’t need ChatGPT to know what to cook tonight. At FairPrice Group Punggol, your grocery cart can already answer in Hokkien.     This isn’t a lab demo. It’s happening today in Singapore’s most iconic retailer. FairPrice and Google Cloud just rolled out AI-powered shopping and workplace tools: - Smart carts that can guide shoppers through aisles, recipes, and promotions in multiple languages and dialects. - A “digital sommelier” suggesting pairings and wellness assistants nudging healthier choices. - Back-office tools where frontline staff generate ad creatives and campaigns 10× faster, at 100× lower cost. 👉 The difference? Most AI pilots die in PowerPoint decks. This one is live in aisle 5, solving real consumer pain while freeing up staff to do higher-value work. For SEA operators, the playbook is clear: - Localisation matters (voice, dialect, cultural nuance). - AI’s ROI shows up fastest where workflows are most repetitive (promos, recipes, FAQs). - Frontline empowerment is the multiplier — staff who can launch campaigns in seconds scale impact far beyond tech teams. According to DataReportal, 81% of APAC consumers now expect personalised, experience-driven retail. This rollout shows how quickly expectations can become reality. Disclaimer: All views expressed are personal insights. Content is based on publicly available sources (Retail Asia, Tech in Asia, ComputerWeekly.com, DataReportal) and operator experience. This is not financial advice.     #Retail #Ecommerce #DigitalTransformation #ArtificialIntelligence #ConsumerBrands   https://lnkd.in/gJ2Jzx4c

If you think gen AI in ads is just about creative automation—think again. One area I've been really bullish on at Disruptive Digital is using AI to create personalized creative at scale. Imagine being able to use AI to generate the right ad for the right user at the right time... Meta's new retail-specific AI tools should help reach that vision of enhancing both user experience and ad effectiveness, including: → Virtual try-ons using AI models to reduce friction → Background generation for Catalog ads → AI-powered product copy that actually converts I'm thoroughly excited about the virtual-try on being able to showcase AI models of different ages, genders and body sizes wearing your products. Why? When people see themselves represented in ads, they are more likely to buy. In fact, Meta is already seeing this is the case... By combining these with dynamic product sets, some brands saw up to a 25% drop in cost per purchase and 23% lift in ROAS! The takeaway? AI isn't just an efficiency lever—it's becoming a core creative partner.

Planning a vacation used to mean 20+ browser tabs, decision fatigue, and a messy spreadsheet. Last week, I gave an AI assistant a few simple prompts, and it returned a complete, tailored itinerary in minutes. We’re moving from systems we adapt to → to systems that adapt to us. This is the fundamental shift we're living through. For decades, every tech wave, from mainframes to mobile, promised more "personal" experiences. But they all delivered one-size-fits-all systems that we had to adapt to. We learned their commands, their menus, their workflows. AI-powered software can learn how to adapt to us. We’re moving from segment-based personalization to individual understanding. Instead of being put in a bucket like "frequent flyer" or "first-time buyer," AI-powered systems can understand you as an individual. They learn your preferences, anticipate your needs, and respond to your own words. This isn’t just a new interface; it’s a new relationship with technology. We're already seeing this create massive value: 📌  Netflix saves over $1B annually by personalizing recommendations that understand your viewing habits, not just what's popular. 📌  Amazon drives up to 35% of its revenue by recommending products based on your unique Browse and purchase history. 📌  Nike's "Nike By You" platform uses AI to let you co-design a shoe in real-time, creating a truly personal product. AI agents. Real-time context. Feedback loops. That’s the emerging stack behind this shift. And it’s not science fiction, it’s already here in how we shop, learn, travel, and manage our lives. But this brings new responsibilities. As we build these hyper-personal systems, ensuring they are fair, transparent, and accessible is paramount. The goal is to build more human-centric technology, and now we have the tools. It’s on us to build and use them wisely. For more on this, check out my article on the long journey to personalized tech: https://lnkd.in/g3kNVrr8

AI is getting more personal — and it’s changing how global brands connect with consumers. We’re entering an era where AI doesn’t just automate — it individualizes. From product design to marketing, personalization is becoming the new standard of brand experience. 🟣 Nike uses AI to tailor product recommendations and predict purchasing behavior through its SNKRS and Nike App platforms — driving a reported 40% increase in engagement. 🟢 Coca-Cola leveraged generative AI for its “Create Real Magic” campaign, allowing fans to co-create digital art and content, reaching over 2 billion impressions globally. 🔵 Starbucks uses its “Deep Brew” AI engine to personalize offers and store operations, contributing to a 10–15% lift in loyalty engagement. 🔴 Netflix attributes over 80% of viewership to AI-driven recommendations — proving how deeply personalization drives retention. What’s changing is not just the technology, but the intent: AI is no longer about scaling efficiency — it’s about scaling empathy. The brands that lead this shift are turning data into connection, algorithms into experience, and scale into trust. #ArtificialIntelligence #Personalization #BrandInnovation #MarketingAI #CustomerExperience #GenerativeAI via @tingle.ai #DigitalTransformation #Ai

NEW from me for Modern Retail: The application of artificial intelligence as a customer-facing element of physical stores is far from one-size-fits-all. In February, The Vitamin Shoppe opened an “innovation store” in New York City’s Upper East Side with a “Shoppe Advisor” touch screen. The AI-powered screen provides product information, wellness articles and videos, as well as information on in-store and online inventory. It aims to enable “more informed, interactive conversations throughout the shopping experience,” according to Retail Dive. Last summer, The Guitar Center Company launched Rig Advisor, an AI shopping assistant used by customers on the store floor to explore and compare gear. Customers can scan a QR code in the store and type in a question, and Rig Advisor will recommend products that are in stock at that specific location. Guitar Center CEO Gabe Dalporto told Modern Retail in December that Rig Advisor was built to fill the void when a customer walks into a store and the associates are too busy with other guests to help them. “This is basically everything an associate can do, on your app or on your mobile device,” Dalporto said. These two examples alone show how AI use cases for in-store shopping, in discovery, research and checkout, can vary. Not even considering behind-the-scenes use cases like supply chain tech and employee assistants, retailers are finding all sorts of ways to bring the technology into brick and mortar. These range from big kiosks to mobile app features, audio summaries or computer vision. Story below with Greg Carlucci of Gartner and Melissa Minkow of CI&T. https://lnkd.in/gYJF9CUg

Your next competitor isn’t faster or cheaper. They just know your customer better. And it’s not because they’ve hired a guru. They plugged in AI. Here’s why “AI-powered personalisation” is now the #1 growth lever in electronics DTC: 🔌 Best Buy now uses AI to serve personalised accessory bundles… inside a paid membership 🎧 Turtle Beach tripled their DTC revenue after piping first-party data into dynamic PDPs 🧠 71% of shoppers expect personalisation everywhere, and 76% get annoyed when it’s missing 💰 The ROI? Up to +30% marketing efficiency and +15% topline revenue lift And we’re not talking just about “You may also like” widgets anymore. We’re talking: 🧩 Conversational quizzes that recommend SKUs based on real needs (budget, specs, platform) 🖼️ AI-personalised email creatives where even the product image matches your last click 🛠️ Smart “configure & save” prompts at checkout that stack protection plans and accessories 🔄 Auto-replenishment flows based on usage data, not guesswork Meanwhile, some brands still send the same email to everyone. On a Tuesday. With 12 products. And a discount. AI doesn’t make things colder. It makes your brand feel smarter. And that’s what converts in 2025. #DTC #AI #ecommerce #personalisation #shopify #consumertech #retailtech #firstpartydata #marketplaces #digitalgrowth #conversionrateoptimization #dtcstrategy #channelmojo

I’m always on the lookout for 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝘀𝘁𝗼𝗿𝗶𝗲𝘀 of how generative AI (GenAI) is being adopted to enhance our everyday experiences, and Walmart’s latest innovations are a prime example. Building on an impressive CES event earlier this year, where they showcased their GenAI capabilities, Walmart is now rolling out these technologies in ways we can experience directly, just in time for the holidays. Here’s a look at how Walmart is leveraging AI to transform holiday shopping for us all:⁣ ⁣ - 𝐆𝐞𝐧𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐒𝐡𝐨𝐩𝐩𝐢𝐧𝐠 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭: helping customers with natural language conversations⁣ - 𝐆𝐞𝐧𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐇𝐞𝐥𝐩: helping with product comparisons and summary⁣ - 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭: designed to understand each customer’s unique needs, recognizing intent and handling actions like order management with minimal effort. It’s support that feels personal, right when you need it most.⁣ ⁣ Walmart is investing in their proprietary GenAI platform, Wallaby, crafted specifically for retail and customer interactions. Wallaby powers everything from personalized recommendations to enhanced customer support, optimizing these experiences for high demand. With beta testing already yielding strong results, I’m eager to see how this scales, both in terms of enhancing our experience as shoppers and in reducing operational costs for Walmart.⁣ #RetailAI #RetailGenAI #WalmartTech https://lnkd.in/e7R9thbf

While working with retailers through RSA, one of the patterns that surfaced was around everyday cooking behavior. The AI flagged a simple insight, that customers who purchased certain meat cuts, often cooked them with red cabbage. Based on that pairing, the system recommended a 50% discount on red cabbage to increase basket relevance and conversion. What happened next is where the case becomes interesting. The merchant team reviewed the recommendation and applied their own understanding of cost, margin, and local pricing dynamics.  Based on that context, they chose to increase the discount to 80%. The outcome was strong. The AI identified the habit and pairing logic and retailer applied human intelligence to pricing and execution. Together, it worked. For the merchant team, this was described as eye-opening. Not because the insight was complex, but because it showed how AI and human judgment can complement each other when roles are clear. At RSA, this is exactly the model we see working best: AI surfaces patterns at scale. Retailers decide how to act on them. That balance is what turns data into something genuinely useful on the store floor. #AppliedAI #RetailIntelligence #GroceryRetail