Real enrichment changes what an AI shopping agent can do with a product. Rewording only changes how the copy reads.
A lot of what the market calls "AI enrichment" is rewording existing copy. A materials description becomes a longer, smoother sentence. A title picks up a few adjectives. The product page reads better to a person, and nothing underneath it changes.
Rewording is not enrichment. Enrichment changes what an AI shopping agent can do with a product, not how the copy reads to a shopper.
That distinction matters because the reader has changed. Enrichment is one step in Agentic Commerce Optimization (ACO), the discipline of preparing product catalogs for AI-powered shopping. The audience for the work is no longer a shopper skimming a product page. It is an AI shopping agent that parses product data to decide whether a product is eligible, whether to include it in an answer, and whether to recommend it. Most product catalogs were not built for how AI shopping agents evaluate products, so the gap is wide and the temptation to close it with better prose is strong.
This is also where enrichment gets misjudged. Look at an enriched record and the human-facing copy can seem barely different, which makes it easy to conclude that nothing was added. That conclusion applies a human standard to machine output. A shopper reads the prose. An agent reads the structure underneath it. Different readers, different bars. The signals that decide whether a product qualifies and gets surfaced are largely invisible to the teams responsible for performance, so the work gets judged by the one layer those teams can see: the copy.
Definition
Product data enrichment is work that changes what an AI shopping agent can do with a product. It surfaces structured data the brand already owns, or sources and infers genuinely new data. Rewording existing copy is not enrichment.
Enrichment takes two real forms, and both add something the agent did not have before. Reformatting an existing sentence does neither.
Parent SKU, category mapping, spec sheets, design files, product imagery. The data exists, but it has never been exposed in a machine-readable form. It sits in a PIM, a tech pack, or a folder, and never reaches the catalog record in a form an agent can read.
Corroborate an attribute against an external reference, or infer an attribute from a product image. New information enters the record that was not there before. This is the harder of the two, and for most brands it is the second move, not the first.
Reformatting an existing sentence does neither. It rearranges words inside data the agent already had. The record looks busier. It does not become more complete, more structured, or more interpretable.
| Reformatting | Enrichment | |
|---|---|---|
| What changes | The wording of existing copy | What data the record exposes to an agent |
| The data | Rearranges data the agent already had | Surfaces owned data, or sources and infers new data |
| After the work | Reads better to a shopper | Agent can categorize, include, and recommend the product |
| On a skeleton SKU | Not even possible: the words do not exist yet | Assembles attributes so an agent can place the product at all |
The test is operational, not editorial. Before the work, can an AI shopping agent categorize the product, include it in an answer, and recommend it? After the work, can it do something it could not do before? If the agent's options changed, that is enrichment, even when not a single word of the copy changes.
As shopping moves inside retailer agents, the brand-side question shifts from whether an agent will mention a product to what product information it uses, which SKUs it surfaces, and why it recommends a competitor. Those are operational questions. If a product was mapped to the wrong category and now maps correctly, that is enrichment, even when not a single word of the human-facing description changes. If the prose reads better but the agent still cannot determine sleeve length, material, or compatibility, that is editing. The question is never whether the copy improved. The question is whether the agent's options changed.
The copy is not the deliverable. What the agent can do with the product is.
The gap is sharpest on new and skeleton products. A skeleton SKU often has little or no usable copy to begin with, so there is nothing to reword. Enrichment there has to assemble data: pull attributes from the source the brand already holds, structure them, and map the product so an agent can place it at all.
Reformatting is not even an option, because the words do not exist yet. The data has to come from somewhere.
Surface internal data first. Most catalogs carry far more information than they expose, sitting in PIMs, tech packs, spec sheets, and design files rather than in the record an agent reads. Structuring that owned data is the largest near-term win. Sourcing external data is the harder, second move.
Brands increasingly expect agents to corroborate attributes against brand sites by style number, infer attributes such as sleeve length from product images, and cross-reference other retailers. That expectation is real and worth naming. It is also the harder problem, and it is the second move, not the headline.
When you evaluate enriched product data, do not ask whether the copy reads better. Ask whether an AI shopping agent can now do something with the product it could not do before: categorize it, include it, and recommend it as consumers research, find, and buy. That is the bar enrichment has to clear.
Frequently asked
Short answers for brand, retail, and catalog teams measuring how their product data performs in agentic commerce.
Reformatting rearranges words inside data an agent already had. Enrichment adds something new: it either surfaces structured data the brand already owns but has never exposed in a machine-readable form, or it sources and infers genuinely new data. The test is whether an AI shopping agent can do something with the product it could not do before.
Apply an operational test, not an editorial one. Before the work, can an AI shopping agent categorize the product, include it in an answer, and recommend it? After the work, can it do something it could not do before? If the answer changes, the data was enriched, even when the human-facing copy barely changes. If the prose reads better but the agent still cannot determine an attribute, that is editing.
A shopper reads the prose. An agent reads the structure underneath it. Different readers, different bars. A record can read almost the same to a person and still move from invisible to eligible for an agent.
Surface internal data first. The largest near-term win is structuring data the business already owns in PIMs, tech packs, spec sheets, and design files. Sourcing or inferring external data matters, but it is the harder, second move.
A new product launches at the exact moment demand is highest and its product data is thinnest, and that gap is why AI shopping agents can't see it.
Catalog Scoring gives every product a 0–100 readiness score for AI shopping agents across seven dimensions, and shows exactly which gap to fix first.