Retailgentic published analysis arguing that "product enrichment" has become an overloaded term masking five different capabilities—attribute completion, PIM automation, image tagging, catalog distribution, and AI-generated descriptions—yet most vendors and merchants remain stuck at Level 1, filling in basic attributes like dimensions, weight, color, and UPC (Retailgentic). The author, speaking after NRF Nexus conversations with brands and retailers, contends that true enrichment in the agentic era must evolve beyond static data to answer shopper questions and match intent with product context.
Retailgentic outlines four progressive levels of enrichment maturity. Level 2 adds conversational attributes (breathability, shrinkage, comfort, seasonality) that answer shopper questions rather than just storing facts. Level 3 incorporates buyer context—use cases, occasions, compatibility, product lifecycle, pros, cons, and related products—plus market context including promotions, popularity, inventory, trends, reviews, and price changes, all of which AI agents rely on heavily (Retailgentic). Level 4, the most advanced, shifts from product attributes to decision-ready context: which product fits this shopper, what objections must be overcome, and what evidence supports recommendations—optimizing for reasoning rather than keyword-filtered navigation.
For commerce practitioners, the implication is stark: companies that win in agentic commerce will not be those with the largest catalogs or perfectly completed PIMs, but those with the richest continuously optimized context responding to product changes, shopper behavior, and market dynamics through multi-surface context capture (Retailgentic). Red flags include the absence of detailed, growing product FAQs, reliance on off-the-shelf AI models to fill catalog data, and sending identical data without channel customization across Google, ChatGPT, Copilot, and Meta.