channels, from bricks-and-mortar stores to online storefronts and third-party marketplaces. There’s constant pressure to adjust quickly to changing trends and demands. If the inventory isn’t managed well, the stock doesn’t get shifted, and old stock may have minimal value if the market has moved on. If a retailer doesn’t have visibility into inventory and doesn’t know what’s selling and what’s not, it’s also much harder to make future decisions. Products may not be reordered or cancelled from suppliers in time. Manufacturers need accurate, relevant data to design new products and plan production – data that comes from the front line of retail. AI-powered inventory management Using artificial intelligence (AI) to optimise inventory management benefits both retailers and consumers. Here are some reasons why: 1. Real-time stock visibility Knowing what stock you have is critical in retail. Inventory often represents a major portion of a retailer’s assets and poor management can lead to a cash flow problem if too much capital is tied up in inventory.  Accurate inventory management helps ensure efficient order fulfilment and a seamless omnichannel experience. Customers expect to browse products across different channels and see what stock is available.  Retailers can streamline processes such as click-and-collect, ship-from-store, or in-store pickups.  Walmart uses a system called IRL – Intelligent Retail Lab – in one of its New York stores. This involves 1,500 AI-enabled cameras that keep a visual eye on stock levels, as well as shelf sensors that measure weight and count remaining inventory. Using real-time analytics that integrate upcoming sales demand data, the system triggers out-of-stock notifications so that products can be refilled before levels fall too low. 2. Cross-selling and upselling By combining AI-powered inventory management with generative AI, retailers can provide more intelligent shopping suggestions. Armed with specific information about what stock is available and what needs shifting, GenAI can combine this with data such as search history and user demographics in real time and suggest the next logical purchase or step in a customer journey.  For example, a customer buys a new hair dryer. Looking at what other hair-dryer buyers have bought and noting that the customer has previously searched for hair conditioning products, and armed with the knowledge that the retailer has overstock of hair serum, GenAI can promote the serum with human-like text (this example from ChatGPT): “Hey, I wanted to share something interesting I discovered recently. After getting a new hair dryer, I started using a hair serum, and not only does it offer heat protection, but it also effectively controls frizz, nourishes the hair, and adds a noticeable shine.” 3. Smarter stocktakes Stocktakes are the bane of many retailers, being slow and full of human error. They may need to be done outside business hours, requiring overtime and penalty rates for staff or requiring the shop or warehouse to shut down for a substantial amount of time. AI offers two key advantages here. First, having real-time tracking of inventory makes records much more accurate. This speeds up stocktakes. Secondly, automated stock taking is becoming a reality, particularly for larger warehouses. AI-powered drones and robots can physically count inventory, reducing the staff members required. 4. Accurate forecasting Demand forecasting is critical today. It enables retailers to make more accurate financial projections and better decisions about budgets and resource allocation. The goal is to order sufficient products to satisfy future customer demand while avoiding overstocking and under-stocking. AI can analyse in real time, combining existing stock levels, sales data and market trends to continually rebalance demand and supply.  Weather data is a notable example. Customer habits change as temperatures spike or drop, not just with obvious products like buying umbrellas during the rains, but whether people shop online, in air-conditioned shopping malls, or high street stores. Analysing these patterns and combining them with forecast data can help retailers optimise product planning and strategy. For example, when winter is approaching, are we headed for a good snow season, and will people be visiting physical stores to try on ski boots, or does other data suggest an exodus to warmer destinations and consumers preferring to purchase swimwear online? By using these emerging technologies, retailers can transform the backend as well as the frontend customer experience, and in doing so achieve huge cost reductions and increased profit growth.