With over 20 years of experience, we transform your digital presence. We specialize in website and E-Shop development, SEO and Digital Marketing, ERP software and smart automation that take your business to the next level.
Address:
154 Egnatia Street, Thessaloniki 546 36
Phone:
+30 231 231 4095
E-mail:
hello@twodots.gr
Ready for the next step?;
Google's AI shopping is transforming e-commerce by allowing consumers to discover, compare and buy products with the help of AI agents. These agents act as intermediaries, synthesizing data from product feeds, reviews and structured data to offer personalized recommendations. E-commerce businesses need to organize their data, improve SEO and checkout to stay competitive. AI shopping requires an integrated strategy that combines SEO, marketing and conversion optimization.
Google's AI shopping brings information agents and ideas like the universal cart, changing how we discover, compare and buy products. For e-shops, this means better product feeds, cleaner structured data and faster checkout.
Semrush's article on Google's addition of information agents and universal cart describes a shift that e-commerce owners can't treat as just another update to search. AI shopping is not just about more «intelligent» answers to search, but a new environment where consumers can discover products, compare options, evaluate features, receive recommendations and, gradually, complete part of the purchase intent through AI experiences. Simply put, the journey from query to cart is becoming shorter, more guided and more dependent on the quality of the data an online store gives Google.
The most important business reading is that AI agents act as market intermediaries. They are not limited to displaying ten blue links or a classic product grid. They can synthesize information from product feeds, reviews, structured data, return policies, prices, availability, and product page content to answer more complex questions like «which running shoe is best for a beginner with an overpronation and a budget under 120 euros?» In this context, Google AI Mode shopping and universal cart-type features mean that product visibility is not only earned with bids or product titles, but with complete, reliable and machine-readable data.
For an e-shop, the question is not whether AI shopping will directly replace traditional e-commerce. The practical question is which brands will be organized enough to be properly «read» by AI systems, recommended at the right times and reduce friction at checkout. The more search platforms become shopping assistants, the more SEO strategy, Merchant Center, product feed optimization, Performance Max and conversion rate optimization need to be designed as a single system rather than as separate channels.
Information agents are AI functions that gather, organize and explain information on behalf of the user. In e-commerce, this can mean comparing products, evaluating benefits, summarizing reviews, identifying differences in technical features or selecting products based on constraints such as budget, size, color, delivery time or usage. The universal cart concept goes one step further: instead of the user constantly moving from search to site and from site to checkout, the ecosystem can support a more unified shopping cart, where the purchase intent is moved more directly to checkout.
This is the core of agentic commerce. In traditional e-commerce, the consumer searches, opens tabs, compares prices, reads reviews, goes back to Google, enters a marketplace, abandons carts and finally buys when they feel confident enough. In agentic commerce, an AI agent can do much of this work: filtering options, explaining why a product is a better fit, suggesting alternatives, and reducing cognitive load. This doesn't remove the need for brand, site experience or SEO. On the contrary, it makes them more demanding, because the brand must prove its credibility not only to humans, but also to the systems that organize the information.
Google has already reported that the Shopping Graph contains over 50 billion product listings and is updated over 2 billion times per hour. This size shows why product data is not a technical detail, but a commercial asset. When millions of products are competing for the same purchase intent, the accuracy in titles, descriptions, GTIN, attributes, prices, stock status and shipping data determines whether a product is suitable to be displayed in an AI shopping environment.
As shown in the graph below, the scale of the Google Shopping Graph is such that product management should be treated as a continuous data process rather than a one-off catalog entry.
Source: Baymard Institute, Checkout Usability Research
Source: McKinsey Global Survey on AI, 2024
Preparing for AI shopping doesn't require panic, but discipline. The right plan starts with product data, moves to technical SEO, and ends with checkout and measurement. For a business that wants to stay competitive, the goal is to make its catalog understandable, trustworthy and marketable by humans, search engines and AI agents at the same time.
Then align the feed with the landing pages. If the price on the site differs from the Merchant Center, if the product appears available in the feed but out of stock on the site, or if the color and size variations are not properly mapped, an inconsistency is created that can affect both ads and AI system trust. For e-shops with a large catalog, it is worth creating data enrichment rules by category so that attributes are not filled in piecemeal. In fashion products, for example, material, fit, gender, occasion and seasonality are critical. In electronics, technical specs, compatibility, warranty and energy class take priority.
At checkout, remove mandatory steps where they are not needed. Offer guest checkout, popular payment methods, a clear cost summary before the last step and quick order confirmation. If you have omnichannel commerce functions, such as store pickup or physical point of return, make sure they are clear at the product and checkout level. Automated checkout will not become an advantage for everyone; it will become an advantage for those who have already simplified their processes.
Finally, count in a way that connects the channels. Track queries leading to product discovery, impressions in Shopping, click-through rate, add-to-cart rate, checkout abandonment, conversion rate, refund rate and margin by category. AI shopping is not optimized by traffic alone. It is optimized with commercial quality. If a product brings clicks but too many returns, AI-driven merchandising needs to be aware of it. If a category has high demand but low conversion due to incomplete information, the solution may be content and data enrichment, not necessarily a larger ad budget.
The right strategy is to treat AI shopping as a new layer of infrastructure on top of existing e-commerce. It doesn't replace SEO, performance marketing or CRO, but it forces them to work more closely together. The SEO team needs to know what's missing from the feeds. The performance team needs to understand which products have real margin and stock. The content team needs to create leads that are linked to commercial categories. Development needs to ensure structured data, speed and reliable checkout. And management needs to see product data as an asset, not a file that is updated when there is time.
For immediate implementation, start with three priorities. First, audit the 20% categories that bring in 80% of revenue and improve titles, attributes, images, reviews and structured data. Second, identify the checkout abandonment points and fix anything related to hidden costs, bill, payments and delivery. Third, created content that answers real purchase questions, not just keywords. This makes your brand more useful to humans and more understandable to AI agents.
AI shopping will favour e-shops that have a clear value proposition, reliable data and a fast shopping experience. Those that rely solely on offers, weak feeds or generic product descriptions will struggle to stand out in environments where AI comparison becomes more rigorous. The opportunity, however, is great: if you organize your catalog, content and checkout correctly, you can show up not just when a user searches for your brand, but when they ask for help deciding what they really need to buy.
Read also: SEO for eCommerce: basic steps and Reactive AI vs Genetic AI.
Sources: Semrush: Google Adds Information Agents And Universal Cart | Google Shopping updates and AI shopping experiences | McKinsey: The State of AI | Baymard Institute: Cart Abandonment Rate Statistics | Google Search Central: Product Structured Data | Google Merchant Center: Product Data Specification
Google's AI shopping is a new approach to e-commerce that uses artificial intelligence to guide consumers in discovering and buying products. It includes features such as information agents and universal cart, making the process from search to cart shorter and more data-dependent.
AI shopping affects SEO and advertising as it requires e-shops to have clean and reliable product data. Product feed quality, descriptions and structured data are now critical for product visibility in AI environments.
Information agents gather and organise product information, while the universal cart enables a unified shopping experience. These features reduce friction from search to checkout, making the process smoother and faster.
E-shops need to focus on optimizing product data, Merchant Center and checkout. They need to ensure that information is clear and reliable, and content must answer real questions from users.
One of the main challenges is the need for complete and accurate product data. Failure to manage this data can negatively impact the visibility and competitiveness of an e-shop in AI environments.
AI shopping improves the shopping experience by providing more personalised and guided product recommendations. It uses data to answer complex questions and suggest products that best fit the consumer's needs.
We optimize product data, structured data and checkout so your store is read correctly by Google and converts better.
