Something significant has been happening in the way people discover products to buy — and it's been easy to miss if you're heads-down managing campaigns.
A growing share of product discovery is now happening through AI interfaces: people asking ChatGPT for product recommendations, using Google's AI Overviews to research purchases, or querying Perplexity for comparisons. These aren't replacing traditional shopping journeys yet — but they're becoming a meaningful part of the funnel, particularly for considered purchases.
Understanding how these AI agents actually find and surface products is becoming as important as understanding how Google Shopping worked in 2019.
For most of the last decade, the main pathways for online product discovery were fairly predictable: Google Search, Google Shopping, social media ads, and word of mouth. Each had established mechanics — optimise your feed, bid well, show great creative.
In 2026, add these to the mix:
AI-generated summaries at the top of search results, increasingly including product recommendations and comparisons
Users asking "what's the best [product] for [use case]?" — getting AI-curated recommendations with links
AI-powered search with commerce features, surfacing products with source citations for product queries
Each of these works differently — but they share a common characteristic: they're pulling from structured data sources, review signals, and web content to generate recommendations. And they're doing it without showing you the traditional ten blue links.
Think of an AI shopping agent like a very well-read personal shopper. It's read millions of product reviews, blog posts, retailer websites, and comparison guides. When you ask for a recommendation, it draws on all of that accumulated reading to give you its best answer.
But here's the key: it can only recommend products it "knows about." And it learns about products through:
AI systems crawl the web and are particularly good at reading structured data — the machine-readable code (schema markup) that tells them: "this is a product, it has these attributes, it costs this much, it has these reviews." Products with rich, accurate schema markup are much more likely to be well-understood and correctly represented by AI agents.
If your product pages are missing Product schema or have incomplete attributes, AI agents may not understand your product well enough to recommend it — even if you sell exactly what someone is looking for.
For Google's AI Overviews and Shopping surfaces, the most direct connection is through Google Merchant Center. Products in a clean, verified Merchant Center feed have a direct line to Google's AI product recommendation systems.
This is a well-established signal Google's AI uses heavily — making Merchant Center participation arguably more important than ever for ecommerce businesses that want AI visibility on Google's surfaces.
Reviews are a major input into AI product recommendations. An AI system asked "what's the best standing desk under $500?" will draw heavily on review data — both structured (schema markup reviews) and unstructured (review articles, comparison posts, forum discussions).
Products with high review volumes, high ratings, and reviews that discuss specific use cases tend to appear more frequently in AI recommendations. This is because AI agents can match "this product has reviews that mention [use case]" to the buyer's stated need.
AI agents trained to surface trustworthy recommendations pay attention to authority signals: Is this product sold by a recognised brand? Does it have a verified presence on Google (a Business Profile, a Merchant Center account, a Google Knowledge Panel)? Has it been reviewed by authoritative third-party publications?
These signals are harder to build quickly but matter significantly for competitive categories where many products exist and the AI needs to make a quality judgement.
The key insight is that the fundamentals of AI product discovery overlap significantly with good SEO and Merchant Center hygiene. If you're already doing these well, you're better positioned than you might think.
The mindset shift: Traditional paid search was about paying to appear. AI discovery is about being worth recommending. The two aren't mutually exclusive — but businesses that invest in the underlying quality signals (good products, accurate data, real reviews) will be better positioned in both.
Honesty matters here: AI product discovery is genuinely new, and the signals that drive it are still being understood. What we know is based on documented API specifications, published research, and observed behaviour — not access to the black boxes inside these AI systems.
What practitioners and researchers at Search Engine Land and elsewhere have observed is that the overlap between "good SEO fundamentals" and "good AI visibility" is significant. Businesses that have invested in structured data, quality content, and legitimate review building are better positioned than those who haven't.
The space will evolve quickly. Watching how Google, OpenAI, and Perplexity develop their commerce features over the next 12–18 months will be important for any business that relies on product discovery.
If you want to improve your AI product discovery visibility, start with the things that have the most established impact:
You don't need to overhaul everything. Start with your highest-margin or highest-traffic products and make sure they're optimised for machine readability. Even modest improvements in this area compound over time as AI discovery becomes a larger part of the customer journey.
Search Engine Land — AI Overviews, Google Shopping AI, and product discovery · Google Product Schema Documentation — Official specification · SEMrush Blog — AI SEO and ecommerce visibility research
Common questions about this topic.
AI shopping agents crawl and index product data from multiple sources: structured product markup (Schema.org) on your website, Google Merchant Center product feeds, Google Shopping listings, and general web crawling. They prioritise products with complete, accurate, and well-structured data — including price, availability, reviews, and specifications — over those with sparse or inconsistent information.
Having a Google Merchant Center account significantly increases your visibility to AI shopping agents. It provides a structured, verified product feed that AI systems can trust and index reliably. While AI agents can crawl your website directly, a Merchant Center feed gives you controlled, up-to-date data that reduces the risk of incorrect prices or availability being surfaced.
Schema.org markup is structured data code added to your website's HTML that explicitly labels product information — price, name, brand, availability, ratings — in a format that search engines and AI agents can read directly. Without it, AI agents must infer this information from your page text, which is less reliable. Adding Product schema markup makes your data machine-readable and increases the likelihood of appearing in AI-powered results.
Product reviews are a major signal for AI shopping agents. They assess review quantity (number of ratings), recency (recent reviews outweigh older ones), and average score. Products with many recent, high-rating reviews are more likely to be recommended by AI agents. Structured review markup using Schema.org AggregateRating also helps agents accurately read and surface your review data.
Start with your product data completeness. Ensure every product has: a descriptive title (brand + product type + key attribute), accurate price and availability, a detailed description with specifications, high-resolution images, and Schema.org Product markup. Then verify your Google Merchant Center feed is approved and free of errors. These two areas — on-site data quality and feed health — have the greatest impact on AI shopping discoverability.
I audit ecommerce catalogues for AI-readiness — schema markup, feed quality, description depth, and review integration — and give you a clear action plan to improve visibility across both traditional and AI-powered discovery channels.
See AI-Ready Catalogue Audit →