Your Product Data Isn’t Ready for AI Shopping Agents
Why most product data enrichment fails in the age of agentic shopping… and how to create differentiation signals that actually work.
Consumers are increasingly finding products through conversational AI – ChatGPT, Perplexity, Google AI Overviews, and a growing ecosystem of LLM-powered shopping agents.
But most product feeds today were built for a different era. Product attributes are optimised for keyword search and marketplace compliance: they tell a search engine what a product is, but they aren’t optimised for the modern age of AI agents.
That’s why product data enrichment matters now more than ever.
But not all enrichment is created equal.
The #1 product data enrichment mistake
Ecommerce product data enrichment is the process of enhancing basic product information by adding more detailed, accurate, and relevant content, making it more engaging and helpful for customers. AI agents like ChatGPT Atlas don’t browse product pages the same way we do as humans. Instead, they retrieve, reason about, and compare structured product data to generate recommendations in real time.
The most common mistake in product data enrichment for AI Agents is restating information that already exists in the spec table. Consider a product like an electric range with an air fry feature.
A typical enrichment might generate highlights like:
“5.3 cu. ft. oven capacity accommodates large meals”
“Includes dishwasher-safe air fry basket”
“Fan convection circulates hot air for cooking multiple dishes at once”
“Warming center keeps finished dishes warm”
“Storage drawer included for cookware”
Every one of those claims is already present in the product’s structured data. An LLM agent can read spec tables already: it doesn’t need a second, less structured copy of the same information. When enrichment simply restates what’s already there, it’s just padding – and it won’t help your products get found on search more often, or in new ways. Seven highlights where only two contain genuine insight, ten Q&A pairs where six could be answered by scanning the spec table… More data, but not more useful data.
How to optimise product data for LLM agents
To understand what good enrichment looks like, you need to understand how an LLM shopping agent processes product data.
When a shopper asks something like “what’s a good electric range for air frying?”, the agent is doing several things simultaneously. It retrieves product data from indexed sources, ranks products by relevance to the query, compares shortlisted products against each other, and generates a conversational response that helps the shopper make a decision.
At every stage of that process, the agent is looking for information that helps it differentiate one product from another. Spec data can’t do this because specs are largely standardised across products in the same category. Every electric range has a capacity measurement. Every jacket lists its fabric composition. Every card game states the number of players.
What the agent needs instead is information that answers the questions that specs can’t:
How does this product perform in practice? Not “fan convection cooking” but “reviewers report consistent temperature control and even cooking across the oven cavity.” The first is a feature. The second is evidence that the feature works.
What do real users say about it? Not “5 burner elements” but “customers praise the variety of burner sizes for accommodating different cookware, from small saucepans to large stockpots.” The first is a count. The second tells the agent something useful about why the count matters.
What makes this product different from alternatives? Not “includes air fry basket” but “no-preheat air fry capability reduces time-to-table for frozen snacks and quick meals.” The first is a line item. The second is a reason to choose this product over one without that feature.
When and why would someone buy this? Not “suitable for cooking” but “quick weeknight dinners with frozen foods” or “batch cooking multiple dishes simultaneously.” The first matches every appliance ever made. The second matches a specific shopper intent that an LLM agent can connect to a natural language query.
The anti-padding principle
Good enrichment is not about generating more data. It’s about generating the right data and being honest when there isn’t enough to say.
A useful framework: if a product has limited review coverage or minimal competitive differentiation, the enrichment should reflect that. Three strong, well-sourced highlights are better than seven where four are spec restatements used to fill a quota. An empty differentiators field is better than a fabricated comparison with no evidence behind it.
This matters more than it might seem. LLM agents have context windows. When an agent is comparing ten products to answer a shopper’s query, each product’s enrichment data competes for attention within that window. Padding dilutes signal. Fewer, stronger claims mean the product’s best attributes are more likely to surface in the agent’s response.
So, don’t pad, shrink. Output only what you have evidence for. Zero items in a field is acceptable. Filler is not.
The Q&A test for product enrichment
Q&A pairs are one of the most valuable product enrichment attributes for agentic commerce because they directly mirror how shoppers interact with AI agents. But they’re also the most commonly padded.
A simple quality test: could a shopper answer this question by reading the spec table? If yes, skip it.
Bad Q&A (spec-derived):
“What is the oven capacity?” / “The oven has a 5.3 cu. ft. capacity.”
“Does it have a storage drawer?” / “Yes, it includes a storage drawer.”
“What width does this range fit?” / “This is a standard 30-inch freestanding range.”
Good Q&A (requires multi-source synthesis):
“Does the air fry basket fit a whole chicken?” (requires real-world usage knowledge)
“Is it noisy during convection?” (requires review synthesis)
“How long does self-clean take and does it smell?” (requires user experience data)
The difference is clear. The first set adds nothing an LLM can’t already extract from structured data. The second set provides information the agent genuinely needs to answer shopper questions with confidence rather than hallucinating an answer.
Intent matching through use-case tags
Use-case tags serve as intent-matching hooks for LLM agents. When a shopper asks “I need something for hosting dinner parties,” the agent needs to match that intent to specific products. Tags like “large-batch hosting with buffet-style serving” make that connection. In contrast, more generic tags like “everyday family cooking” match everything and therefore differentiate nothing.
The test for a good use-case tag is: would this tag help an LLM select this specific product over others in the same category? If the tag could apply to every product in the category, it’s too generic to be useful.
Specific, intent-rich tags work because they align with how people actually talk to AI agents. Nobody asks “recommend me a product for everyday use.” They ask “what’s good for busy weeknight dinners when I’m cooking from frozen?” or “I need a jacket I can wear to the office and then cycle home in.” Enrichment that maps to those real queries will outperform enrichment that maps to category-level generics.
Visual analysis and product data enrichment
Text-based enrichment has a blind spot: it can’t see the product. Spec sheets describe materials and dimensions. Reviews describe experiences. But neither captures visual details that influence purchase decisions.
A blazer with hidden closures and no visible branding might signal “quiet luxury,” but that insight only emerges from examining the product image. A workbench with a half-depth lower shelf maximises legroom – something that’s obvious from the product photo, but absent from the spec table. A pack of coloured paper carrying a “Made in France” seal on the packaging adds a provenance signal that text data doesn’t surface.
Visual analysis adds a data source that most enrichment approaches miss entirely. If you can enrich your product data from images, that adds extra differentiation signals for LLM agents that can’t be derived from text alone.
What good product enrichment looks like
The best product data enrichment for agentic commerce has a few consistent characteristics.
Claims are unique and add extra information, not restated from specs. Q&A pairs answer questions a shopper would actually ask an AI agent, not questions answerable from the product listing. Use-case tags are specific enough to match real intent queries. Differentiators name competitors or categories and cite evidence. And when evidence is thin, the output is lean rather than padded.
The products that perform best in LLM-driven discovery won’t be the ones with the most data. They’ll be the ones with the most useful data: structured for how AI agents reason, grounded in real-world evidence, and honest about what the product does well and where it falls short.
The shift from keyword search to conversational AI is not incremental. It’s a fundamental change in how product data gets consumed. The feeds that were built for Google Shopping and marketplace compliance won’t cut it. The next generation of product content needs to be built for how machines think, not just how they index.
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