Marketers often talk about "AI visibility" as though ChatGPT, Gemini, Google AI Mode, Perplexity, and Copilot were different interfaces pointing to roughly the same source set. Current citation research suggests that assumption is unsafe.
A 2026 analysis published by OrganiKPI examined 153,425 citations across 5,000 queries and six AI platforms. One of its most striking findings was that Google AI Mode and Gemini shared only 4.66% of cited domains in that dataset, even though both are Google products and use Gemini model technology.
The study also found large differences in citation behavior across ChatGPT, Perplexity, Copilot, Grok, Gemini, and AI Mode. That means a company can perform well in one answer engine and remain nearly invisible in another.
For marketers, AI search visibility should therefore be measured by engine rather than collapsed into one universal score.
Shared model technology does not guarantee shared citations
The AI Mode and Gemini result is especially useful because it removes a common assumption. If two products come from the same company and use related model technology, it is tempting to expect them to retrieve similar sources.
The OrganiKPI dataset found otherwise.
AI Mode accounted for 88,392 citations in the study, while Gemini accounted for 13,487. Yet their cited domain overlap was very small. Their interfaces also exposed different citation mechanics. In the May 2026 dataset, AI Mode no longer included text fragments that revealed the exact cited sentence, while 84.1% of Gemini citations still carried those fragments.
This demonstrates that retrieval architecture, product design, search integration, ranking systems, and citation rendering can create meaningfully different outcomes even when the underlying models are related.
A marketer cannot therefore optimize "for Gemini" once and assume that Google AI Mode is covered.
Different engines create different competitive realities
Imagine a software company tracking 100 commercial prompts.
It might appear in 45 ChatGPT answers, 30 Gemini answers, 18 AI Mode answers, and 12 Perplexity answers. A competitor might show the opposite pattern. A blended visibility score could make the two companies look similar even though their actual platform strengths are completely different.
That matters because users do not distribute themselves evenly across every engine. A B2B buyer may rely heavily on ChatGPT. A consumer may encounter AI Mode inside Google. Another researcher may prefer Perplexity because of its citation interface.
A useful GEO vs SEO framework therefore needs to preserve platform differences. Traditional SEO already separates Google from Bing when their results diverge. AI search needs at least the same level of care.
The practical reporting unit should be prompt, engine, brand outcome, source, and time period.
Citation sources reveal where optimization work belongs
The OrganiKPI study found that YouTube and Reddit were the two largest individual citation sources across its dataset. It also found that only 23.05% of cited URLs appeared in the organic Google top ten for the same query.
Those findings do not mean every brand should flood Reddit or start producing videos for every topic. They show why website only optimization can miss a large part of the information environment AI systems use.
If a competitor is repeatedly recommended because it appears in community discussions, reviews, videos, industry media, or trusted reference pages, improving an on page heading may do little to close the gap.
This is one reason best GEO tools for product marketing teams should be evaluated on source discovery as well as mention tracking. The most useful question is often not only "Did we appear?" but "What information did the engine rely on instead?"
Source analysis turns AI visibility from a scoreboard into a diagnostic process.
One universal GEO checklist is likely to age badly
The rapid change in citation behavior is another reason to avoid fixed recipes.
In the OrganiKPI research, AI Mode text fragment behavior changed dramatically between earlier and later datasets. A technique that helped researchers identify exact cited passages stopped working when Google changed the citation format. Gemini moved in the opposite direction and exposed fragments more frequently.
That kind of product change will continue. AI systems are still evolving quickly, and their retrieval layers, browsing behavior, citation formats, model versions, and user experiences are not stable.
A universal checklist built around one current platform quirk can therefore become obsolete quickly.
Durable work looks different. Publish useful information. Make key claims clear. Support them with evidence. Keep product and company facts current. Earn third party references. Monitor which sources engines actually use. Then adapt the distribution mix based on observed gaps.
The Mustard Seed AEO strategy approach fits this model because it starts with audience questions and evidence rather than a promise that one technical tactic controls every answer engine.
Multi engine measurement needs consistent prompts
Comparing engines requires discipline.
If a marketer asks ChatGPT one question, Gemini a different question, and AI Mode a third, the results cannot be compared cleanly. The prompt set should be built around real buyer tasks and kept as consistent as the interfaces allow.
Results should also be sampled repeatedly because generative answers can vary. A single run can tell you what happened once. A repeated sample can show whether a pattern is persistent.
Teams should record mention frequency, recommendation position where meaningful, sentiment, citations, cited domains, and major competitor presence. They should also note platform changes that may affect the series.
The goal is not false precision. It is enough consistency to distinguish a real pattern from random variation.
GEO is becoming portfolio management across answer engines
The phrase "rank in ChatGPT" is becoming too narrow for serious AI visibility work.
Marketers increasingly need to manage a portfolio of answer engines, each with different users, retrieval behavior, citation preferences, and commercial importance. A brand may decide that ChatGPT and Google AI Mode deserve the most attention while Gemini and Perplexity remain secondary. Another company may make the opposite choice.
That prioritization should come from customer behavior and commercial value, not from whichever AI visibility dashboard is easiest to buy.
The citation data makes one lesson clear: visibility in one AI system does not guarantee visibility in another. A GEO strategy that treats the market as one engine risks optimizing the wrong sources, missing competitor advantages, and reporting an average that hides the real problem.
Originally published on the Mustard Seed blog.
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