Google’s test of AI Mode-integrated Shopping Ads marks a shift from keyword search to context-based search. It changes the way the advertisement interacts with the user; it is no longer a static result, but something that enters into the merits of a conversation. For a brand or service company, the challenge shifts from winning the top spot on the page to the ability to emerge as an ideal solution while the user is still defining his or her needs.
The end of dry transactional research
Until now, Shopping Ads have intercepted an explicit demand. If a user searched for a “smartphone,” Google would show a carousel of prices. In AI Mode, the path is different. The user asks for recommendations, compares alternatives or delves into technical details before arriving at the carousel.
The announcement comes into play long before the final purchase. Being present at this stage allows you to influence choice while the option set is still open. For brands, it means positioning the product as the natural response to a need expressed in everyday language, intervening at the exact moment when preference is formed.
What are shopping ads in AI Mode and how they work
Le shopping ads in AI Mode are dynamic ads powered by Merchant Center data that appear within Google’s text responses. Unlike the classic product carousel at the top of the page, these ads are generated in real time as part of the artificial intelligence response.
If a user asks for advice on a purchase, the AI does not just respond in words, but extracts the most relevant products from the brands’ feed, showing them with images, prices and technical details directly in the text of the conversation. The ad stops being an isolated element and becomes a specific suggestion, chosen by the algorithm because it meets exactly the requirements that emerged during the dialogue. The success of the format thus no longer depends only on the economic offer (bid), but on the completeness of the technical data that allow the AI to consider that product the ideal solution for the user.
Optimizing the feed for conversational positioning
In AI Mode, the quality of the data sent to the Merchant Center becomes the predominant ranking factor. If the information is generic, the algorithm does not have the necessary elements to fish out the product and include it in a complex response or technical comparison. Thus, visibility depends on the precision with which the catalog is structured.
To preside over this space, feed management must evolve on three levels:
- Technical attributes and granularity: titles and descriptions must move beyond the logic of keywords to embrace that of structured data. AI analyzes materials, compatibility, and benefits of use to answer specific questions (e.g., “what are the best shoes for trail running on muddy terrain?”). The more granular the data, the more “digestible” the product is for the algorithm.
- Visual assets and semantic consistency: Google cross-references pixel analysis with text descriptions. High-quality images, contextualized or showing construction details, not only serve the user, but help the AI confirm that the product actually meets the search requirements.
- Data integrity and synchronization: accuracy is a signal of reliability for the algorithm. A discrepancy between the price or availability suggested by the AI and the actual data on the landing page penalizes the brand ranking and degrades the user experience, disrupting the conversion flow.
The transparency of announcement comparison
AI Mode makes the competition between Shopping ads extremely direct. If the user asks for a comparison between models or brands, the system generates a response that lays bare the data from the different ad feeds, side-by-side with prices, reviews, and delivery times.
Ad performance is thus linked to the actual competitiveness of the commercial offering. In an AI-generated comparison, any product weakness (a higher price without technical justification or long shipping time) becomes an element of immediate exclusion. Media planning must therefore include dynamic management of feed attributes to ensure that the ad is always the most advantageous choice in the real-time comparison.
Integrating Shopping Ads in AI Mode into Media Strategy.
This format does not replace Performance Max campaigns or traditional Search, it acts as a top layer of interaction. The challenge for retailers is to coordinate these ads with the rest of the paid activity to avoid redundancies and maximize coverage.
Integration requires a two-pronged approach:
- Synchronization between paid and organic: AI validates the reliability of an ad by cross-referencing it with signals coming from the website. If the product feed claims a technical feature, but the site’s organic content does not support it, the algorithm might exclude the ad from the most authoritative conversational responses.
- Planning for caring intent: While standard Shopping campaigns are optimized for immediate clicks, AI Mode ads require a caring-based strategy. You need to map the questions customers ask during the evaluation phase and make sure the feed attributes answer those specific concerns.
New metrics to monitor
With the introduction of AI Mode, traditional metrics risk becoming biased. If the user receives all the necessary information directly in the AI response, the traditional CTR (Click-Through Rate) may decrease while increasing the quality of the final conversion.
The success of these campaigns will have to be measured by analyzing the relevance of the impressions.
How many times has the product been selected by AI as a “recommended solution” over competitors? This new visibility indicator becomes crucial to understand whether the catalog is working correctly in the user’s decision-making phase.
Toward advertising based on data quality
The shift to AI Mode transforms advertising into automated customer service. For brands and retailers, competitive advantage depends on the accuracy of the information provided, rather than just the ability to spend. Budget ensures space occupancy, while the quality of the data determines whether the algorithm will select the ad as the ideal response.
The Merchant Center evolves into a strategic positioning tool. Optimizing the feed requires building a comprehensive information infrastructure, which is necessary for the AI to propose the ad as the most authoritative and relevant solution for the user.
