AI in Action: Product Content Meets Real-World Innovation
How Brands and Retailers Are Responding
Retailers and brands are actively embedding AI into the digital shelf, evolving from static content syndication to intelligent, AI-activated content ecosystems. Structured product information is now the gateway to visibility in AI-driven search, recommendation, and discovery. Amazon’s Rufus assistant interprets shopper queries and recommends products using structured data, reviews, and Q&A content. Despite initial costs of nearly $300 million, Amazon projects over $700 million in operating profit from Rufus by 2025 and $1.2 billion by 2027. 9
Other global retailers and brands are following suit. Unilever has implemented structured metadata and AI-generated content across its 400+ Amazon product lines, boosting product discoverability and availability by ensuring machine readability. Its collaboration with Amazon includes Kaizen-led workshops aimed at improving both supply chain and content quality. 10 Beauty leader L’Oréal has launched Beauty Genius, a generative AI-powered assistant that offers personalized skincare recommendations and real-time try-ons, while its chatbot Lore—developed with NTT DATA and OpenAI—provides consultations via Instagram and WhatsApp, accessing structured product data to guide the shopper through personalized routines . 11
Walmart and Target are also building conversational search capabilities into their digital platforms, where natural language queries are matched against real-time inventory and detailed product attributes. Levi’s, meanwhile, leverages structured product content to fuel chatbot systems that help users identify the best-fitting jeans based on fabric, fit, and body type.
These examples illustrate how brands are not merely leveraging AI—they are embedding machine-readable taxonomies into every layer of the commerce stack. From Amazon’s backend infrastructure to L’Oréal’s direct-to-consumer experiences, leading brands are transforming product content into dynamic infrastructure for AI-powered shopping. They are enriching metadata, optimizing copy, and architecting product content so it can be interpreted, compared, and acted upon by AI agents. In each case, success hinges not just on the technology stack, but on the depth, consistency, and structure of the product data that powers these intelligent systems.
