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.
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.
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