commercetools released guidance on preparing product catalogs for agentic commerce, identifying four critical data layers required for AI platform visibility. Master data covers core product attributes like SKUs and materials; dynamic data includes real-time pricing and inventory; outcome-focused data explains use cases and problem-solving benefits; and organizational data surfaces brand credentials and certifications (commercetools Blog).
The framework addresses a growing market pressure: Gartner estimates that by 2030, 20% of online shopping transactions will flow through AI platforms and agents (commercetools Blog). LLM referral traffic increased by 80% comparing the first half of 2025 with the second half (commercetools Blog), yet 44% of online shoppers have abandoned purchases due to insufficient product data (commercetools Blog). Pages with structured data are cited 3.1x more frequently in Google AI overviews, and 71% of pages cited by ChatGPT contain structured data (commercetools Blog).
The guidance emphasizes that AI agents lack the contextual inference humans apply—they require machine-readable schema markup, live API-fed data, unblocked crawler access, and direct feeds to major platforms including OpenAI, Google, and Perplexity. Commerce teams should audit catalogs against these four layers, prioritize high-value product segments, standardize naming and formats, and enrich descriptions with outcome-focused language that answers conversational shopper queries.