Most ecommerce data is organized for storage, not reasoning. Product feeds, analytics events, support tickets and return records often describe the same reality in different languages. A commerce ontology creates a shared map.
The ontology defines the important entities and relationships: products, variants, attributes, customer segments, channels, campaigns, return reasons, creative promises, inventory states and business outcomes.
This matters because AI agents need context. If an agent cannot connect a product variant to a return reason, a creative claim and a margin outcome, it can only summarize fragments. The ontology lets the system reason across the business.
The first version does not need to be complex. Start with the decisions the team wants to improve. If the goal is return reduction, map products, variants, size attributes, return reasons, support themes and page content. If the goal is creative performance, map products, claims, audiences, formats and conversion outcomes.
A useful ontology should be operational. It should help teams answer questions like: which SKUs have image-related returns, which campaigns are overpromising, which attributes are missing, and which content gaps affect high-margin products.
The long-term value is compounding context. Every decision, correction and outcome improves the map. That is how commerce intelligence becomes more than reporting.