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.

It was the best of search, it was the worst of search. It was the age of instant answers, it was the age of disappearing links. It was the epoch of personalization, it was the epoch of lost discovery. It was the season of AI-driven clarity, it was the season of algorithmic opacity. It was the spring of conversational commerce, it was the winter of ten blue links. According to Adobe Analytics, U.S. retail websites saw a 1,200% increase in traffic from generative AI sources between July 2024 and February 2025. During the 2024 holiday season alone, this figure jumped 1,300% year-over-year, with Cyber Monday traffic spiking 1,950% compared to 2023. Consumer adoption is driving the shift. A survey of 5,000 U.S. shoppers found that 39% have used generative AI for online shopping, with 53% planning to do so this year. Users rely on AI for product research (55%), recommendations (47%), deal-hunting (43%), gift ideas (35%), product discovery (35%), and shopping list creation (33%). AI-generated traffic isn’t just growing—it’s more engaged than traditional sources. Visitors spend 8% more time on-site, view 12% more pages per visit, and have a 23% lower bounce rate than those from search or social media. Conversational AI interfaces are improving consumer confidence and making online shopping more intuitive. That said, conversion rates for AI-driven traffic still lag behind traditional sources by 9%, but the gap is closing. In July 2024, the difference was 43%, signaling growing consumer trust in AI-assisted purchases. Another key insight: AI-assisted shopping is happening on desktops, not mobile. Between November 2024 and February 2025, 86% of AI-driven traffic came from desktop users—suggesting that consumers prefer larger screens for complex, AI-guided shopping experiences. While the numbers are compelling, they only hint at what’s coming. AI-driven agents won’t just assist shoppers—they’ll shop for them. The way consumers find, evaluate, and purchase products is shifting fast, and this data is just beginning to tell the story. -s

AI does not force us to buy. But it can make the invitation more personal, timely and difficult to resist. Search for one product and similar recommendations can soon appear across shopping sites and social media. AI is increasingly helping businesses decide not only what consumers see, but which message may be most persuasive to them. My new article for The Conversation UK explores: • how AI is changing marketing from broad targeting to personalised persuasion • why online shopping can shorten the gap between wanting and buying • how this can contribute to overconsumption and waste • how mindful consumption can help people pause and make more considered choices The article draws on my research into mindful consumption, which views it through awareness, caring and temperance - recognising when enough is enough. My related academic work was published in the Journal of the Academy of Marketing Science. The issue is not simply whether AI makes marketing more effective. It is whether personalisation can remain useful without weakening consumer autonomy or encouraging unnecessary consumption. Read the article here: https://lnkd.in/eKRsamBs Why AI wants you to buy more – and mindfulness could help you buy less I would be interested to hear your view: where should businesses draw the line between helpful personalisation and excessive persuasion? Cardiff Metropolitan University Cardiff School of Management British Academy of Management Sustainable and Responsible Business SIG The Conversation #ArtificialIntelligence #Marketing #ConsumerBehaviour #MindfulConsumption #ResponsibleMarketing #Sustainability

I believe AI creates real value when it tackles hard, physical problems — the kind that live in factories, warehouses, and service tasks. Recently, I learned the attached from a plastics machine manufacturer and logistics provider struggling with unpredictable production schedules, warehouse congestion, and reactive maintenance routines. When a structured AI implementation approach was brought into the equation the following outcome was achieved 👇 🔹 Smart Production Planning – Machine learning models forecasted demand and optimized resin batch production, cutting material waste by 18%. 🔹 AI-Driven Warehouse Logistics – Intelligent slotting and routing algorithms boosted order fulfillment rates by 25%, reducing forklift travel time and idle inventory. 🔹 Predictive Maintenance for Service Teams – Sensor data and pattern recognition flagged early signs of machine wear, reducing unplanned downtime by 30%. The result wasn’t automation replacing people — it was augmentation empowering people. Operators, warehouse managers, and service engineers gained real-time insights to make faster, better decisions. 💡 Takeaway: AI success in industrial environments isn’t about technology first — it’s about aligning data, people, and process to create measurable operational impact. #AI #IndustrialServices #SmartManufacturing #WarehouseOptimization #PredictiveMaintenance #DigitalTransformation #OperationalExcellence

Black Friday has evolved from panic at the disco store chaos to 24/7 online browsing, but this year marks a different shift: it's the first holiday season where AI is officially part of the shopping ritual. 🛒🤖 Simon-Kucher reported that 54% of consumers plan to use AI for holiday shopping, mostly for product reviews, comparisons, price tracking, and gift ideas. At the same time, 46% say they won’t use AI because they still want a human touch in gift-giving. https://lnkd.in/e97HQKcF That tension is fascinating with tech efficiency colliding with emotional intent. The adoption gap also follows a predictable pattern: Gen Z and Millennials lean in, while Gen X and Boomers hold back. No surprises there. Here's what's interesting to me: 1️⃣ Holiday shopping is now a stress test for retail AI. The past month of reporting shows retailers’ recommendation engines, AI search layers, dynamic pricing systems, and optimization models are all under heavy strain during this holiday shopping season. This is the first real-world trial of whether their AI infrastructure can handle both the volume and the expectations. 2️⃣ The purchase funnel is quietly being rewritten. In IAB Research published in October on AI x shopping, we found 57% of consumers use AI for product comparisons, 53% for product-specific questions and 53% for price tracking and deals. https://lnkd.in/euUFnFGW Deloitte's recent study on AI's effect on holiday shopping also noted price tracking highly at 56% of their study. https://lnkd.in/eT8NkjbT Awareness ➡️ Consideration ➡️ Conversion now sees the consideration phase being collapsed with AI. 3️⃣ The center of gravity is shifting from traditional ads to product data. The most meaningful change isn’t a new ad unit. It’s that brands are now optimizing product data, metadata, structured content, images, and reviews to ensure brand visibility in AI platforms. Visibility now depends on how “AI-ready” your catalog is. 4️⃣ Yes, AI-driven shopping is up, but spending patterns are more complicated. Consumers plan to spend 6% more this year, but Simon-Kucher says this is mostly due to tariffs and higher prices, not because they’re buying more. 5️⃣ And with AI in the mix, trust matters even more. This season is already seeing issues with price camouflage, deal inflation, and scams, a reminder that “AI-powered shopping” needs transparency and consumer protections to scale responsibly. ⭐️ The BIG PICTURE ⭐️ Black Friday 2025 isn’t just about commerce. It’s a preview of what AI-mediated shopping will look like when agents become the default discovery interface. And the brands that win won’t necessarily spend the most on ads...they’ll be the ones whose product data is structured cleanly enough for AI systems to understand and recommend. #ai #shopping #blackfriday

🚀Reimagining Slotting: An AI‑Driven Idea for Dynamic D365 Warehousing🚀 🤖 Background — Why AI for warehousing: Warehouses generate high‑volume, fast‑changing data (orders, picks, forecasts). D365 WMS is robust but static: slotting rules and classifications are set at go‑live and rarely adapt to real‑time demand. 📦 Problem — what breaks operations: When fast‑moving SKUs remain in distant or high locations, picks slow down, aisles get blocked, labor and forklift time spike, and dispatches slip — all driving up cost and hurting service. 📍 Key metric — Location Efficiency Score (LES): LES ranks every storage location by operational cost drivers (distance to outbound, rack height, pick complexity) so locations are objectively comparable. 🔢 Key metric — Demand Score: Demand Scores normalize SKU velocity across time windows and units so true fast movers are identified consistently, not by noisy short‑term spikes. 🧠 Core idea — what the AI agent does: The agent ingests D365 data (sales, forecasts, on‑hand, location metadata (LES)), computes Demand Scores and continuously monitors mismatches between SKU velocity and locations where they are currently stored. 🔎 Actionable output — reclassification: The agent dynamically based on demand reclassifies SKUs in D365 (fast, medium, slow) based on normalized demand patterns so slotting logic reflects current reality rather than stale assumptions from last update. 📤 Actionable output — move suggestions: The agent generates prioritized, planner‑ready suggestions to move items — minimal‑movement swaps with estimated ROI, labor impact, and pick‑time savings. 🤝 Planner‑in‑loop workflow: Suggestions include clear rationale and LLM‑style explanations; planners review and approve moves, preserving human control while removing manual data‑sifting. ⚡ Why reclassification matters: Accurate, automated reclassification ensures fast movers are always treated as fast movers — reducing travel time, avoiding high‑level picks, and preventing recurring bottlenecks. 🔁 Why move suggestions matter: Targeted, minimal swaps deliver immediate throughput gains without large‑scale reshuffles; they convert analytics into operational change quickly and safely. 📈 Business outcomes: Faster picks, fewer aisle blockages, lower labor and equipment cost, improved on‑time dispatch, and continuous, measurable uplift in warehouse KPIs. ✅ Conclusion: This AI agent turns D365 from a static rule engine into a dynamic decision partner — it reclassifies SKUs and recommends precise moves, enabling daily, data‑driven slotting that scales with your business. 📤 Surfaces approved suggestions with clear ROI and LLM‑generated explanations so teams trust and adopt changes quickly. 💡 💡 This is not a product — it’s an idea exploring how AI can finally make D365 warehousing truly dynamic. #AI #Dynamicslotting #Dynamicsitemclassification #idea #AIidea #warehousemanagement #D365

If AI Doesn’t Recommend You, You Won’t Be Found AI is now the start for high-intent shopping journeys. This isn’t a quirk. It reflects a structural shift in how consumers shop. Adobe reports AI-driven retail traffic is up 1,200% YoY, and this traffic arrives more engaged, with lower bounce rates, and is closer to purchasing. Consumers no longer search “Nike vs Asics”. They ask: “Best running shoe for 20km/week on concrete under $200?” By the time a shopper reaches your site, their decision is mostly made. The journey becomes: AI research → AI shortlist → PDP → checkout The homepage is no longer the front door, the PDP is. This is a material shift and creates a set of fundamental changes for how brands operate. I see three areas that all brands should be considering: 1. Winning the LLMs understanding (AEO) LLMs reward brands with: • High-quality, recent reviews (vendors like Yotpo become invaluable) • Expert & creator consensus (independent authoritative voices) • High-signal open-web content (Reddit, YouTube, TikTok, comparison sites) • Machine-visible loyalty (“my go to”, “I always repurchase”) • Structured product data (fit, materials, ingredients, performance, compatibility, occasion) This is how you earn more AI-qualified traffic. 2. Winning the consumer’s search intent To be listed, you must shape how consumers describe their needs when they ask AI. This comes from: clear category framing, distinctive brand assets, credible creators, sustained proof, and PDPs that explain why this product is the right solution. Influencing the query dramatically increases the chance of AI pointing to your brand. 3. Brands need a different team to win AI-driven traffic If discovery now happens in AI, your resourcing must shift with it. My three predictions for how this impacts resourcing are: a) I don’t see SEO easily evolving into AEO. SEO was about optimising your site for Google. AEO is about shaping the open web so that AI models understand, trust, and recommend you. Different skills. Different instincts. Different people. b) Community management returns, but as reputation engineering. Not posting memes or moderating comments. You need operators who can spark high-signal conversations, activate advocates, and generate the retention cues (“my third bottle”, “I always repurchase”) that models weight heavily. c) Brand Marketing & PR will wrestle budget from Performance Marketing. Performance Marketing dominated the last decade because it had clean ROI. You can’t buy your way into an LLM recommendation, you earn it. In an AI-driven funnel, Brand suddenly has a direct, measurable impact on discovery and revenue. Performance won’t vanish, but it loses its monopoly on growth. Retailers who don’t rewire their talent for this reality won’t just fall behind; they won’t show up at all.

I let ChatGPT decide what I bought this Christmas. Not because it was fun. Because it was better. OpenAI didn’t just add “shopping” to ChatGPT. They quietly moved the front door of commerce. Black Friday timing wasn’t subtle. Neither is the intent. Instead of: search → ads → reviews → 12 tabs → decision fatigue We now get: prompt → clarification → ranked options → trade-offs → checkout That’s not search. That’s delegated decision-making. I’ve been testing this for the last 20 days. Did most of my Christmas shopping this way. Planning. Comparing. Deciding. And I’m convinced this is how 2026 shopping will feel. A few things should make every retailer sit up straighter: 1️⃣ Share of shelf is becoming share of prompt If ChatGPT decides what “best” looks like, SEO tricks and sponsored placements matter less. What matters is whether your product data actually answers real human questions. 2️⃣ Reddit beats glossy product pages OpenAI is weighting lived experience over marketing copy. That should make brand teams uncomfortable. Because customers already trust other humans more than your PDP. 3️⃣ Checkout is moving upstream Target and Walmart didn’t partner for novelty. They see what Amazon saw years ago. Whoever owns the moment of intent owns the margin. 4️⃣ AI shopping assistants will fight for relevance Sparky. Rufus. ChatGPT. Perplexity. Gemini. Consumers won’t use five. They’ll use one that saves time and feels right. Here’s the uncomfortable truth: Most brands aren’t optimized for AI buyers. They’re optimized for algorithms from 2016. Clean data. Clear trade-offs. Honest reviews. Context that helps humans decide. That’s what wins in an AI-mediated shopping world. The race isn’t traffic anymore. It’s trust. And the battlefield just shifted from the browser… to the prompt. ♻️ Repost if this changed how you think about commerce 🔔 Follow Ranjana Sharma for clear thinking on AI, retail, and what’s actually coming next

AI Is Transforming Warehouses Into One Of The Next Major Automation Frontiers Despite decades of investment in enterprise software and inventory management systems, many warehouses still rely heavily on manual processes such as clipboards, spreadsheets, and physical inventory counts. Artificial intelligence companies are now targeting this inefficiency as one of the largest untapped opportunities for industrial automation and operational optimization. The article highlights Gather AI, which combines autonomous drones, AI-equipped cameras, computer vision, and AI-driven software to help businesses automate warehouse inventory management. While often viewed as a drone company, Gather AI’s broader focus is using intelligent sensing systems and AI agents to continuously monitor inventory conditions inside warehouses and production facilities. The need for modernization remains substantial. Many businesses still conduct labor-intensive manual inventory audits similar to processes used decades ago, requiring operational shutdowns and large employee teams to verify stock counts. These outdated methods create inefficiencies, inaccuracies, labor costs, and delays that AI systems are increasingly capable of reducing. The article explains that AI-powered warehouse automation can improve inventory visibility, detect discrepancies in real time, optimize storage layouts, reduce shrinkage, and support faster supply chain decision-making. Autonomous drones and computer vision systems can scan shelves continuously without interrupting operations, generating dynamic inventory intelligence at a scale difficult for human teams alone. The broader trend reflects how AI is moving beyond software applications into physical industrial environments. Warehouses are becoming highly data-driven operational ecosystems where sensors, robotics, AI agents, and automation systems work together to optimize logistics, inventory management, and supply chain performance. Key Takeaways for the material. Warehouses remain surprisingly dependent on manual inventory practices despite years of digital investment. AI-driven automation systems using drones, computer vision, and autonomous monitoring are emerging as powerful tools for improving inventory accuracy, operational efficiency, and supply chain responsiveness. The broader implication is that physical infrastructure industries may become one of the largest long-term growth areas for AI deployment. As intelligent automation expands beyond digital workflows into real-world industrial operations, logistics and warehouse systems could undergo transformations similar to those already occurring in software and knowledge work. I share daily insights with tens of thousands followers across defense, tech, and policy. Keith King https://lnkd.in/gHPvUttw