Some retailers report significant improvements, such as a notable reduction in lost sales (due to better in-stock availability) and lower inventory holding costs, after implementing AI-driven demand forecasting tools.
Personalized customer experiences are another domain where agentic AI is making waves. E-commerce platforms and brick-and-mortar retailers alike are using AI to tailor the shopping experience to each customer. Recommendation engines, powered by machine learning, analyze browsing and purchase history to suggest products a customer is likely to want, increasing cross-sell and upsell opportunities. In-store, some retailers have experimented with AI-driven personalized promotions — for instance, a loyalty app that greets a customer when they enter and offers a tailored discount based on their past purchases. Chatbots on retail websites serve as personal shopping assistants, handling customer queries about product details, checking stock at nearest stores and even helping with the checkout process. These chatbots operate continuously and can handle multiple customers at once, significantly enhancing online customer service responsiveness.
Supply chain and logistics operations in retail also gain efficiency through agentic AI. From warehouse management to delivery routing, AI systems can optimize each step. In warehouses, AI-driven robots (a physical manifestation of agentic solutions) can autonomously pick and move goods, guided by algorithms that optimize picking routes and storage organization. When it comes to delivery, AI can plan logistics and delivery routes for shipments to minimize transit times and costs, accounting for traffic conditions and fuel usage. For global retailers, autonomous agents monitor supply chain risks – for example, by analyzing news and alerts, an AI agent might warn of a potential delay due to a port strike or a factory issue, prompting the retailer to re-route shipments or find alternate suppliers proactively.
