Insights

How Do Brands Get Recommended by ChatGPT, Gemini, and AI Shopping Assistants?

AI systems synthesize evidence and context to generate recommendations. Here are the five areas Spangle uses as a practical framework for evaluating how retailers show up.

Published September 16, 2026 by Maju Kuruvilla, CEO of Spangle.

More consumers are asking AI systems for product recommendations. Retailers want to understand what makes their brand appear in the answer. This piece describes the factors that influence AI recommendations at a level useful to executives and their teams, without pretending there is a single formula that guarantees inclusion.

Key takeaways

01AI systems synthesize multiple signals to generate recommendations. No single tactic guarantees inclusion.
02There is no universal formula for getting recommended by AI. Retailers can evaluate their readiness across five areas that consistently matter to how products and brands can be understood and evaluated by AI systems.
03At Spangle, we use five areas as a practical framework: representation, product understanding, authority and evidence, relevance to intent, and freshness and accuracy.
04These are not SEO ranking factors. SEO helps retailers get found. AI Discovery helps retailers get chosen.
05AI systems change frequently. No brand should optimize against a specific system as if the rules are fixed. Optimize for how the retailer is understood.
06Not every AI intent matters equally to the retailer. Brand positioning, assortment strategy, and merchandising priorities should shape which recommendation opportunities the retailer chooses to prioritize.

What determines whether a brand appears in an AI recommendation?

AI systems answering commerce questions do not work like search engines returning ranked results. They synthesize information from multiple sources, apply a model of what the customer is asking, and generate an answer that may or may not include specific brands or products.

There is no universal formula for getting recommended. But retailers can evaluate their readiness across five areas that consistently matter to how AI systems understand and evaluate a brand. At Spangle, we use these five areas as a practical framework:

Representation

How well the brand's products, positioning, and category are represented in the sources AI systems draw from. This includes the retailer's own website, third-party editorial content, structured product data, customer reviews, and other commerce signals.

Weak, inconsistent, or generic representation can limit the evidence AI systems have available when evaluating a retailer.

Product understanding

How well AI systems can understand what the retailer sells and what makes each product distinctive.

Machine-readable product data matters here: clear descriptions, complete attributes, structured specifications, images, and the kind of details a customer would need to make a decision. Retailers with sparse or generic product information limit what AI systems can say about them.

Authority and evidence

The credibility of the sources supporting the brand's claims. AI systems draw from editorial coverage, expert reviews, ratings, customer feedback, and other third-party evidence to evaluate whether a brand is a credible answer to the customer's question.

Independent evidence can give AI systems additional context when evaluating claims about a brand or product.

Relevance to intent

How well the retailer's products and brand align with what the customer is actually asking for.

A customer asking "what's the best running shoe for a marathon?" is asking for a specific product profile matched to a specific use. The more clearly a product's attributes and evidence map to the customer's intent, the more information an AI system has to evaluate whether it is relevant.

Freshness and accuracy

Whether the information AI systems have about the retailer is current and correct. Pricing, availability, product specifications, and product content change over time. AI systems working from outdated information will represent the retailer inaccurately.

Why traditional SEO tactics are not enough

SEO was designed to help a page rank in a list of results. AI recommendations are different in kind. They synthesize evidence and context into an answer rather than returning a set of pages the customer navigates.

Traditional SEO fundamentals still matter. Clear content structure, schema markup, and authoritative sources all support AI understanding. But they weren't designed for an environment where AI synthesizes evidence into an answer, not just a set of ranked pages. Similarweb's research on how AI is changing the consumer buying journey found that consumers rate AI tools as most useful at the discovery and evaluation stages, well ahead of traditional search engines.

SEO helps retailers get found. AI Discovery helps retailers get chosen.

Common mistakes retailers make

Optimizing for visibility instead of representation. A brand can appear more often in AI answers without being described any more accurately or being chosen any more often.
Treating AI systems as static. ChatGPT, Gemini, Perplexity, and others change how they generate answers frequently. Tactics tied to a specific system's current behavior age quickly.
Ignoring the retailer's own commercial priorities. Being recommended for the wrong intents can be worse than not being recommended at all, especially for premium brands or brands with strategic product priorities.
Assuming AI referral traffic measures AI influence. AI can influence a purchase without ever generating an AI referral in the retailer's analytics.
Treating this as a one-time content project. Products, inventory, brand priorities, and AI systems all change. Retailers who treat AI Discovery as a project rather than an ongoing discipline lose ground to retailers who treat it as continuous.

What should retailers be asking?

Are our products, brand, and commercial context represented accurately in the sources AI systems draw from?
Are we appearing when customers ask for something we should be recommended for?
Which of our category's high-value intents are we currently missing?
Who inside our organization is responsible for improving how we appear in AI?
Are we measuring the diagnostic (citations, visibility) or the outcome (customers choosing us)?