Artificial Intelligence in Retail

The teen models in Mango's latest campaign have perfect poses, perfect lighting, and one small detail: they don't exist. This Spanish fashion giant launched their Sunset Dream collection using entirely AI-generated models across 95 markets. Not a single human model was photographed. Here's how they did it: 📌 Took photos of real clothes on display stands 📌 Fed these pictures to their AI system 📌 Created model images in minutes 📌 Rolled out everywhere at once The business impact is massive. Fashion brands typically save 60-80% by leveraging AI photoshoots. Those savings can now fund innovation, better pricing, or faster expansion. But cost isn't the real story here. Speed is. While competitors wait weeks for campaign photos, MANGO creates, tests, and launches collections in days. No weather delays. No scheduling conflicts. No reshoots. This wasn't luck. Since 2018, Mango has built 15 different AI platforms across their business. They've been preparing for this moment. The result? Their 2024 turnover reached 3.3 billion euros in 2024, growing 7.6% from 2023. What makes this significant is that Mango proved AI-generated content can drive real sales. Their teen customers embraced these virtual models without hesitation. Fashion's biggest players are watching. If Mango's approach succeeds long-term, traditional photography could become a thing of the past for e-commerce. The brands that adapt now will set industry standards. Those that don't might find themselves competing against companies moving at AI speed. Which fashion tradition do you think AI will disrupt next?

According to Deloitte Asia Pacific’s latest report, The Future of Commerce: Agentic Shopping in Asia Pacific, the region is expected to drive two-thirds of the world’s new retail sales over the next five years, powered by 4.3 billion shoppers, 18 megacities, and the world’s fastest-growing middle class. With nearly 75% of consumers already using AI to discover and compare products, agentic commerce, where intelligent agents act on behalf of shoppers, is reshaping how consumers buy and how brands compete. For leaders, the message is clear: success in this era will depend on trust, transparency, and strong AI foundations. Key insights from the report: ➡️ Adoption of agentic AI among Asia Pacific consumer businesses will rise from 29% today to 76% within two years ➡️ Over half already have live AI implementations across key functions such as marketing, sales, and customer support ➡️ Only 30% say more than 40% of their AI initiatives reach production, highlighting execution challenges ➡️ 90% of retail executives expect AI to surpass traditional search by 2026 By 2030, 25% of global e‑commerce sales could be influenced by AI agents Explore how businesses can lead in the age of intelligent, agent-enabled commerce: https://lnkd.in/gTCYthtN Vanessa Matthijssen Anand Ramanathan Vivek Sharma Robert Hillard Stuart J. Yuki Kuboshima Nobuo Okubo 

The branded webshop is DYING ☠️ The last couple of years, we’ve seen an explosion of (luxury) brands going online, investing tons into a digital “flagship” that stands out in a sea of sameness. As a business consultant, I vividly remember recommending brands to make the jump from offline to “multi-channel”. 🗣️: “Go digital or die.” But in 2025, I’m afraid a new reality is approaching. The branded webshop may be on its way out. 📊 Let’s look at some numbers: • In 2025, there are +28mio (!) e-com stores worldwide… • In the USA alone, up to 3.5 million compete for the attention of the consumer • The top destinations for shoppers remain multi-brand. In the US, Amazon alone captures nearly 40% of online retail. • ONLY 15% of (global) online shoppers prefer to buy directly from brand sites ➡️ Online sales are consolidating around mega-platforms. 🇨🇳 And in China it’s even clearer. Nearly ALL online shopping happens on Taobao, Tmall, WeChat, JD (+80% of all online revenue). The branded webshop plays a marginal role (if any) over there. It might become like that here too. 🤔 Why? Shoppers are overwhelmed by choice, on the one hand. They DEMAND convenience, elaborate/trusted product information and (price) transparency on the other hand. And note that, the rise of mobile (& social) commerce is making this trend even more daunting to monobrand e-shops. “Only” 50% of e-commerce happens on phones today, with its share growing exponentially YoY. More mobile sales = less monobrand. 🌎 However, the West may never follow China’s path! Enter AGENTIC COMMERCE.. ..where AI agents shop on our behalf, searching across platforms and brands to find the best deals and experiences. 🤖 Imagine, you tell ChatGPT your skincare concerns and it not only assembles your personal 7/8-step skincare routine but also buys it from the different platforms for you. ShopGPT. Browsing sites, reviews, return policies, prices, promotions, … 100x faster than you. Checking out for you. Delivered to your door. 🤯 In this world (which we are MAX. 18 months away from), what criteria will be the defining factor? How will our products be chosen over the other brand’s items? Access for the LLM’s to checkout on the customer’s behalf? Price? Delivery promise? Reviews? 👀 What I do know is that the branded webshop will be just another data source, not a destination. What does this mean for us, (luxury) brands? 1️⃣ Brands must meet customers where they are: on platforms, in social feeds and soon, via AI agents. 2️⃣ The future will be less about OWNING a digital storefront and more about delivering trusted experiences wherever the customer is. 3️⃣ Data is KING. As AI agents take over, the brands that win will be those with the richest, most accessible product data and the strongest reputations. 🔮 The branded webshop isn’t dead yet.. but I believe its days are numbered. Are you ready to let go?

AI is changing how we shop and how retail jobs are done. More than 15 million Americans work in retail (BLS). It’s one of the largest sectors in the economy and one where both consumers and frontline workers are starting to interact with AI in real ways. As the 2025 holiday season is in full swing, Rachel Brown on my team looked at new data on how AI is showing up in retail: from what shoppers are doing with it, to how it’s changing day-to-day work on the floor. Shoppers are using AI and converting at higher rates Nearly 60% of U.S. adults report using AI to help them shop this year. Some use it to compare prices. Others turn to tools like ChatGPT for gift ideas or product reviews. One signal that stood out: shoppers who land on retail sites via an AI assistant are 38% more likely to make a purchase (Adobe Analytics). That could reflect better targeting or that consumers are turning to AI when they already have high intent to buy. Even though most online purchases now happen on mobile, the vast majority of AI-generated traffic is still coming from desktops. That may change as interfaces evolve. AI is shaping how people expect to shop Consumers are getting used to more conversational search. Some even say they trust AI more than friends for product advice (Cian, 2025). But they also express concerns around scams, data privacy, and losing the “human touch.” That presents a real design and trust challenge for retailers. There’s a fine line between providing real value and being seen as using AI to optimize margin at the customer’s expense. On the retail floor, AI is starting to augment AI is showing up in inventory systems, virtual assistants, and mobile tools for frontline workers. Lowe’s, for example, is using its MyLow Companion to give associates real-time answers on products or stock without needing to radio for help. In addition to adding tools, AI is changing roles. A survey of employers found 62% plan to retrain or upskill retail workers for new tasks as AI adoption increases (TotalRetail). One case worth watching: Ikea. When call center jobs were automated, they retrained 8,500 workers to become virtual interior design advisors. That team generated $1.4B in revenue in 2022 alone (Reuters). What this tells us about AI and frontline work It’s early, but retail offers a useful testbed for AI’s broader impact on consumer-facing industries. The risks are real. But we’re also seeing evidence that, with investment in training and thoughtful role design, AI can support both better customer experiences and new forms of frontline work.

Inflation isn't just about rising prices; it's a catalyst for changing consumer behaviors. As purchasing power shifts, businesses must adapt swiftly to meet evolving demands. Hindustan Unilever Limited (HUL), a leader in the FMCG sector, showcases how embracing AI can turn these challenges into opportunities. 📌 The Challenge #HUL observed significant fluctuations in demand across its diverse product portfolio during inflationary periods. Premium products experienced slower sales, leading to overstock situations, while budget-friendly items frequently faced stockouts. Traditional forecasting methods, relying heavily on historical sales data, struggled to keep pace with these rapid changes in consumer preferences. 📊 The Solution: AI-Driven Demand Forecasting To address this, HUL integrated AI-powered analytics into its demand forecasting processes. This advanced system enabled the company to: Analyze Real-Time Consumer Behavior: By examining current purchasing patterns and consumer sentiment, HUL could detect emerging trends and shifts in preferences. Incorporate External Economic Indicators: The AI model factored in various economic indicators, such as inflation rates and consumer confidence indices, to predict their impact on product demand. Optimize Inventory Management: With precise demand forecasts, HUL adjusted its inventory levels accordingly, ensuring optimal stock across all product categories. 🔹 Key Insight: The AI-driven approach revealed that demand for budget-friendly products was increasing at a rate three times higher than traditional models had predicted, while premium product sales were declining in specific regions. 📈 The Impact 20% Reduction in Unsold Premium Stock: By aligning inventory with actual demand, HUL minimized excess stock of premium items. 35% Improvement in Stock Availability for Budget-Friendly Products: Ensuring that high-demand, cost-effective products were readily available led to increased customer satisfaction. Enhanced Revenue and Profit Margins: Optimized inventory management reduced holding costs and prevented lost sales, positively impacting the bottom line. 💡 The Lesson In times of economic uncertainty, relying solely on historical data can be a pitfall. HUL's proactive adoption of AI-driven demand forecasting exemplifies how leveraging advanced analytics allows businesses to stay agile and responsive to market dynamics, ensuring they meet consumer needs effectively How is your organization utilizing data analytics to navigate market fluctuations? #datadrivendecisionmaking #businessstrategies #dataanalytics #demandforecasting

𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐧𝐨 𝐥𝐨𝐧𝐠𝐞𝐫 𝐣𝐮𝐬𝐭 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐢𝐧𝐠 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬. 𝐍𝐨𝐰, 𝐭𝐡𝐞𝐲’𝐫𝐞 𝐛𝐮𝐲𝐢𝐧𝐠 𝐭𝐡𝐞𝐦 𝐟𝐨𝐫 𝐮𝐬. In recent weeks, Visa, Mastercard, and PayPal each unveiled major initiatives that move us closer to agentic commerce - a future where AI agents search, decide, and purchase on our behalf. 𝐇𝐞𝐫𝐞’𝐬 𝐰𝐡𝐚𝐭’𝐬 𝐡𝐚𝐩𝐩𝐞𝐧𝐢𝐧𝐠: • Visa launched Intelligent Commerce • Mastercard introduced Agent Pay • PayPal rolled out its Agent Toolkit 𝐖𝐡𝐢𝐥𝐞 𝐛𝐫𝐚𝐧𝐝𝐞𝐝 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲, 𝐭𝐡𝐞𝐲 𝐚𝐥𝐥 𝐝𝐨 𝐭𝐡𝐞 𝐬𝐚𝐦𝐞 𝐭𝐡𝐢𝐧𝐠: Empower AI agents to act as autonomous shoppers - securely, reliably, and under user control. 𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐭𝐡𝐢𝐬 𝐦𝐞𝐚𝐧 𝐟𝐨𝐫 𝐞-𝐜𝐨𝐦𝐦𝐞𝐫𝐜𝐞? ➡️ Frictionless buying: No more checkout pages or logins. Agents will handle the full funnel - from discovery to delivery. ➡️ Hyper-personalization: Agents know preferences, price thresholds, even timing - optimizing not just what is bought, but how and when. ➡️ New interfaces: Shopping may move from screens to voice, messaging, or even background automation. We’re witnessing the start of a new paradigm. E-commerce is becoming AI-native. Curious to hear your thoughts: Would you let an AI agent handle your next purchase? Or your next 100?

In retail, speed is no longer a competitive advantage—it’s the price of admission. The difference between leaders and laggards comes down to one thing: real-time data. You either see the moment as it unfolds, or you react after the market has already moved on.   When I sit down with retail leaders, I often talk about what I call the low-hanging fruits—not because they’re easy, but because they deliver disproportionate impact, fast.   - First, ERP integration. When buyers and suppliers operate on the same live version of truth, friction disappears. Decisions get sharper. Trust goes up. - Second, intelligent agents. Not dashboards that explain yesterday, but systems that think in the moment—forecasting demand, monitoring inventory, and optimizing logistics as conditions change. - Third, next-generation VMI. Inventory that manages itself—cutting stockouts without tying up capital in excess stock.   These aren’t moonshots. They’re practical, achievable today, and they build momentum quickly.   Recently, we partnered with a leading luxury retailer to bring this vision to life. Their reality was familiar: no real-time visibility, an overwhelming flood of OMS events, legacy infrastructure that couldn’t scale, and legitimate concerns about protecting sensitive data. We re-architected the foundation. A serverless AWS platform capable of processing millions of OMS events in real time. A secure, centralized data lake. AI and ML models embedded into the flow of operations. And live dashboards that put insight directly into the hands of business leaders.   The outcomes spoke for themselves: - Real-time and historical visibility across the enterprise - A scalable, cost-efficient technology backbone - A future-ready platform for advanced analytics and faster decision-making   This isn’t about operational efficiency alone. This is about competitive advantage.   The next wave of retail disruption is already here. The winners will be the ones who master real-time analytics and AI—not as experiments, but as core capabilities embedded into how they run the business. #AIinRetail

Ever wonder why some e-commerce brands always seem to have the right products in stock, while others struggle with overstock or empty shelves? It all comes down to demand forecasting—and in 2025, it’s getting an AI-powered upgrade. ● From guesswork to precision Traditional forecasting relies on historical sales data. AI-driven tools now go beyond that, integrating real-time factors like weather, local events, and even social media trends. The result? Forecasts with 90%+ accuracy instead of the usual 50%. ● GenAI: the next step Generative AI takes it further by analyzing unstructured data (customer reviews, trends, emerging demand signals) and answering questions in plain language. No more complex spreadsheets—just instant insights for better inventory planning. ● AI tools leading the way: ✔ Simporter – AI-powered forecasting that integrates multiple data sources to predict sales trends. ✔ Forts – uses AI for demand and supply planning, ensuring optimized inventory. ✔ ThirdEye Data – AI-driven forecasting that factors in seasonality and customer behavior. ✔ Swap – AI-based logistics platform that enhances inventory management. ✔ Nosto – AI-driven personalization that recommends the right products at the right time. ● Why this matters for #ecommerce? ✔️ Avoid stockouts that frustrate customers ✔️ Reduce excess inventory and free up cash ✔️ Adapt quickly to market shifts How are you managing demand forecasting in your store? #shopify

Quick commerce might create new rails for fashion in India. But AI is about to rewrite the stack. It won’t just improve margins or automate workflows. It will reshape how demand is created, what gets made, and how we buy. Here’s my prediction: 1. Search becomes intent-led Nobody wants to scroll through 400 SKUs. AI will learn your taste, body, budget, event, and mood, and surface five things that just work. Think: Spotify-style discovery, but for clothes. Discovery becomes contextual, not chaotic. We’re already seeing this in early interfaces like Perplexity’s shopping copilots. 2. Assortments get micro-targeted Massive catalogs are a liability. AI lets brands adapt SKUs dynamically, by user, region, season, even returns history. Shein scaled fast fashion through supply speed, but never cracked fit. Newme is flipping the model by doing weekly drops of 10–15 SKUs based on real-time feedback As merchandising behaves like content, inventory becomes a live system. 3. Returns are engineered out Returns were the biggest margin killer. Now they’re a solvable product problem through predictive sizing + fit-tech + try-at-home delivery. Zalando and H&M are already running fit-tech integrations + virtual try-ons at scale. Fit-tech will become table stakes. 4. Supply chains go real-time From design to drop to replenish to clear. AI enables live demand forecasting, smarter markdowns and faster reaction cycles. Urbanic, Zara, and Myntra are tightening feedback loops using browsing + returns + trend signals Fashion will respond to signals, not seasons and less dead stock will lead to better margins. 5. Shopping shifts from search to recommendation Shopping will shift from browsing to context-driven nudges. AI copilots will shop with you, not for you. Voice-first agents are already live. AI doesn’t just improve conversion: it changes the loop. The next generation of fashion brands will scale through personalization, fit precision, intelligent curation, and habit-forming UX Fashion will live at the intersection of fast-moving infrastructure and intelligent systems. This wont change how we buy. It will change what gets made.