Artificial Intelligence in Retail

Because with wrong demand forecasting everything else falls apart... This infographic shows 10 red flags in demand forecasting and how to turn them green: 🚩 # 1 - Over-reliance on historical data How to Turn Green: incorporate external data like market trends, competitor activity, and consumer sentiment to enrich forecasts 🚩 # 2 - Ignoring promotions and discounts How to Turn Green: build a promotions-adjusted forecasting model, considering historical uplift from similar campaigns 🚩 # 3 - Forgetting cannibalization effects How to Turn Green: model cannibalization effects to adjust forecasts for existing products 🚩 # 4 - One-size-fits-all forecasting method How to Turn Green: use demand segmentation (for example, high variability vs. stable demand); do not treat all SKUs equally 🚩 # 5 - Not monitoring forecast accuracy How to Turn Green: track metrics like MAPE, WMAPE, bias, and forecast value-add (FVA) to improve over time 🚩 # 6 - High forecast error with no accountability How to Turn Green: tie accountability to S&OP (sales and operations) meetings 🚩 # 7 - Poor collaboration with sales and marketing How to Turn Green: hold regular cross-functional meetings to align forecasts with upcoming campaigns 🚩 # 8 - Over-reliance on intuition How to Turn Green: balance judgment-based inputs with statistical and AI-driven models 🚩 # 9 - Infrequent forecast updates How to Turn Green: move to a rolling forecast system that updates regularly based on the latest data 🚩 # 10 - Past sales (instead of demand) consideration How to Turn Green: make the initial predictions based on the unconstrained demand; not on sales that are impacted by cuts and out of stock situations Any others to add? #supplychain #salesandoperationsplanning #integratedbusinessplanning #procurement

Many business leaders that I’ve spoken to assume that their technological choices are invisible to consumers. In fact, the opposite is true when it comes to #GenAI! This means that business leaders need to factor in #consumer views front and centre when deploying this technology at scale. Yet there is little known on how consumers perceive GenAI. BCG’s Center for Customer Insight (CCI) sought to fix that with our latest work.   We surveyed 21,000 consumers from 21 countries on six continents, and they have a message to companies deploying new #AI and #generativeAI technology: “we see you”. Our study finds that over 80% of respondents globally are aware of GenAI, and nearly a quarter are already trying out this technology. The 80% is a staggering figure given how new this technology is. Secondly, there is an inherent polarization in consumer perceptions. While 43% of consumers are excited about the possibilities AI and GenAI present, 29% are worried about its potential downsides such as privacy concerns. Levels of enthusiasm vary from market to market – and here we find another surprise. Receptiveness to GenAI is entirely disconnected to the maturity of the market - in fact, markets like China, Indonesia and India see higher net positive scores on GenAI perception than US, France and Australia.   This means that companies need to adopt honest and objective communication with their consumers to generate trust as they deploy GenAI solutions at scale. Responsible AI is a mandatory consideration from day 1.   Read more in our new article, Consumers Know More About AI than Business Leaders Think: https://lnkd.in/g4CCgh9x.

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?

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.

Machine learning beats traditional forecasting methods in multi series forecasting. In one of the latest M forecasting competitions, the aim was to advance what we know about time series forecasting methods and strategies. Competitors had to forecast 40k+ time series representing sales for the largest retail company in the world by revenue: Walmart. These are the main findings: ▶️ Performance of ML Methods: Machine learning (ML) models demonstrate superior accuracy compared to simple statistical methods. Hybrid approaches that combine ML techniques with statistical functionalities often yield effective results. Advanced ML methods, such as LightGBM and deep learning techniques, have shown significant forecasting potential. ▶️ Value of Combining Forecasts: Combining forecasts from various methods enhances accuracy. Even simple, equal-weighted combinations of models can outperform more complex approaches, reaffirming the effectiveness of ensemble strategies. ▶️ Cross-Learning Benefits: Utilizing cross-learning from correlated, hierarchical data improves forecasting accuracy. In short, one model to forecast thousands of time series. This approach allows for more efficient training and reduces computational costs, making it a valuable strategy. ▶️ Differences in Performance: Winning methods often outperform traditional benchmarks significantly. However, many teams may not surpass the performance of simpler methods, indicating that straightforward approaches can still be effective. Impact of External Adjustments: Incorporating external adjustments (ie, data based insight) can enhance forecast accuracy. ▶️ Importance of Cross-Validation Strategies: Effective cross-validation (CV) strategies are crucial for accurately assessing forecasting methods. Many teams fail to select the best forecasts due to inadequate CV methods. Utilizing extensive validation techniques can ensure robustness. ▶️ Role of Exogenous Variables: Including exogenous/explanatory variables significantly improves forecasting accuracy. Additional data such as promotions and price changes can lead to substantial improvements over models that rely solely on historical data. Overall, these findings emphasize the effectiveness of ML methods, the value of combining forecasts, and the importance of incorporating external factors and robust validation strategies in forecasting. If you haven’t already, try using machine learning models to forecast your future challenge 🙂 Read the article 👉 https://buff.ly/3O95gQp

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

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 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