AI shopping assistants are making choices on behalf of shoppers, and those choices are not neutral. A study of nearly half a million AI responses, reported by Search Engine Land, found that ChatGPT and Google AI recommend large national retailers in 46% to 58% of shopping queries when web search is enabled, even when smaller stores appear in the AI's sources. The gap between being cited and being recommended is where independent retailers are losing ground, and one prompt modifier changes the outcome entirely.

The research, conducted by Vaer AI on behalf of Lightspeed Commerce, analysed 20,000 shopping prompts across the U.S. and Canada. Those prompts generated 200,000 AI responses without web search and 260,000 with search enabled. The findings show a consistent pattern: AI systems can find smaller retailers, pull them into their source set, and then recommend a national chain instead.

Being cited is not the same as being recommended

When live search was enabled, large and small retailers each accounted for roughly 38% of stores cited in AI responses. That sounds balanced until you look at which stores AI systems actually recommended. Large chains appeared about 2.5 times as often as small retailers in the top recommendation. In other words, visibility in the AI's source pool does not translate into being named when a shopper asks where to buy.

Without web search, the imbalance was sharper. ChatGPT and Google Gemini recommended large national chains 63% to 70% of the time. Small or local independent retailers appeared about 10% of the time. When the models were given one large retailer and one smaller store without size labels, they chose the larger retailer 90% to 94% of the time.

This is not a technical problem with retrieval. The systems are pulling the right information. They are making editorial decisions about what to present, and those decisions favour scale.

Specificity makes it worse

The more specific the shopping query, the more likely AI was to recommend a large retailer. Small stores captured roughly one-third of top recommendations for broad searches, such as toys for a 6-year-old. Their share fell to about 10% when shoppers named a specific product. Meanwhile, large retailers increased from roughly 40% to 60%.

This pattern matters because it mirrors how people actually search. A shopper with intent to buy a particular item is further down the funnel and closer to conversion than someone browsing a category. If AI systems are steering those higher-intent queries toward national chains by default, independent retailers are losing the moments that matter most.

One word changes everything

Adding "independent" to a shopping prompt more than doubled the share of small and local stores recommended. Their share rose from roughly one-third to nearly four-fifths in a randomised sample of the study's prompts. On Google's platforms, the share of retailer sources from large national chains fell from about 44% with neutral prompts to as low as 9% when "independent" was used.

"Local" and "near me" made far less difference. The study noted that AI systems could count a nearby branch of a national chain as local, which explains why those modifiers failed to shift recommendations toward genuinely independent stores.

This is the operational finding. If shoppers are starting to use "independent" as a filter, and if that filter works, then independent retailers need to signal that status clearly in every place AI systems pull information. That includes structured data, on-page content, directory listings, and anywhere else a model might gather context about what kind of business you are.

What this means for independent retailers

The priority is not just AI visibility. It is making sure that when AI systems do cite you, they understand and communicate that you are independent. That requires deliberate signalling, not assumption.

Start with structured data. If you are using Schema.org markup, make sure your LocalBusiness type is accurate and that any relevant properties, such as name, description, and category, reinforce your independence. If your business name does not make it obvious, your metadata should.

Review your on-page content. Do your homepage, about page, and product descriptions make it clear that you are an independent retailer? AI systems are trained on natural language and will often use descriptive passages to classify a business. If you describe yourself as "family-owned," "independent," or "locally operated," use those terms consistently and prominently.

Check your directory and citation presence. Google Business Profile, Bing Places, Apple Maps, and vertical directories are all sources AI systems may use. Make sure your listings reflect your status as an independent store, and that your category selections are accurate.

Finally, track whether AI systems are citing you, recommending you, or both. The study makes it clear that citation is not enough. You need to know whether your store is being named when shoppers ask where to buy. That means regular prompt testing, either manually or through one of the emerging AI visibility monitoring tools. If you are being cited but not recommended, your signal to the AI about what kind of store you are is probably unclear.

The broader pattern

This is part of a wider shift in how agentic AI systems mediate discovery. Shoppers are no longer just searching and choosing from a list of links. They are asking AI to make a recommendation, and the AI is doing so based on its own model of what makes a good answer. Scale, familiarity, and brand recognition are all inputs to that model, and unless you actively signal otherwise, AI will default to the retailer it has seen most often.

The gap between citation and recommendation is not a bug. It is a feature of systems designed to give a definitive answer rather than present options. For independent retailers, the task is to make independence part of that answer, not a detail buried in the fine print.