What if your product was perfectly referenced on Google, but invisible to ChatGPT’s 900 million weekly users? This is no longer a hypothesis. In 2025, AI-generated traffic to e-commerce sites surged by +4,700% in one year (Adobe). At the same time, 58% of consumers have already replaced traditional search engines with AI tools to search for products (Capgemini, 2025).
The purchase journey is shifting ground. And product data is the passport to exist within it. Three concepts structure this new reality: GEO, AEO, and agentic commerce.
GEO, AEO, Agentic Commerce: three concepts to know
While these three terms are often mentioned together, they refer to distinct realities and different operational challenges:
- GEO (Generative Engine Optimization) refers to all practices aimed at being cited in responses generated by LLMs. Where SEO seeks a ranking in Google results, GEO seeks a mention in the synthesis produced by an AI. 67% of marketing directors at large companies have identified it as a strategic priority for 2026 (McKinsey, Q4 2025);
- AEO (Answer Engine Optimization) goes further by making your brand the direct answer to a question asked to a conversational engine. Being the reference that AI retains when a user asks “What is the best product for [use]?”. Well-structured content is 28 to 40% more likely to be cited in an LLM response (Envive, 2025);
- Agentic Commerce is the next step: AI no longer recommends, it acts. Autonomous agents compare, select and purchase without the consumer visiting a single website. According to Morgan Stanley, nearly half of online shoppers will go through AI agents by 2030 for approximately 25% of their spending.
Why product data is at the heart of this visibility
An LLM doesn’t display results: it arbitrates. And what determines its choice is the quality of the data provided to it.
A concrete example: a sports nutrition brand whose product sheet mentions “high protein content” faces a competitor stating “32g of protein per serving, Informed Sport certified, tested on 5,000 units”. The LLM has no hesitation: it recommends the second, because its data allows it to answer the consumer’s question precisely.
The rule is mechanical: fragmented or inconsistent data reduces the probability of being recommended. Structured and complete data increases it. Visitors coming from LLMs convert 31% better and generate 254% more revenue per visit compared to classic organic traffic (Adobe Digital Insights, January 2026). Being absent from AI responses means missing the most qualified buyers.
How to concretely prepare your product data?
The trap to avoid: data that is “sufficient for traditional retail, but invisible to AI”. A product sheet at 60% completeness may work on a traditional drive or marketplace, but not to be selected by an AI agent. Here are four priority areas to address:
- Complete attributes beyond the retailer’s requirements. Uses, benefits, certifications, composition: each additional piece of information increases the probability of appearing in a relevant response;
- Ensure cross-field consistency. An LLM detects contradictions and penalizes them. An allergen missing from the dedicated field, a nutritional value inconsistent with the Nutri-Score are errors that degrade the credibility of a product sheet in the eyes of models;
- Formulate benefits in an explicit and verifiable way. “High quality” is not enough, “ISO 9001 certified, 48-hour battery life” is exploitable by an LLM;
- Ensure real-time omnichannel synchronization. An AI agent compares data from multiple sources. Contradictory information across channels directly harms the recommendation.
With Equadis, prepare your product data for AI engines
Faced with these new requirements, Equadis supports brands and retailers in making their data structured, consistent and exploitable by AI agents. To address these challenges, the ACE (AI Content Engine) solution offers four key AI features, compatible with both Equadis PIM and PDS as well as those from other market players.
- AI Content Generation, to automatically enrich product sheets with precise, benefit-oriented descriptions adapted to each channel;
- AI Inconsistency Detection, to identify in real time cross-field contradictions that make a product sheet unreadable for an LLM;
- AI DocExtract, to automatically extract attributes from documents and packaging;
- AI Translation, to guarantee the semantic consistency of data across all markets.
Digital Shelf Analytics (DSA) completes this solution by measuring product data performance directly on retailer websites, to identify optimization levers before your competitors.
GEO, AEO or Agentic Commerce: whatever the speed at which these channels establish themselves, the quality of product data will be the differentiating factor. In an environment where LLMs already arbitrate purchase recommendations, every month of delay is a share of visibility conceded to the competition.
Want to prepare your catalog for the new AI channels?
