More shoppers are asking AI what to buy for Christmas. Can small shops benefit?

Those details matter. Respondents were talking about what they intended to do, not showing receipts for purchases completed by an AI agent. “Using AI” could mean finding inspiration, comparing products or looking for discounts. It doesn’t mean that 65% of purchases will happen inside a chat window, or that most people are ready to hand their buying decisions over to automation.

In fact, the same report contains a finding that might temper some of the platform providers’ enthusiasm. Shoppers are more willing to accept an AI recommendation or let an assistant help build a basket than to have an agent complete the purchase on its own. Shopify says only around a third of respondents trust AI enough to let it buy a product on their behalf. There’s still a clear line between getting gift ideas and giving something permission to spend your money.

Why a small brand might suddenly appear alongside a big one

Traditional product searches often start with a familiar brand, a category or a specific product name. AI makes it easier to describe a need in everyday language. Shoppers don’t have to know the proper name for a specialist material, feature or accessory. They can explain the conditions a product needs to work in and ask for suggestions.

In Shopify’s report, 71% of people who regularly use AI say they are more inclined to choose independent brands than they were a year ago. That’s an interesting signal, but it doesn’t prove AI caused the change. People who are keen to try new tools may also be more likely to seek out unusual products for entirely different reasons. And Shopify, as a commerce platform provider, has a commercial interest in promoting its own AI shopping solutions.

The potential advantage for a small brand is quite specific. If it sells a specialist product with a clear purpose, good photographs and an accurate description, an assistant has a chance of matching it to a highly specific request. A big name isn’t always the best answer to an unusual problem. But if a small shop describes everything as “premium quality”, “modern design” and “the perfect gift”, AI has no more useful information to work with than a human does.

Discovery, comparison and purchase: three different stages

First comes discovery. The shopper describes a problem, and the assistant suggests products, brands or categories. Next comes comparison. Dimensions, materials, price, reviews, stock levels, delivery dates and meaningful differences between variants all matter. Third comes the purchase, which often still takes place in an ordinary online store, with its usual payment and order fulfilment process.

Each stage needs slightly different information. A product category can help someone find an item, but it isn’t enough for a proper comparison. Price might determine which offer comes first when sorting by cheapest, but it won’t explain why a more expensive model lasts longer or suits a particular use. And even after a product has been recommended, unclear delivery information or a dreadful checkout can still lose the sale.

Whether any part of a purchase can happen inside a chat depends on the integrations available for the platform, agent, market and seller involved. Shopify is developing its own catalogue technology and shopping channels for AI assistants. That doesn’t mean every store can already sell every product through every chat interface.

What does AI need to know before it can recommend a product?

I wouldn’t start with the fashionable acronym AEO or a new “LLM optimisation” plugin. I’d start with the information a good online store should have had all along. What is it? Who is it for? How does it differ from similar products? What does it cost, is it in stock and when will it arrive? If the product page doesn’t answer those questions, technical tricks won’t make up for the missing information.

• Specific product descriptions: Not just marketing claims, but dimensions, materials or ingredients, compatibility, instructions for use and limitations.
• Clear product variants: Distinct colours, sizes and capacities, so the system doesn’t recommend a version the customer can’t actually buy.
• Accurate prices and availability: Consistent information on the product page, in the basket and across every channel receiving the product catalogue.
• Transparent purchasing terms: Clear delivery information, charges, returns and details of what’s included, rather than hiding important conditions until the final step.
• Verifiable claims: Genuine reviews, photographs and evidence supporting the product’s advertised features instead of empty promises.

There’s a technical reason for this approach too. Google’s Merchant Center documentation calls for prices and availability to be consistent across product data, on-page markup and the purchasing process. Structured data makes product information easier for machines to interpret. It doesn’t guarantee that any particular AI will recommend a product, but it can help systems understand the offer correctly. That’s a much more reliable foundation than trying to charm an algorithm with an undocumented file or a handful of keywords.

Do you need to rebuild your store on a new platform?

No. Shopify is describing features it’s developing for its own ecosystem, but that doesn’t suddenly make WooCommerce stores, custom applications or other ecommerce platforms obsolete. What matters is the quality of the data, how it’s made available and updated, and which integrations connect the store to channels where its customers actually look for products. WooCommerce also publishes guidance on making store listings understandable to AI shopping agents.

Not every business needs to rush onto every new channel, either. If customers generally choose a product after a personal consultation, completing the purchase inside a chat may matter very little. But for accessories with precise specifications, being discoverable through natural-language searches about how they’ll be used could be much more interesting. Decisions should be driven by what you know about your customers, not fear of missing the latest industry buzzword.

How can a small store test the trend before Christmas without buying another promise?

A useful experiment can be remarkably simple. Pick a handful of representative products. Ask people who don’t know your range to describe what they need in their own words. See whether the product page gives them enough information to judge if it’s a good fit without guessing. Compare their questions with the descriptions on your site and the information in your product feed. If the basics are missing, fix those for human shoppers first.

Then check the technical side. Can the crawlers used by the services you’ve chosen access the relevant pages? Is the product markup correct? Are stock levels being updated and prices displayed accurately? That doesn’t mean opening up your customer records or admin area without thinking. Making your catalogue more visible does not require exposing orders, private accounts or other sensitive information.

Finally, distinguish experimenting from measuring results. One assistant recommending a product in one test doesn’t establish lasting visibility. More traffic from AI tools doesn’t, by itself, prove sales have increased. Track referral sources where they can be identified technically, along with actual orders and the quality of the traffic. Some visits will remain difficult to attribute, so a little caution is better than pretending the numbers are more precise than they are.

WebsiteGarage’s take: small shops don’t need to be bigger, just easier to understand

It’s not hard to imagine the next version of the old SEO circus in the world Shopify is describing: hundreds of services promising first place in AI answers, miracle prompts hidden in product descriptions and magical files supposedly doing the rest. There’s just one problem. Customers still don’t buy keywords. They buy something that solves a problem.

AI may help people discover smaller brands, but it won’t conjure up a competitive advantage out of thin air. If a brand genuinely makes something that meets an unusual need, it should be able to explain that need and show why its product is the right answer. And if the store’s only selling point is “top quality at a great price”, even the most talkative AI agent won’t have much to build on beyond a bland recommendation.

Shopify’s report isn’t announcing the death of traditional retail. What it suggests is that product search is becoming more conversational, but responsibility for the offer and fulfilling the order hasn’t gone anywhere. For smaller stores, that’s actually encouraging. Clear product pages, genuine expertise and a checkout that works properly may be a better investment than the latest “AI-ready” sticker.

Sources

• Shopify: Welcome to the first holiday season of the agentic era (6 October 2026; announcement and interpretation of the data by the report’s publisher).
• Shopify 2026 Global Holiday Retail Report (original report; methodology: Sapio Research, 18,000 consumers and 7,500 business representatives across nine countries).
• Google Merchant Center: Product data specification (guidance on product data quality, pricing and inventory).
• WooCommerce: How to make your store readable to AI shopping agents (WooCommerce guidance on making product listings understandable to shopping agents).