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Search has quietly split in two. There's the search engine results page you've spent years optimizing for, and there's a growing layer of AI-generated answers sitting above it, Google AI Overviews, ChatGPT responses, Gemini summaries, Perplexity answer cards. For Adobe Commerce merchants, this shift changes what "ranking" even means.
Getting cited in an AI Overview isn't about beating nine other blue links anymore. It's about being the source an AI model trusts enough to quote. And most Adobe Commerce stores, built for traditional crawlers and classic on-page SEO, aren't structured for that job yet.
This guide breaks down exactly what's changed, why Adobe Commerce sites often struggle here, and the technical and content fixes including Adobe LLM Optimizer Integration that help your catalog show up when AI decides what to recommend.
As of early 2026, AI Overviews have been seen on approximately 48% of the tracked Google search queries, compared to about 32% one year ago, industry tracking data compiled by Advanced Web Ranking and BrightEdge shows. It's not a special feature anymore, it's a part of their search that's standard.
The catch: organic click-through rates on queries that trigger an AI Overview have dropped by roughly 34% to 61% depending on the study, per data from Seer Interactive and Ahrefs. Fewer people click through to a website when the AI answer already satisfies the query.
There's a flip side worth paying attention to. Brands that do get cited inside an AI Overview see meaningfully better outcomes than brands that don't, some analyses put the lift at over 30% more organic clicks and significantly higher paid click performance for cited domains. Visibility inside the AI answer is starting to matter more than the traditional ranking position underneath it.
For Adobe Commerce specifically, this matters because the platform still commands serious enterprise weight; it processes an estimated $173 billion in annual GMV globally and remains used by roughly one in five of the top 1,000 US and Canadian internet retailers, according to platform usage research from 2026. That's a lot of product data, category pages, and buying guides sitting on infrastructure that was never built with LLM readability in mind.
Adobe Commerce is powerful, but its default architecture creates real friction for AI crawlers and answer engines.
Many Adobe Commerce storefronts, especially those on older Luma themes or complex PWA setups, lean heavily on client-side rendering. Googlebot handles JavaScript reasonably well after multiple rendering passes. AI retrieval crawlers, working on tighter time and compute budgets, are far less patient. If your product description, price, or availability data only appears after a script executes, it may never get pulled into an answer at all.
The majority of Magento store development and Adobe Commerce catalogs are based on the manufacturer-provided copy or limited number of attributes (such as size, colour, SKU...). It is okay to use a grid to scan by a human. The LLM's task is not just to grasp why a product is important, who it is for, and how it compares to other products. Descriptive content, that explains use cases and is written in a natural way, is a preferred style for generative engines.
One of the most evident signals that AI systems rely on to interpret entities, prices, reviews, and availability is schema markup. Comprehensive Product, Offer or FAQ schema is not a built-in feature of Adobe Commerce – much of it relies on theme configuration or extensions and gaps can easily creep in across thousands of SKUs.
Large Adobe Commerce catalogs often accumulate duplicate layered navigation URLs, orphaned category pages, and bloated XML sitemaps. That dilutes crawl budget for both traditional bots and AI retrieval agents, making it harder for either to find your best content.
Before touching content, confirm AI crawlers can actually reach your pages. Audit robots.txt to make sure you're not accidentally blocking retrieval agents alongside training crawlers — these serve different purposes and shouldn't be treated identically. Check that critical product and category data renders in the initial HTML response rather than depending entirely on client-side JavaScript. Server-side rendering or hybrid rendering (available through Adobe Commerce's PWA Studio or headless setups) meaningfully improves how reliably AI systems can parse a page.
Adobe LLM Optimizer, generally available since October 2025, is built specifically for this problem and integrates directly with Adobe Commerce catalogs. It monitors how your brand and products currently appear or fail to appear across AI-generated answers, then generates specific, deployable fixes.
For product catalogs, it identifies listings with thin names or descriptions and suggests AI-readable rewrites that translate technical attributes into the kind of narrative context LLMs actually use when generating recommendations. Teams can review each suggestion and deploy it with a single click, directly back into the Adobe Commerce catalog, without manually rewriting thousands of SKUs by hand.
Adobe's own internal case is telling: after applying similar optimizations across Adobe.com, the team saw a 41% increase in LLM-referred traffic and a 200% increase in LLM visibility relative to competitors within a matter of weeks.
Content that directly addresses a question tends to appear at the top of the page without any inferences, which is the trend of AI systems. For product pages, the first sentence or two should be about what the product is, who it's designed for, and a notable aspect of the product. On category and guide pages, include a brief summary block near the top, from which a model could almost verbatim quote.
On pages that address common questions of buyers use a minimum of the following schemas: Product schema (price, availability, SKU), Review and AggregateRating schema, BreadcrumbList, Organization schema with verifiable credentials, and FAQPage schema. Structured data can lead to a citation, but it doesn't always, but AI systems always prefer a source where the facts are unambiguous.
AI Overviews and tools such as Perplexity will often reference pages that exhibit knowledge and proficiency, rather than simply stock. With buying guides, comparison pages and FAQ hubs around actual customer queries, your site has more than a spec sheet to quote from. This also meets Google's E-E-A-T criteria, as it gives a firsthand experience and not manufacturer boilerplate.
LLMs cross-reference brand mentions across the web - review platforms, directories, guest content, press coverage to judge whether a source is trustworthy enough to cite. A consistent brand description, accurate business listing data, and credible third-party mentions all feed into how confidently an AI model will attribute a claim to your store.
Do a site scan for duplicate layered navigation URLs, remove or noindex thin taxonomy pages and mark pages that are in danger of being cannibalized. This results in a cleaner sitemap where Googlebot and AI retrieval agents only crawl the pages that you want indexed.
| Factor | Traditional SEO | GEO / AI Overview Optimization |
|---|---|---|
| Primary goal | Rank in top 10 blue links | Get cited or quoted inside the AI-generated answer |
| Content style | Keyword-optimized, length-focused | Direct-answer, context-rich, quotable |
| Technical priority | Page speed, crawl budget, indexation | Same, plus JS rendering visibility to AI crawlers |
| Structured data | Helpful for rich snippets | Often essential for entity and fact confidence |
| Success metric | Rankings, organic CTR | Citation frequency, AI-referred traffic, share of voice |
| Key tools | Search Console, Ahrefs, Semrush | Adobe LLM Optimizer, AI visibility trackers |
| Content depth | Product-focused | Buying guides, comparisons, FAQs alongside products |
A lot of what AI Overview readiness requires - server-side rendering, structured data, sitemap cleanup, page speed - sits in platform architecture, not just copy. That's where experienced Adobe Commerce development services make the difference. Content edits only go so far if the underlying template can't deliver clean, fast, structured HTML to a crawler working on a tight time budget.
A development partner who fully grasps both the Adobe Commerce stack and AI retrieval can manage hybrid rendering, automatically handle schema across vast catalogs, and rebuild category architecture - tasks difficult to perform just with content changes.
AI Overviews are not search, they're changing who is seen in search. For those running Adobe Commerce stores, that implies the platform's standard SEO method should be enhanced with a technical and content-focused approach tailored to how LLMs consume, trust, and cite content. Tools such as Adobe LLM Optimizer, when paired with a powerful Adobe Commerce development service, provides you with both sides of the equation: the optimisation layer for AI and the platform engineering for doing it at scale.
Bhumi Patel is a Client Partner at Magneto IT Solutions, working with organisations across Australia and New Zealand to deliver valuable business outcomes through modern digital commerce and growth marketing initiatives. She supports B2C, D2C, and B2B brands in aligning commerce strategy, technology execution, and operational priorities to build scalable, high-performance digital ecosystems.
