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?

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

“Let me explain the issue again…I was saying…” Does this sound familiar? We’ve all been there: stuck on the phone or chat, explaining the same problem to a new support agent for the third, fourth, or fifth time, feeling unheard. But customer service isn’t just about solving problems. It’s about making people feel heard. Yet, far too often, support interactions feel robotic, cold, and disconnected. You’re bounced between departments. Asked to repeat yourself again and again. Given a ticket number instead of a real solution. And the worst part? No one seems to remember your last conversation. This isn’t just inefficient; it’s deeply frustrating and exhausting, and it shows a lack of empathy. Customer service must go beyond transactions. It should tap into attentive empathy, truly listening to customers, acknowledging their frustrations and cognitive empathy, and offering relevant solutions based on past interactions and emotional context. So how do we do that at scale? OpenAI’s latest update is a step in that direction. ChatGPT can now remember past conversations across sessions. This simple upgrade unlocks a smarter, more empathetic future for customer service. Imagine this: • Your support agent already knows what you’ve been through • They pick up right where you left off • They tailor responses to your preferences and pain points This is what modern, emotionally intelligent service should feel like. And the data speaks volumes: 🔹 76% of customers say repeating themselves is their #1 frustration 🔹 81% prefer brands that personalize the experience With AI memory in play, customer service teams can now: • Offer personalized support journeys • Reduce friction in every interaction • Proactively engage based on past pain points • Build long-term trust through seamless continuity For businesses, this means: • Smarter, AI-powered systems that improve with every touchpoint • Consistent journeys that feel human even when powered by machines • Stronger retention through empathy-led engagement If you’re a forward-thinking company, here’s what to do: • Invest in AI tools with conversational memory • Redesign support flows to feel continuous, not fragmented • Train agents to collaborate with AI as empathy amplifiers • Prioritize data transparency and privacy to build lasting trust Because when customers feel understood, they don’t just stay, they advocate. #AI #ChatGPT #customerexperience #CX #KSA #SaudiArabia

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.

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

Email templates can help customer service reps improve efficiency. But what happens when just choosing the right one becomes overwhelming? It's a case where AI can unlock human super-skills. One company implemented an AI tool from Laivly to help agents select the right template. Laivly's "Smart Response" feature analyzes incoming emails to suggest the right template for agents to use. Agents can review the suggested template for accuracy, and add personalization before sending the final email. The Smart Response tool improved productivity by 49%. Even better, customer satisfaction increased 10% and first contact resolution rose by 17%. It's a great example of using AI to handle tedious, repetitive tasks so agents can be freed to concentrate on work where they can add more human value. I'm increasingly seeing stories like this. Rather than humans or AI, it's humans and AI.

🧠 Is Generative AI Just Cool, or Does It Really Have an Impact? That's the big debate in tech circles these days. A study led by researchers from Stanford University, MIT, and the National Bureau of Economic Research (NBER) sheds light on this question by examining the real-world impact of deploying generative AI in a customer support environment. Their analysis offers empirical evidence on how AI tools, specifically those based on OpenAI's GPT models, are transforming customer service operations at a Fortune 500 software company. The researchers employed a mix of methodologies: a randomized control trial (RCT) and a staggered rollout, encompassing around 5,000 agents over several months. By analyzing 3 million customer-agent interactions, the study assessed metrics such as resolutions per hour, handle time, resolution rates, and customer satisfaction (Net Promoter Score). To understand the AI's impact over time, dynamic difference-in-differences regression models were used. Here is what they found: 1. 𝐒𝐢𝐠𝐧𝐢𝐟𝐢𝐜𝐚𝐧𝐭 𝐁𝐨𝐨𝐬𝐭 𝐢𝐧 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐯𝐢𝐭𝐲: The AI tool led to a 13.8% increase in the number of customer queries resolved per hour, particularly benefiting less experienced agents. 2. 𝐍𝐚𝐫𝐫𝐨𝐰𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐆𝐚𝐩: AI tools accelerated the learning curve for newer agents, allowing them to reach the performance levels of seasoned employees more quickly. 3. 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐝 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐒𝐚𝐭𝐢𝐬𝐟𝐚𝐜𝐭𝐢𝐨𝐧: The AI deployment resulted in higher customer satisfaction scores (as shown by improved Net Promoter Scores) while maintaining stable employee sentiment. 4. 𝐋𝐨𝐰𝐞𝐫 𝐀𝐭𝐭𝐫𝐢𝐭𝐢𝐨𝐧 𝐑𝐚𝐭𝐞𝐬: Interestingly, the AI support led to reduced attrition rates, especially among new hires with less than six months of experience. 5. 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬: The AI system reduced the need for escalations to managers, improving vertical efficiency. However, its impact on horizontal workflows, like transfers between agents, showed mixed results, suggesting more refinement is needed in AI integration. 6. 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐞𝐝 𝐀𝐈 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: The software wasn’t off-the-shelf; it was a custom-built solution tailored to the company’s needs using the GPT family of language models. This emphasizes the importance of context-specific AI applications for effective outcomes. For leaders, managers, and AI practitioners, these insights are invaluable—highlighting not just the potential of AI, but also the nuanced ways it reshapes workflows, impacts employee dynamics, and transforms customer experiences.So, does generative AI really make a difference? According to this study, the answer is a resounding yes—but it depends on how thoughtfully it is deployed. Link 🔗 to the paper: https://lnkd.in/ejhUfufz

Picture a small e-commerce client watching 15% of their monthly revenue vanish due to warehouse errors. 📉 Three months later? Their error rate plummeted to under 1% after implementing strategic automation solutions. Here's what most business owners overlook about warehouse automation: It's not just about the flashy robots. 🤖 After helping dozens of businesses streamline operations through automated systems, I've discovered that successful warehouse automation relies on three critical factors: → Strategic placement of technology where it delivers maximum value → Real-time visibility systems that catch stock discrepancies before they become costly problems → Phased implementation that preserves your existing workflows The biggest mistake I witness? Companies attempting to automate everything simultaneously. Smart automation begins small. Target your highest-impact, lowest-risk processes first. For most operations, that means inventory tracking and order sorting-not those impressive robotic arms everyone discusses. Yes, upfront costs are substantial. But when you factor in reduced labor expenses, improved accuracy, and the ability to scale without proportional staffing increases, the ROI becomes clear within 18-24 months. The key lies in understanding which automation solutions align with your current volume and growth trajectory. A 10,000 square foot operation requires different solutions than a 100,000 square foot state-of-the-art facility. What's your biggest warehouse challenge right now? Let's discuss how automation might help solve it. 💬 #EcommerceSolutions #LogisticsExcellence