commercetools released comprehensive guidance on preparing product catalogs for AI-driven commerce, identifying four critical data layers that must work together. Master data covers SKUs, dimensions, materials, and compliance certifications; dynamic data includes real-time pricing, inventory, and promotions; outcome-focused data explains what products do and who they serve; and organizational data reveals brand credentials and values (commercetools Blog).
The shift is urgent: Gartner research estimates that by 2030, 20% of online shopping transactions will flow through AI platforms and agents (commercetools Blog), and LLM referral traffic increased by 80% comparing the first half of 2025 with the second half (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). Additionally, 44% of online shoppers have abandoned a purchase due to insufficient product data (commercetools Blog).
The framework emphasizes four data-quality checkpoints: schema markup, live API-fed data, AI crawler access via robots.txt, and direct platform feeds to OpenAI, Google, and Perplexity. commercetools recommends starting with a catalog audit, prioritizing high-value categories, standardizing naming and formats, and enriching descriptions with use cases and real-world context to answer the conversational queries that AI agents receive (commercetools Blog).