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Most groups speak about “AI visibility” prefer it’s one factor. New knowledge on 3.7 million citations throughout ChatGPT, Perplexity, and Google AI Overviews suggests it isn’t. And the hole between the three engines is wider (and extra strategically necessary) than your dashboard doubtless admits.
Immediately’s memo breaks down:
- Why a blended AEO rating hides the one discovering that issues.
- Which web page sorts and domains truly journey throughout engines.
- The shift from measuring AI presence to measuring portability.
One of many largest variations between AEO and web optimization is that AEO performs on extra platforms.
Omnia knowledge reveals throughout a number of samples that solely 2.35% to 2.45% of cited URLs appeared in ChatGPT, Perplexity, and Google AI Overviews for a similar immediate. 91% of citations appeared in just one engine.
Conclusion: AI visibility isn’t a single leaderboard. As a substitute, it’s three completely different distribution methods that typically overlap and normally don’t.
Solely 2% Of URLs Get Cited By All 3 Engines
Most individuals would guess that if a URL will get cited by one main AI engine, it has an affordable shot at showing within the others.
However the 20,000 immediate pattern reveals solely 2.37% of cited URLs present up throughout all three engines for a similar immediate.
In the meantime, 91.07% present up in just one. These two numbers belong subsequent to one another as a result of they clarify one another. The remaining ~7% overlap in pairs, which suggests engines are drawing from largely disjoint swimming pools quite than rating the identical pool in another way.
For AEO/web optimization groups, meaning a single composite visibility rating is the unsuitable unit of measurement. Averaged AEO scores cover this. A model can look sturdy in combination and be invisible in 2 of three engines. Groups chasing one blended AI visibility quantity are compressing three rating methods into one metric and calling it technique.
The two% Holds Throughout Each Reduce
The ~2% overlap fee and ~91% unique fee keep nearly completely flat throughout 4 samples.
That consistency issues greater than the precise decimal level. The consensus hole isn’t an artifact of 1 question set or one time window. It seems structural.
In Q3 2025, common overlap was 2.2%. In This autumn 2025 and Q1 2026, it rose to 2.7%. Engine-exclusive citations fell from 90.1% to about 88%. So sure, a small quantity of convergence. However even after that shift, fragmentation nonetheless dominates.
Business Prompts Don’t Converge Both
The intent break up is without doubt one of the quietest however most helpful elements of the dataset. You might argue that business queries ought to produce extra consensus. When somebody searches for [the best CRM], [best running shoes], or [best project management software], the pool of acceptable sources feels narrower than it does for broad informational prompts.
Surprisingly, the information doesn’t assist a giant distinction.
Business prompts present 2.4% common overlap. Informational prompts present 2.0%. Even when the question ought to slender the reply set, the engines nonetheless select completely different sources more often than not.
That pushes towards a standard intuition in web optimization and content material technique. Groups typically assume high-intent queries are the place shared authority will present up. The alternative seems nearer to true. Even in business territory, every engine’s personal retrieval logic, what sources it trusts, what codecs it prefers, is doing a lot of the work.
Guides Beat Homepages By 2x
The web page sort breakdown under reveals guides and tutorials have the best cross-engine overlap at 2.3%, adopted by blogs at 1.8%, class pages at 1.6%, product pages at 1.2%, and homepages at 1.1%.
Two classes:
- First, explanatory content material travels higher than model or transactional belongings. If you’d like the very best shot at exhibiting up throughout engines, the strongest candidate isn’t the homepage and never the product web page. It’s the web page that helps, explains, compares, or teaches, however take into account that these are additionally content material codecs that AIs can reply instantly properly.
- Second, even the very best web page sorts carry out badly in absolute phrases. Guides are usually not successful throughout engines in any significant sense. The appropriate learn on this isn’t “publish extra guides and you’ll win in every single place.” It’s less complicated than that: Useful content material travels higher than model content material.
Visibility Is Not The Identical As Portability
One of many best errors on this area is to confuse quotation frequency with quotation portability. Wikipedia is the cleanest instance. It seems 16,073 occasions within the dataset, however just one.3% of these appearances are common throughout engines. Reddit seems 14,267 occasions, however solely 0.1% are common. Reuters reveals up 1,202 occasions and nonetheless lands at 0.0% common overlap.
That’s the reason an necessary metric is portability. A website can present up throughout one engine and barely journey, which suggests a model trying dominant in an combination dashboard could also be one platform’s behavior away from invisibility. Presence tells you whether or not you’re seen. Portability tells you whether or not that visibility is resilient.
What This Means For Operators
The sensible implication is easy: Cease treating AI visibility as one factor. Look at the excellent visibility of your area by measuring:
1. Presence, the % of your tracked prompts the place your area seems in any engine. Presence tells you whether or not you’re seen.
2. Portability, the % of your cited URLs that seem in all three engines. Portability tells you whether or not that visibility is resilient.
3. Focus, the % of your citations that come from a single engine. Focus tells you which of them engine your present dashboard is secretly constructed on.
If the overlap between engines is that this low, a single AEO technique is just too summary to be helpful.
Once we method AI visibility from a holistic perspective, it forces sharper questions:
- Which engine issues most for us?
- Which of our belongings journey throughout engines, and which solely work in a single?
- Are we measuring presence once we must be measuring portability?
This additionally modifications how model groups ought to take into consideration diagnostics. A weak homepage throughout engines might not be a homepage downside. It’s a symptom of one thing broader: Engines favor utility over model centrality. In that world, visibility comes much less from being the official supply and extra from being the helpful supply.
The strategic query is now not, “How can we rank in AI?” We should always as a substitute be asking ourselves, “How can we construct belongings that survive completely different engine preferences?” That may be a narrower query. It’s also a greater one.
Methodology
There are just a few caveats to this evaluation:
- The dataset is skewed towards Omnia’s buyer base.
- The intent and page-type cuts depend on regex classification, which is helpful for directional evaluation however not good taxonomy work.
These caveats don’t weaken the principle discovering a lot. The largest sign isn’t precision on the edges. It’s consistency on the heart. Regardless of how the cuts change, the identical sample resurfaces: little or no overlap, very excessive engine-specificity, and solely modest variations by time, intent, or web page sort.
Dataset Dimension And Time Window
The evaluation attracts on 4 immediate samples. Three cohorts of 5,000 prompts every, tracked from Jan. 1, 2025; July 1, 2025; and Jan. 1, 2026. A separate 20,000-prompt random pattern underpins the headline 2.37% and 91.07% figures. The time-view lower spans Q3 2025 by way of Q1 2026 (thus far) and covers 3.7 million URL citations in complete. Business/Informational/Different intent splits are drawn from roughly 2.6 million URLs throughout the mixed pattern. Web page-type splits span 4.1 million URL appearances.
How Prompts Have been Chosen
The 20,000 prompts are drawn as a random pattern from Omnia’s reside immediate monitoring pool. The pool displays what actual advertising groups selected to trace, weighted towards Omnia’s buyer geography (Spain-heavy, plus UK, Nordics, and different EU markets). Every immediate runs in its nation’s major language, so Spanish is overrepresented versus a U.S.-only dataset. Trade combine is fintech/insurtech, journey, SaaS, B2B companies. Deal with findings as directional for European AI search.
Engine Protection
The examine covers three engines: ChatGPT, Perplexity, and Google AI Overviews. Every fires the identical immediate concurrently inside the similar minute, twice a day, with nation localization, and every engine queried in its default web-enabled, unauthenticated state. Perplexity monitoring runs on Sonar, whereas ChatGPT and Google AI Overviews use every vendor’s default manufacturing mannequin for logged-out internet looking (which neither OpenAI nor Google pins publicly to a particular model).
Classification Methodology
Intent and web page sort are assigned by regex. Intent buckets are Business, Informational, and Different. Web page-type buckets are Information/tutorial, Article/weblog, Class web page, Product web page, Homepage, Wikipedia, and Different. The principles are keyword- and URL-pattern-based, which makes them quick sufficient for a multi-million-URL dataset however coarse on the edges. Edge circumstances fall into Different, which is why Different carries a excessive share in each the intent and page-type tables. Deal with the regex cuts as directional, not authoritative.
Extra Assets:
Featured Picture: FGC/Shutterstock; Paulo Bobita/Search Engine Journal
