Artificial Intelligence in Retail

Retailers shift from Google to AI agents – what this means for FMCG brands A silent shift is underway in digital commerce — and FMCG brands should take note. In August 2025, ChatGPT drove 20% of referral traffic to Walmart and Etsy Shop, with Target at ~15% and eBay at 10%. Just a month earlier, these numbers were significantly lower. While referral traffic is still under 5% of total visits, the growth velocity is clear. Consumers are replacing search with conversation. Instead of using Google, users now ask ChatGPT: - “Which toothpaste is best for sensitive teeth?” - “Top healthy snacks for kids?” - “Why is Swiss Cheese so good and where can I buy it?” - “Best laundry detergent for cold wash?” This behavioral shift matters. AI agents filter and surface product recommendations based on trust, brand recognition, and relevance — not just ad spend. For FMCG producers, the implications are clear: – Visibility is no longer guaranteed by shelf space or SEO. – If your brand isn’t part of AI agents’ product surfaces, you’re invisible. – Retailer data access policies now shape your discoverability. Retailers like Walmart (420 million SKUs) and Target are gaining exposure by remaining open to AI crawlers. Amazon, however, has blocked many bots — causing its ChatGPT-driven traffic to drop 18% in August. This evolving ecosystem affects how FMCG brands are discovered, recommended, and ultimately purchased. And unlike paid search, where placement is auctioned, AI-driven recommendation engines operate in more opaque, model-based hierarchies. Key facts: – 2.5 billion daily ChatGPT prompts – ~50 million daily shopping-related queries – 60% of US shoppers have used genAI for shopping (Omnisend, Aug 2025) As OpenAI and others move toward affiliate fees and embedded checkout, FMCG brands must act now — ensuring their products are correctly indexed, accurately represented, and promoted within retailer ecosystems that are embracing AI traffic. The next shelf is conversational. And it's already stocked. #retail #ecommerce #fmcg #omnichannel #ai #chatgpt #openai #shoppingagents #digitalcommerce #referraltraffic #amazon #walmart #etsy #target #ebay #rufus #retailtech #consumertrends #searchvschat #affiliate #onlineshopping #generativeai #shoppingbots #conversion #usa #northamerica #martech #digitalmarketing #adtech #aiincommerce #futureofshopping #platformeconomy #brandvisibility #fmcgmarketing

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.

The most valuable ad slot in retail might not be on a shelf, but inside a chatbot. OpenAI just forecast $102B in ad revenue by 2030. Some retailer media networks might see that as a threat, but the smart ones see opportunity Great to have the chance to kick off the IAB Australia's new 'Perspectives on Retail Media' series with a piece on why AI is retail media's next growth engine. The core argument: AI isn't coming for retail media. It's coming to supercharge it. But only for the retailers and advertisers who move now. A few numbers worth sitting with: 🛒 OpenAI's ad revenue is forecast to jump from $2.4B this year to $102B by 2030. Halfway there still makes ChatGPT a top-five global ad platform. 🛒 Google says some brands using its AI ad tools are seeing up to 80% sales lifts. 🛒 WARC puts agentic commerce at $136B this year, heading to $1.7T by 2030. Most of that AI ad spend will likely be incremental or come out of search, not retail media directly. But there's a second-order effect. If shoppers start product discovery inside ChatGPT, Google's AI Mode, or Perplexity instead of on a retailer's site, retail media's growth ceiling quietly drops. Budgets don't shrink overnight. They just stop compounding. Here's where Australia is already getting interesting. Woolworths Supermarkets launched Olive (powered by Google's Gemini) earlier this year. Bunnings followed weeks later with Buddy, an agentic assistant that builds your deck project from a photo. Both are live. Both are being marketed as better shopping experiences. They're also the most brand-safe, first-party, high-intent ad environments in the country. The strategic question stops being "does our AI assistant improve CX?" and starts being "how do we monetise it without breaking the trust that makes it work?" The infrastructure is already forming. Criteo is building the bridge between retailer chat experiences and sponsored product surfacing. Thrad has launched a product specifically to help retailers monetise their AI assistants. Retailers who define the rules of in-chat advertising on their own terms will own this. The ones who wait will inherit whatever Amazon, Alphabet Inc. and OpenAI decide is fair. For advertisers, the shift is smaller but just as urgent. Product content needs to answer questions, not just match keywords. And last-click attribution will undercount everything AI touches, because conversational discovery sits earlier in the funnel than the attribution models were built for. Push for incrementality. The early-mover window is open. It won't stay open forever. Thanks to Gai Le Roy, Lachlan Brahe and the IAB Australia Retail Media Council for letting me share my thoughts. Full piece linked in the comments 👇 #RetailMedia #CommerceMedia #Advertising #RMN #Retail Woolworths Group Wesfarmers Wesfarmers OneDigital ChatGPT Claude Anthropic

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

Did you hear about Target and Shopping Inside of ChatGPT? AI Platforms Are Becoming Primary Commerce Channels 🦄 We're witnessing the shift from AI-assisted shopping to AI-native commerce, where major retailers are embedding full transactional experiences directly within AI platforms rather than using AI to drive traffic to traditional e-commerce sites. Target announced a ChatGPT integration which follows Walmart's similar OpenAI partnership. This is in addition to Amazon developing in-house AI shopping with Rufus, and Etsy and Shopify integrating with OpenAI's Instant Checkout. This represents a fundamental platform shift. Retailers are racing to establish commerce presence inside AI environments rather than trying to bring customers back to their own digital properties. ‼️ So What: This signals the potential "unbundling" of traditional e-commerce. Instead of browsing websites, consumers may increasingly shop through conversational AI that can access multiple retailers seamlessly. I think that these movements will dis-intermediate traditional e-commerce platforms and websites, similar to how social media changed content discovery. The retailers who establish early AI-native commerce capabilities may capture disproportionate market share, while those who remain website-dependent risk becoming invisible in AI-mediated shopping. 🏇 Do What: Evaluate your commerce strategy through an "AI-first" lens. Don't just ask "how can AI improve our website" instead ask "how do we sell when customers never visit our website?" Consider how your product discovery and purchase processes need to change for conversational rather than visual shopping experiences.

When COVID hit, StyleNook's demand model was technically accurate. But it was predicting demand for a world that had ceased to exist overnight. Who needs work clothes when you're at home all day! Today, whether it's Dubai or Mumbai, the same question is coming up. Are the outputs from our AI forecasting still usable? In most organizations, the answer is probably not. I've spent twenty years in retail AI. Most demand forecasting models are built to handle volatility. Seasonal cycles, fashion shifts, category swings. They treat shocks as recoverable: assume the pattern will return, smooth over the noise, wait for reversion. For most of what retail throws at them, that works. The problem is the model cannot tell you whether the shock you are in is recoverable, or whether it has permanently chaged the baseline. COVID did not look like a bad season to a demand model. The current Gulf environment does not look like a market dip. Both are events that may have fundamentally shifted who buys what, when, and why. Most retail AI investment is optimized for accuracy. Very little is built to catch the moment when our world has shifted dramatically. What is needed is a system that tells us when it has stopped predicting well. Consumer confidence shifting. Search behavior moving toward essentials. Category sentiment changing by the week. The data exists but for most fashion retailers these feeds are never connected to the demand model. These signals appear weeks before a difference shows up in sell-through. They are publicly available. They are just not built in. Two massive disruptions in less than a decade have exposed the same blind spot. Build for accuracy. And build for the moment when your model loses its grip on the world. Think of it as a Signal Confidence Layer. Four things to build on top of your existing stack. What each one looks like is in the carousel below.

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

One forecasting mistake can quietly cost apparel brands revenue: Treating stockout days like normal sales days. This sounds small, but in apparel, demand often comes in short windows. A seasonal product gets traction. A bestseller starts moving. A campaign drives traffic. A color suddenly takes off. But if Medium and Large sell out, the sales report starts lying. Let’s say a style sells 30 units a day when it is fully in stock. Then the key sizes sell out. Sales drop to 10 units a day. The report says: “Demand slowed.” But demand may not have slowed. The customer just could not buy the right size. That matters because the brand missed revenue during the demand window. And if that data goes straight into the next forecast, the team may underbuy the same product again. So one stockout can create two problems: 1. Lost sales today. 2. A weaker forecast tomorrow. A simple AI workflow any apparel team can try: Export five files: 1. Daily sales by SKU 2. Daily inventory by SKU 3. Stockout dates 4. Product master 5. Similar styles or same style in other colors Then ask AI, ChatGPT or Claude: “Review this apparel sales and inventory data. Flag products where demand may be understated because of stockouts. Compare sales velocity before the stockout, during the stockout, and after restock if available. Estimate lost demand and explain whether the forecast should be adjusted before the next reorder” Then ask for the output in this format: • Product • Sizes or colors affected • Stockout days • Sales before stockout • Sales during stockout • Estimated lost demand • Revenue at risk • Forecast adjustment needed • Recommended action • Confidence level The important point is simple: Zero sales during a stockout does not mean zero demand. It means zero availability. And in apparel, availability during the right season can be the difference between capturing demand and missing the window. AI is useful here because it can connect sales, inventory, size availability, and restock timing quickly. Not to replace the planner, but to help the team avoid underbuying products customers already proved they wanted.

𝐀𝐈 𝐢𝐧 𝐫𝐞𝐭𝐚𝐢𝐥 𝐢𝐬𝐧’𝐭 𝐨𝐧𝐥𝐲 𝐚𝐛𝐨𝐮𝐭 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐟𝐢𝐱𝐢𝐧𝐠 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐢𝐧𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐢𝐞𝐬. The retailers seeing real impact from AI aren’t chasing the most impressive use cases. They’re identifying where money, time, and customer experience are leaking and fixing it at scale. Here’s what that looks like in practice • 𝐖𝐚𝐥𝐦𝐚𝐫𝐭 AI monitors shelves in real time → fewer stockouts, faster replenishment. Recovering lost revenue, minute by minute. • 𝐊𝐫𝐨𝐠𝐞𝐫 Digital shelves reduce ~40% energy costs while enabling retail media. One system driving both cost savings and new revenue. • 𝐒𝐞𝐩𝐡𝐨𝐫𝐚 Color IQ + virtual try-ons remove buying uncertainty. Confidence converts directly into sales. • 𝐇&𝐌 AI embedded across demand forecasting and supply chain. Less waste. Better inventory turns. Smarter pricing. • 𝐇𝐚𝐫𝐫𝐢𝐬 𝐅𝐚𝐫𝐦 𝐌𝐚𝐫𝐤𝐞𝐭𝐬 400+ models forecasting 20,000+ products. SKU-level precision improving margins and sustainability. • 𝐆𝐚𝐥𝐯𝐚 𝐏𝐡𝐚𝐫𝐦𝐚𝐜𝐲 93% accurate prescription translation. Seconds saved per order → massive operational efficiency. • 𝐖𝐚𝐥𝐠𝐫𝐞𝐞𝐧𝐬 AI across pricing, inventory, and workflows. Enterprise-wide decision intelligence. • 𝐏𝐢𝐥𝐥𝐏𝐚𝐜𝐤 AI + automation powering fulfillment. Speed, accuracy, and better customer experience. 𝐓𝐡𝐞 𝐫𝐞𝐚𝐥 𝐬𝐡𝐢𝐟𝐭: AI in retail is moving from “isolated use cases” to interconnected systems of intelligence. 𝐃𝐚𝐭𝐚 → 𝐌𝐨𝐝𝐞𝐥𝐬 → 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬 → 𝐎𝐮𝐭𝐜𝐨𝐦𝐞𝐬 → 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 If you’re leading AI transformation in retail: Start asking: “Where are we wasting time?” “Where is customer friction highest?” “Where are we losing money?” Because that’s where AI delivers real value. 𝐖𝐡𝐚𝐭’𝐬 𝐭𝐡𝐞 𝐦𝐨𝐬𝐭 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐢𝐧𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐲𝐨𝐮’𝐫𝐞 𝐬𝐞𝐞𝐢𝐧𝐠 𝐢𝐧 𝐫𝐞𝐭𝐚𝐢𝐥 𝐭𝐨𝐝𝐚𝐲? Follow Vinod Bijlani for more insights