Is GEO Just Good SEO? What 1,899 AI Prompts Tell Us About Brand Visibility

A question is reshaping how marketing teams plan their budgets. If a brand ranks well on Google, does that automatically mean it will appear in AI-generated responses? The instinct is to say yes. If AI systems learn from the web, and good SEO means more web presence, then better SEO should translate into better AI visibility.

We ran 1,899 AI prompts across 26 businesses on the Contxt network, spanning ChatGPT and Google AI Overview, to test that assumption. What we found challenges one of the most commonly held beliefs in digital marketing right now: that Generative Engine Optimisation is simply Search Engine Optimisation applied to a new interface.

It is not. And the data makes this uncomfortably clear.

What we measured

The Contxt platform tracked 26 businesses across ChatGPT and Google AI Overview, running 838 unique prompt variations and completing 73 full tracker runs. Each prompt asked a realistic buying, research, or comparison question within a category. We then measured whether each brand was mentioned in the AI response, producing a mention rate per business per provider.

The businesses span a range of sectors: consumer retail, industrial B2B manufacturing, logistics, outdoor apparel, pet supplies, healthcare products, entertainment venues, and specialist trade suppliers. They range from household names with significant SEO footprints to niche operators with modest web presences.

The overall network mention rate across all businesses and prompts was 64.6%. But that average conceals a distribution that tells a far more interesting story.

Why specificity beats scale in AI responses

To understand why this happens, it helps to think about how AI language models form associations between brands and contexts.

During training, these models process enormous volumes of text. When a brand is consistently mentioned in the context of a specific, narrow topic, the model builds a strong association. An industrial equipment supplier appears in trade publications, technical forums, manufacturer specification sheets, and product reviews, all within the same narrow category. The model learns that when the topic is this specific industrial process, this brand is a highly probable reference.

A multi-category retailer, by contrast, is mentioned in an enormous variety of contexts. It appears in lifestyle content, home improvement guides, garden centre comparisons, technology reviews, and seasonal promotions. The association between this brand and any single category is diluted across thousands of contexts. The model knows the brand exists, but cannot confidently connect it to any specific buyer question.

This is fundamentally different from SEO, where a brand with strong domain authority can rank for almost any category it targets, provided it has the relevant content. AI systems do not work this way. They answer the specific question being asked, and they prefer the brand that is most unambiguously associated with that question's context.

The outdoor apparel brand in the dataset illustrates the upper-mid range of this dynamic. With a focused product range, a dedicated customer base, and content that consistently ties the brand to specific outdoor activities and conditions, it recorded 84% GEO visibility. It is not a niche B2B operator. It is a consumer-facing brand with real breadth. But its breadth exists within a defined lifestyle category, and that definition is enough to establish reliable AI associations.

The cross-provider divergence problem

One finding that the GEO-as-good-SEO argument cannot easily explain is the cross-provider divergence rate in the dataset.

Across the network, 33% of prompts produced different brand mentions when run on ChatGPT versus Google AI Overview. The same brand, the same prompt, different providers, different visibility outcomes. If AI visibility were simply a function of SEO quality, the results should be more consistent. Good SEO would produce good signals for all AI systems. But this is not what the data shows.

The divergence reflects structural differences between how each platform ingests and weights information. Google AI Overview draws heavily on Google's indexed web and tends to favour sources with strong citation authority in a given topic area. ChatGPT, trained on a broader and differently weighted corpus, produces different brand associations for the same queries.

A major logistics and parcel delivery operator in the dataset makes this concrete. It recorded 100% ChatGPT visibility but only 40% on Google AI Overview, a 60-percentage-point gap. This is a brand with significant public recognition and a well-established web presence. The ChatGPT figure reflects its strong general brand association with delivery and logistics. The Google AI Overview figure reflects that platform's tendency to cite specific, authoritative sources for category queries, and a broad logistics operator competes across too many subcategories to dominate any single comparison clearly.

For a brand optimising only for search, this divergence is invisible. You rank or you do not. But for a brand managing AI visibility, the divergence represents a real strategic challenge: the tactics that make you visible on one platform do not guarantee visibility on the other, and treating them as a single channel produces a misleading picture of your actual AI presence.

Where SEO still contributes

To be clear: SEO is not irrelevant to AI visibility. It contributes, but not in the ways that traditional SEO thinking would predict.

What matters is not traffic volume or domain authority in the abstract. What matters is whether the content your brand has published is the kind of content that AI systems absorb and reference. Technical content that answers specific questions precisely. FAQ structures that mirror the way buyers phrase real queries. Structured data that helps AI systems understand what your brand does and in which specific context it is authoritative.

High-performing GEO brands in the dataset, even those with modest traffic, share one characteristic: their content is specific, consistent, and closely tied to a single category or problem type. Their approach to content is not breadth-first. It is depth-first. That depth is what AI systems extract and retain.

Brands that have pursued traffic-maximising SEO strategies, targeting a wide range of keywords across many categories, find themselves in a position where their web presence is broad but shallow from an AI perspective. The model has learned that this brand sells many things. It cannot reliably say it is the best answer to any specific thing.

The distinction matters because it changes the brief for content teams. SEO content optimisation focuses on keyword coverage, backlink acquisition, and page authority signals. GEO content optimisation focuses on something different: how consistently does your published content place your brand name in the same specific context, using the same specific language that buyers use when asking AI systems questions in your category?

The category authority principle

The data points to what the Contxt network results describe as the category authority principle in AI visibility: the brands that consistently appear in AI responses are not the biggest brands in a market. They are the brands that have established the clearest, most consistent association between their name and a single, well-defined category or problem type.

Industrial specialists score 100% not because they have invested more in content marketing than consumer retailers. They score 100% because every piece of content they have ever published is about the same narrow topic. The AI system has processed references to these brands consistently in the same specific context. The association is unambiguous and reliable.

The logistics operator with a 60-percentage-point gap between ChatGPT and Google AI Overview has a different challenge on each platform. On ChatGPT, it benefits from broad brand recognition. On Google AI Overview, it needs the kind of citation-level authority that comes from being referenced in category-specific, authoritative sources, not just from having a large domain. These are different problems requiring different interventions. Neither of them is simply do better SEO.

This principle has direct implications for how brands approach their content strategy. The instinct is often to produce more content. The data suggests the answer is to produce more specific content, consistently anchored in a defined category. Depth in a defined category creates the kind of association that AI systems can reliably make. Breadth diffuses it.

What this means for how you approach GEO

If you are a brand with broad category coverage and strong SEO metrics that do not translate into AI visibility, the dataset points to several areas worth examining.

The first is category clarity. Can an AI system confidently say what specific problem you solve, for whom, and better than whom? If your brand positioning is deliberately broad, that will be reflected in your AI visibility. Broad positioning served search well. It serves AI considerably less well.

The second is content specificity. The pages that drive your organic traffic may be optimised for keyword volume. The content that drives AI visibility is optimised for precision, using the exact language that professionals or buyers in your category use when phrasing questions. These are not the same editorial strategy, and one does not automatically produce the other.

The third is provider-level measurement. The 33% cross-provider divergence in the Contxt dataset means that a brand reporting a single AI visibility score is averaging away meaningful information. ChatGPT visibility and Google AI Overview visibility reflect different signals and require different interventions. Treating them as one number gives you the average of two different problems.

The fourth is competitive mapping at the prompt level. In many sectors, the brands winning AI visibility are not direct competitors in the traditional sense. They are the most specific players in adjacent or overlapping categories. Understanding the full picture requires running the prompts your buyers use and mapping who appears in your place when you do not. The gap analysis is more actionable than a headline score.

The answer to the question

Is GEO just good SEO?

The data from 1,899 prompts across 26 businesses says no, and the evidence is not close.

A national multi-category retailer with significant organic search presence and household brand recognition records 17.5% AI visibility. An industrial B2B specialist with a fraction of its digital footprint records 100%. An entertainment venue with strong social media coverage and multiple locations records zero ChatGPT visibility. A fuel treatment specialist that most general buyers have never heard of is mentioned every time.

The mechanisms are different. The signals that matter are different. The strategic interventions required are different. Borrowing SEO thinking and applying it directly to AI visibility optimisation will produce some results in some contexts. But it will also leave unexplained why brands performing well by every traditional SEO measure are surprised to find themselves absent from AI responses in their own category.

GEO requires its own framework: category specificity over category breadth, depth over volume, and a separate analytical lens for each AI provider in the landscape. The brands that understand this distinction early will build AI visibility advantages that are genuinely difficult for broader, traffic-first brands to replicate, because they are building something that compounds differently. Not link authority. Category association. And in an AI-mediated buying environment, that association is what gets a brand mentioned when a buyer asks a question and an AI system answers it.

The question is not whether your SEO is good. The question is whether your brand has said the same specific thing, in the same specific context, enough times for an AI system to know exactly when to mention you. That is a different task entirely.