Agentic shopping is beginning to mature. Consumer adoption is projected to jump from 19% to 46% by the end of 2026, according to the Braze Retail Customer Engagement Review. McKinsey projects up to $5 trillion in B2C revenue from agentic commerce channels by 2030.
That’s a lot of potential revenue. But tapping into it means retailers need to look at how product data – previously just a backend operational necessity, often managed through spreadsheets – is organised and maintained.
Why product data matters
Product data is the critical foundation of agentic shopping – that is, autonomous AI agents finding, evaluating and purchasing products on behalf of humans. AI agents need structured product data to handle the complex, constraint-based shopping queries that currently account for around 70% of AI shopping traffic. Whereas typical product pages typically carry only five to eight machine-readable fields, AI agents require at least 30 or more attributes, according to research by Paz.ai. Without these attributes, products remain invisible on the digital shelf. And that’s not an outcome any online retailer wants.
The challenge is that many retailers still treat product information and digital assets as separate disciplines. Yet for AI agents, context matters. Detailed specifications, imagery, usage information, sustainability credentials and merchandising content all play a role in how products are discovered, evaluated and recommended. As AI-powered shopping journeys become more sophisticated, disconnected data is becoming a competitive disadvantage.
Enterprise retailers can address these challenges through large-scale technology investments, stitching together product information management (PIM), content management and digital asset management systems. For mid-market retailers, however, building and maintaining that kind of technology stack is often unrealistic.
So what is the alternative?
That’s the question explored in InternetRetailing’s latest bluepaper, produced in association with Canto. The report examines the emergence of a new generation of Digital Asset Management systems, often described as “active DAMs”, which combine digital asset management and product information management in a single platform.
Rather than simply storing images, videos and creative assets, these systems connect content with structured product data, helping ensure products carry the attributes needed for both human and AI purchasing journeys.
For mid-market retailers, the appeal is clear. Instead of replicating the proprietary technology stacks of the world’s largest brands, they gain a practical and affordable route to making product data AI-ready.
As shopping agents become a new route to purchase, product data is becoming a new route to visibility. Retailers that act now will be best placed to ensure their products can be found, understood and recommended by the next generation of AI-powered shoppers.
Download the latest InternetRetailing bluepaper, produced in association with Canto, to discover how retailers can prepare for the rise of agentic commerce without the complexity of an enterprise-scale technology stack.
