Google AI Overviews now appear above the traditional results for a meaningful share of commercial searches in the UK. For ecommerce brands, that means the click-through rate on position one is no longer the right benchmark. The benchmark is whether your brand is named, cited or linked inside the AI Overview itself, and whether the customer ever scrolls past it.
Optimising for AI Overview citation works differently to traditional ranking. The signals weighted are different, the content structures favoured are different, and the page work that earns the citation is different. We covered the broader question of how SEO and AI SEO/GEO sit alongside each other in SEO vs GEO. This article zooms in on AI Overviews specifically: what they pull from, how to make a product or category page eligible for citation, and where the work overlaps with the traditional SEO programme already running.
What AI Overviews actually pull from
AI Overviews don't pull only from the top organic results. The content cited inside an Overview frequently comes from pages ranking outside the top ten on the same query. Google's generative model retrieves passages it considers most relevant to the question, then attributes them to the source. Position one is a strong signal but not a sufficient one.
What the model favours: clear, declarative passages that answer a specific question. Structured data confirming what a page is and what it sells. Consistent entity signals tying the page to a recognised brand. External corroboration from sources Google already trusts in the category. Pages optimised for traditional rankings without these structural signals tend to be skipped over by the Overview even when they rank well beneath it.
What the model deprioritises: thin product descriptions, marketing-led headers without substantive content, ambiguous category pages with no descriptive copy, and pages with weak structured data. The traditional ecommerce stack of product grid, faceted nav and minimal copy is exactly the structure AI Overviews struggle to cite.
The five page-level signals that matter
Product schema, properly populated. Not just the minimum required fields. Brand, GTIN, MPN, full Offer data including price, availability, condition and shipping, plus aggregateRating where reviews exist. AI Overviews lean heavily on Product schema to identify what a page sells and whether it's a credible source for a recommendation. We've covered which schema types matter most across AI search in schema markup for AI search.
Category page descriptive content. Most ecommerce category pages have a short SEO intro paragraph, a product grid, and nothing else. AI Overviews can't cite a grid. Adding genuine descriptive content covering the category, the buying considerations, the use cases and the product types within it makes the page eligible for citation. Three to five hundred words of substantive copy beats two paragraphs of keyword-stuffed intro. We've actually written a detailed post about how to find content gaps for ecommerce brands looking to rank in AI mentions.
FAQ content with FAQPage schema. AI Overviews surface answers, and FAQ blocks are pre-formatted answers. A category page with five to ten genuinely useful FAQs covering buying decisions, sizing, materials, compatibility or use cases gives the model exactly the kind of structured Q&A it can cite.
Entity consistency across the site and the open web. Google identifies brands as entities. Consistent Organization schema, a knowledge panel where eligible, accurate Wikipedia or Wikidata presence in larger brands, and consistent brand mentions across third-party sources all reinforce that your brand is a real, recognised entity worth citing in a recommendation.
Third-party corroboration. AI Overviews frequently cite review sites, comparison guides and editorial content alongside brand pages. Being mentioned in those sources, through digital PR, expert contributor placements and product seeding, increases the chance the model surfaces your brand as an answer. The blue-link ranking and the AI citation are different prizes won by overlapping work.
Which queries trigger AI Overviews for ecommerce
Not every commercial query triggers an AI Overview, and the trigger pattern is worth understanding before deciding where to invest. Overviews are most likely on informational and considered-purchase queries, the kind that involve research, comparison or decision-making.
High-trigger ecommerce queries: best-of and category recommendations (“best electric bikes for commuting”), comparison queries (“Brand A vs Brand B”), buying guides (“how to choose a sofa size”), specification questions (“what watt vacuum for hard floors”), and use-case prompts (“hiking boots for wet weather”).
Low-trigger queries: pure transactional searches (“buy [exact product name]”), branded queries (“[brand] returns”), and tightly local queries. These still run through traditional results without an Overview, which is a reminder that the SEO and GEO programmes need to coexist rather than substitute. Knowing which queries trigger an Overview in your category is the foundation, and the methodology for that audit sits inside the broader work on measuring LLM visibility.
How to prioritise the work
AI Overview optimisation is a programme, not a checklist. Done well, it sequences from highest commercial impact to lowest, with the foundations done first because they're prerequisites for the rest.
Step one is the audit, identifying which queries in your category trigger AI Overviews, which brands the Overviews currently cite, and where your brand sits on each query. This produces a prioritised list of opportunities ranked by commercial value, not by ease of execution.
Step two is the schema and structured data foundation. Product schema fully populated, Organization schema, FAQPage schema on the pages where it's appropriate. This work supports both AI Overview citation and traditional ranking, so the budget compounds.
Step three is the content work, which means expanding category descriptions, adding genuine FAQ content, and restructuring product copy so the buying considerations are explicit and citable. This is the most time-intensive layer, and also where the biggest gains in citation rate sit.
Step four is the external authority work: digital PR, expert placements and product seeding aimed at the third-party sources Overviews cite alongside brand pages. This compounds with the on-site work and is what typically separates the brand that's mentioned occasionally from the brand that's recommended consistently.
How Imaginaire approaches AI Overviews for ecommerce
AI Overview optimisation is one of the core scopes inside our AI SEO services. We start with a category-specific audit identifying which queries trigger Overviews, which competitors are currently cited, and where the easiest gains sit. The audit is the same one we offer free to prospective clients, so the methodology is open.
From there, the schema, content and authority work runs alongside the existing ecommerce SEO programme rather than as a separate workstream. Most of the technical foundation overlaps. The channel-specific work, like entity signals, AI-readable content structure and third-party citation building, sits on top.
Reporting covers AI Overview citation rate alongside traditional rankings and organic revenue, so the commercial picture is one view rather than three. The work is judged on the combination, not on AI visibility alone.
If you're trying to work out where you currently sit in AI Overviews for the queries that matter in your category, we'd be happy to put together a free audit benchmarking your current position and the work that would move it most.
