Can AI shopping assistants find your BigCommerce store?

Shoppers have started asking AI which product to buy. 41% of US shoppers now use AI assistants to research what they buy, according to the IBM and NRF study from January 2026. When ChatGPT or Perplexity answers one of those questions, it names specific stores. A lot of those stores run on BigCommerce, and their owners are now finding out whether an assistant can actually read them.

The stock BigCommerce Stencil theme emits product structured data on your product pages by default, the machine-readable summary an assistant reads to understand a price and what an item is. A custom theme or app can change or strip it, and older setups may emit none, so it is not guaranteed. Even when it is there, what trips owners up is narrower: whether the price, the currency, and the in-stock signal all make it into that markup in a form a machine can trust. The storefront shows a price to a human just fine. Whether the assistant can read it is a separate question.

What AI actually sees on a BigCommerce store

The BigCommerce stores we scanned were not invisible to AI. Of the 12 we scanned in 2026, 8 carried product structured data an assistant could read, 7 of them in modern JSON-LD. The claim that these stores gave an assistant nothing to work with did not hold up.

The gaps show up after the default. The make-or-break one is the offer block: a product can carry a name and a price on the page and still be missing the complete price, currency, and availability inside the markup that an assistant needs to recommend it. Completeness was thin in other ways too. Only 4 of the 12 stores carried a brand or GTIN an assistant can match against a catalog, and none carried a GTIN at all.

Where BigCommerce stores lose points

The offer block: your price on the page, not in the markup

<script type="application/ld+json">
{
  "@type": "Product",
  "name": "Standing Desk"
}
</script>

<script type="application/ld+json">
{
  "@type": "Product",
  "name": "Standing Desk",
  "offers": {
    "@type": "Offer",
    "price": "599.00",
    "priceCurrency": "USD",
    "availability":
      "https://schema.org/InStock"
  }
}
</script>

How to close the gap

1. 1

   Put the key facts in the description

   Write the material, the size, the use case, and what makes the product different into the visible description, in plain sentences a customer would read. This was the gap that held the most stores back, and it is the one no app can write for you.

2. 2

   Complete the offer block in your markup

   Make sure every product emits a complete offer in its structured data: the price, the currency, and an availability signal, together. Check that your theme or a structured-data app actually outputs all three. This is the change that moved the most capped stores in our scan.

3. 3

   Add a brand and a product identifier

   Add a brand and a code like a GTIN or MPN to your products, then confirm your theme or app pushes them into the product markup. Without those fields, an assistant cannot match your item to a real, identifiable product.

4. 4

   Make policies and identity easy to read

   Link your shipping, returns, and refund pages clearly, and make sure your homepage states who you are and how to reach you in a way a machine can read. They are what an assistant checks before it recommends a store.

What we found across real BigCommerce stores

One anonymized store from that scan. Strong product markup and a brand, held back by thin content:

Questions

Does BigCommerce add structured data for AI automatically?

Partly. The stock BigCommerce Stencil theme emits product structured data by default, so a Product is usually there. What varies is whether it carries a complete offer (price, currency, and availability together) and identifiers like a brand or GTIN. A theme or app can change or thin that markup. Having a Product present is not the same as being AI-ready.

Does ChatGPT recommend BigCommerce stores?

It can. Assistants recommend whatever store they can read and trust, on any platform. A BigCommerce store with a complete offer block, real descriptions, clear policies, and identifiers competes fine. The platform is not the blocker. The signals on your pages are.

Is this AEO or GEO for BigCommerce?

Same idea, plain words. AEO (answer engine optimization) and GEO (generative engine optimization) both mean making your store readable and recommendable by AI assistants. That is exactly what this page is about, for BigCommerce stores.

Will an app or theme fix this?

Partly. A good structured-data or SEO app can complete your offer block and identifiers, which genuinely helps. But content depth and policy clarity are editorial work no app writes for you. Run the check to see which gaps are actually yours.