AI Product Search: Visibility for Online Stores

Customers ask ChatGPT for the best stroller, read an AI Overview in Google Search and have Perplexity recommend three suitable models. AI product search is fundamentally changing the path to the shopping cart because the initial selection increasingly happens before a customer ever enters your store. This creates a new question for merchants: Does your product range appear in AI answers, or does the system recommend a competitor? This guide uses current data to show where customers search for products with AI, how these systems build their recommendations and which measures can make your online store visible in ChatGPT, Google AI Overviews and other AI services.

What is AI product search?

AI product search is the use of artificial intelligence to find products by describing a need in natural language and receiving specific recommendations with prices, features and reviews instead of a list of links. The underlying large language models understand the meaning of a query, combine product data from multiple sources and formulate a reasoned recommendation.

In practice, AI product search appears at two different levels:

• External AI systems: AI assistants such as ChatGPT, Gemini and Perplexity, as well as AI summaries in search engines, answer product questions, compare offers and link to stores. The search happens outside your store, and the system decides which merchants appear in the answer.
• AI search within your own store: A semantic search function understands spelling errors, synonyms and complete sentences and guides visitors to suitable products more quickly. In this case, you control the technology yourself.

The technology also changes how people search. Instead of entering keywords such as “men’s running shoes size 10,” users formulate complete requests such as “Which running shoe is suitable for overpronation and wide feet and costs less than 150 euros?” AI search rewards content that answers these questions precisely. It is another step in the broader development described in our guide to artificial intelligence in e-commerce.

How widely is AI used for online shopping?

Around three in ten consumers in Germany already use AI for product search: 29.7 percent consult AI chatbots such as ChatGPT, while 29.4 percent read AI summaries in Google Search. Among people aged 16 to 29, 42.7 percent use AI chatbots to find product information, compared with only 15.9 percent of those aged 50 to 65.¹ AI product search is therefore no longer a niche behavior. It is moving into the mass market with every younger generation of shoppers.

International data confirms this trend while also showing its limits. According to Criteo’s Commerce and AI Trend Report, which surveyed more than 6,000 people, 47 percent use AI tools for product comparisons, 39 percent for product searches and 38 percent for finding bargains. Only 8 percent, however, actually place an order through an AI tool.² AI assistants are also the starting point of the shopping journey for just 14 percent of consumers, behind marketplaces at 36 percent and search engines at 28 percent.

The potential is far from exhausted. In a German survey by kernpunkt and commercetools, 65.8 percent of respondents said they could imagine using AI support for product search, while 36.7 percent could imagine purchasing directly within an AI system. Among the surveyed e-commerce experts, 94.1 percent expect product search to shift strongly toward AI interfaces.³

A Similarweb analysis of real user journeys in the United States shows how AI and search engines divide their roles. During the idea generation stage, 35.0 percent of users consider AI tools the most useful resource, compared with only 13.6 percent for search engines. When people look for a seller and a price, the two channels are almost equal. Brands recommended by ChatGPT are also 2.5 times more likely to receive a website visit within seven days than competitors that were not recommended.⁴ AI and Google are not replacing one another. They are dividing the customer journey between them. AI provides the recommendation, while Google handles the final validation. Users search for the recommended brand, verify the seller and price and then complete the purchase in the store.

“Research moves into AI, final validation happens on Google, and the purchase remains in the store.”

For merchants, the conclusion is clear: AI product search is moving purchase preparation into AI systems, while the transaction remains with the merchant. Visibility is increasingly created before the customer visits the store. A business that is absent from AI recommendations loses customers before its own website has an opportunity to convince them. This makes AI search one of the most consequential e-commerce trends of the coming years.

Where customers use AI to search for products

AI product search currently takes place across three main types of platform: AI assistants such as ChatGPT, AI features within Google Search and specialized systems such as Perplexity, Microsoft Copilot and Amazon Rufus. Each platform follows its own rules when selecting products and merchants. Understanding the main channels shows where your customers are already searching.

ChatGPT Shopping: Product search in the chat

ChatGPT Shopping is ChatGPT’s shopping feature, which displays relevant products with images, prices, reviews and merchant links directly in the conversation. Users describe what they need, such as an espresso machine under 200 euros, and receive a curated selection with explanations. Product comparison, review summaries and, in some cases, even checkout can happen without leaving the chat.

Merchants should note that OpenAI describes these product recommendations as organic rather than paid placements. The feature focuses on categories such as electronics, household goods, beauty and fashion. Merchants can provide product data through feeds so that ChatGPT displays prices, availability and product details correctly. Our guide to ChatGPT in e-commerce explains further use cases for merchants.

Google AI Overviews and AI Mode

Google AI Overviews are AI-generated summaries that appear above traditional search results and link to selected sources. For product-related searches, Google also uses data from its Shopping Graph, which contains billions of product records from Merchant Center feeds and crawled store pages. With AI Mode, Google is gradually developing search into a conversational assistant that asks follow-up questions and refines product recommendations.

For merchants, this means that some users receive their answer directly on the search results page and no longer click a traditional search result. It therefore becomes even more important to appear as a cited source within the AI Overview or to have product data represented in the Shopping Graph.

Perplexity, Copilot and Amazon Rufus

Alongside ChatGPT and Google, other AI systems are establishing their own product search experiences. Perplexity answers product questions with transparent source citations and displays shopping results as product cards. Microsoft Copilot combines text and image search, allowing users to upload a photo and find similar products. Amazon Rufus advises customers directly within the marketplace and influences product selection there.

Although their interfaces differ, the underlying logic is similar. All these systems use machine-readable product data, crawled content and external sources. Merchants that establish these foundations properly improve their chances across all platforms at the same time.

How AI systems select products for their answers

AI systems select products using three groups of signals: Machine-readable product data, crawled store content and external sources such as product tests, forums and review platforms. A language model does not invent its recommendations freely. It summarizes the information it finds about products and merchants through crawlers, feeds and training data. This is where the decision is made about whether your store is recommended.

The three signal groups in detail:

• Product data and feeds: Structured information about price, availability, variants and reviews, for example from Google Merchant Center or product feeds submitted to OpenAI. These records provide the facts used in product cards and price comparisons.
• Crawled store content: AI crawlers such as GPTBot, OAI-SearchBot and PerplexityBot read product pages, category copy and guides. AI systems use this content to extract descriptions, use case recommendations and supporting arguments.
• External sources: Product tests, trade publications, forums and review platforms provide trust signals. When several independent sources recommend a product, the likelihood that an AI system includes it increases.

Providers do not disclose their exact selection criteria, but clear patterns are visible. Complete and consistent product data, competitive prices, available inventory and a brand that appears positively in independent sources all matter. The reverse is equally true: A store whose product data cannot be read by machines effectively does not exist for AI product search.

GEO: How to make your online store visible in AI answers

Generative Engine Optimization, or GEO, covers all measures that prepare content and product data so that AI systems can crawl, understand and recommend them in their answers. GEO does not replace search engine optimization. It builds on it. Content that is well structured for Google usually also helps ChatGPT and Perplexity. Five areas have emerged as the core of GEO for online stores.

Technical foundation: Access for AI crawlers

The most important technical requirement for visibility in AI search is allowing AI crawlers to access your content. Check whether your robots.txt blocks bots such as GPTBot, OAI-SearchBot, PerplexityBot or Google-Extended, and allow the crawlers used by the systems in which you want to appear. You should also deliver content as server rendered HTML because many AI crawlers cannot reliably read information that is loaded later through JavaScript.

The traditional foundations remain essential as well. Clean indexing, fast loading times, a clear page structure and unambiguous URLs are still mandatory. The established principles in our guide to SEO for online stores therefore continue to support visibility in AI systems.

Structured data and product feeds

Schema.org structured data is the most reliable format for helping AI systems read product information. Add complete Product markup to every product page, including price, currency, availability, GTIN, brand and reviews. Add FAQ and Organization markup so that systems can also identify your business as a distinct entity.

Maintain your product feeds at the same time. The Merchant Center feed supplies Google’s Shopping Graph and therefore also the AI features in Google Search. Merchants can also submit product data to OpenAI for shopping results in ChatGPT. Consistency is critical because conflicting prices or availability between a feed and the website undermine trust in your data.

Content with quotable answers

AI systems prefer to cite content that answers a question precisely, factually and in a self-contained form. Write product copy that resolves specific purchase questions. Explain who the product is suitable for, which dimensions and materials it has and what it is compatible with. Add guides and FAQ sections that answer common customer questions directly and begin each answer with a clear defining statement.

Use the language your customers use. Fifty-four percent of consumers say their search queries have become significantly more conversational.⁵ Natural language content with specific figures, comparisons and recommendations matches these queries better than generic marketing copy. Our guide to AI and SEO explains how to use artificial intelligence as a practical tool.

Reviews and external mentions

AI systems place considerable weight on independent sources, which means reviews and external mentions influence whether your store is recommended. Maintain your review profiles on Google and established services such as Trusted Shops, and actively encourage satisfied customers to leave reviews. A product with many authentic reviews gives an AI system reliable evidence for a recommendation.

Work on mentions outside your own store as well. Product tests, trade articles, industry directories and discussions in forums or communities regularly appear among the sources used in AI answers. Present your brand consistently with the same name, description and core messages so that systems recognize it as a distinct entity.

Protect branded search as the connecting step

The path from an AI recommendation to the store usually passes through Google Search: 55.9 percent of AI-influenced visits arrive through a search query, while only 8.8 percent come from a direct click in the AI system. Users read a recommendation in a chat, end the conversation and search for the brand by name in the following days to verify the merchant, availability and price before purchasing.⁴ Analytics records these visits as organic branded traffic, leaving the original trigger invisible.

Protect this connecting step. Your own pages should dominate the search results for your brand so that demand generated by AI does not flow to marketplaces or competitors. Consider branded campaigns in Google Ads when competitors bid on your brand name because every lost branded search reduces the value of the AI recommendation that created the demand.

How to measure and interpret AI traffic

You can identify visitors from AI product search in web analytics through referrers such as chatgpt.com, perplexity.ai and copilot.microsoft.com. Create a dedicated segment for these sources in your analytics platform and monitor sessions, conversion rate and revenue separately from other traffic. Search Console does not currently report clicks from Google AI Overviews separately, so changes in impressions and click-through rates can serve as indicators.

Referral data shows only part of the picture. Because most AI-influenced visits arrive through branded search, monitoring the development of branded queries in Search Console is equally important. If branded demand increases without being explained by campaigns or seasonality, it can indicate growing visibility in AI systems.

Quality matters more than quantity when evaluating this traffic. Visitor volumes from AI systems are still relatively small, but these users often complete their research in the chat and arrive with a specific purchase intention. Similarweb quantifies the difference: AI-influenced visitors view an average of 12.0 pages and remain on the site for 11.8 minutes, approximately twice as long as other visitors.⁴ Your store therefore needs to convert these prequalified visitors effectively. Our guide explains how to increase your conversion rate systematically.

Using AI product search within your own store

AI-powered store search understands the meaning of a query and returns suitable results even when users make spelling errors, use synonyms or write in natural language. A search for “necktie” can also find ties. A question such as “What helps with a headache?” can return relevant products. Customers already expect this capability. Ninety-three percent consider it important that store search understands conversational queries, while vendor data indicates that AI-optimized search can increase revenue by up to 20 percent.⁵

AI-powered product recommendations and personalized messaging also help visitors see relevant items instead of searching through an entire catalog. Our guide to e-commerce personalization explains the opportunities this creates.

An honest assessment must also recognize that visibility in AI search is only half the job. Stores that lose hard-won visitors to purchase abandonment waste the effect of the entire optimization effort. This is where uptain (uptain.com), a software platform for data-driven e-commerce marketing and cart abandonment reduction, comes in. Intelligent exit intent popups and automated abandonment emails bring back visitors who did not complete their purchase. This turns AI visibility into measurable revenue. Learn more about our software for recovering abandoned carts.

Conclusion

AI product search increasingly determines which products customers consider because the initial selection is moving from the search results page into ChatGPT, Google AI Overviews and other AI systems. Merchants that allow AI crawlers, maintain structured data and feeds, publish quotable content and strengthen their review presence gain an advantage that many competitors have not yet recognized. The purchase itself still happens in the store. This means the new visibility only pays off when the store converts prequalified visitors into customers.

Frequently asked questions about AI product search

How do external AI product search and AI search within an online store differ?

With external AI product search, customers research in ChatGPT, Google AI Overviews or Perplexity before visiting a store. AI search within an online store works inside its product catalog and uses natural language, synonyms and error tolerance to guide visitors to suitable products. Both applications require complete and consistent product data.

Where do customers use AI to search for products?

Important touchpoints include ChatGPT Shopping, Google AI Overviews and AI Mode, Perplexity, Microsoft Copilot and Amazon Rufus. Their interfaces and data sources differ, but they rely on similar foundations. These include product feeds, crawled store content, reviews and external mentions.

How do AI systems select products for recommendations?

AI systems combine three signal groups: Machine-readable product data and feeds, crawled content from product pages and guides, and external trust signals such as tests, forums and reviews. Complete attributes, consistent prices and availability, and positive independent mentions increase the chance that a product appears in an answer.

What requirements create visibility in AI systems?

Allow relevant AI crawlers, deliver product pages as server-rendered HTML, and maintain Product markup plus current Merchant Center and OpenAI feeds. Add quotable product copy, useful FAQ content, reviews and external mentions. The decisive factor is consistency across sources for price, availability, brand and product characteristics.

How can traffic from AI product search be measured?

Track direct visits from referrers such as chatgpt.com, perplexity.ai and copilot.microsoft.com in a dedicated analytics segment. Also monitor branded searches in Search Console because many users verify an AI recommendation later through Google. Sessions, conversion rate, revenue and branded demand together provide a more realistic performance picture.

Sources

¹ Celum / TQS Research & Consulting via ESB Marketing Netzwerk: Studie: KI als Gamechanger beim Shopping (2026), https://www.esb-online.com/artikel/celum-studie-ki-als-gamechanger-beim-shopping/ (last accessed: 23 July 2026)

² Criteo via ONEtoONE: 2026 Commerce and AI Trend Report (2026), https://www.onetoone.de/artikel/db/994997SUR.html (last accessed: 23 July 2026)

³ kernpunkt / commercetools: Studie AI im E-Commerce (2026), https://www.kernpunkt.de/magazin/57-prozent-nutzen-ai-assistenten-beim-online-shopping (last accessed: 23 July 2026)

⁴ Similarweb: The Downstream Impact of AI Visibility (2026), https://www.similarweb.com/corp/the-downstream-impact-of-ai-visibility/ (last accessed: 23 July 2026)

⁵ Doofinder: KI im E-Commerce Statistiken 2026 (2026), https://www.doofinder.com/de/blog/ki-statistiken-im-e-commerce (last accessed: 23 July 2026)