While preparing our white paper* on brand visibility in conversational AI, we did what a lot of people have been doing lately: we tried to pin definitions on the acronyms surrounding AI Visibility. AEO here, GEO there, LLMO a little farther down the page. We built a very tidy chart 😅, but it told a much tidier story than reality and made all the blurred edges between those definitions disappear.

The definitions overlap, the tools do not all measure the same thing, and every week seems to bring a new term. We could dismiss all of it as vocabulary. That would be a shame, though, because these words are trying to describe a real change: part of the search journey now happens inside interfaces that assemble an answer for us, instead of handing us a list of links and leaving us to build the answer ourselves.

The uncomfortable part is how quickly each acronym can start to look like a fully established discipline, complete with rules, playbooks and even a standard score. We’re not there yet. The field is still taking shape, and an observation or working hypothesis shouldn’t quietly turn into an established rule along the way.

Before we go any further, we should probably sort through the vocabulary.

Useful words, with somewhat fuzzy boundaries

Let’s start by acknowledging something: depending on who is using the terms, AEO and GEO may refer to very similar work. AEO, or Answer Engine Optimization, is generally used for work that helps information get picked up in a direct answer. The term was already used for featured snippets (the answers Google sometimes places above its list of links) and for voice assistants reading an answer out loud. Today, it is also used for AI-generated answers.

GEO, or Generative Engine Optimization, refers more specifically to the visibility of content inside answers assembled by generative engines.

In practice, AEO and GEO overlap quite a bit, which is why they are sometimes used as synonyms.

LLMO, or Large Language Model Optimization, is often used when talking about work that helps large language models understand or represent a brand more accurately. An LLM is one of the technical building blocks that allows conversational AI tools such as ChatGPT, Claude, or Gemini to generate text.

AI Visibility describes the outcome we are trying to observe: where does the brand appear, in what form, and how accurately?

A quick way to sort it out One subject, viewed from four angles
AEO The direct answer

How to make information easy to pick up in an answer.

GEO The generated answer

How content finds its way into an answer assembled by AI.

LLMO Understanding the brand

How large models represent a brand, its offers, and its evidence.

AI Visibility What we observe

Where the brand appears, in what form, and how accurately.

These are not airtight categories. The terms often overlap, and they do not describe four completely separate professions.

The term GEO did not come out of nowhere. A research paper submitted in 2023 and later accepted at KDD 2024 proposed a framework and metrics for studying visibility in generative engines. It put words around a problem brands are beginning to experience very concretely: an AI system can use several sources, combine them, and rewrite their information without giving any one source the identifiable place it would have had on a search results page.

The paper proposed a research framework. It did not claim to provide one method that would work everywhere, across every conversational AI, language and company. Since then, the market has enthusiastically adopted the term. That may be putting it mildly. GEO now gets used to cover quite a lot of things, but this is fairly typical when a new practice appears: the vocabulary tends to settle after the use cases, rarely before them 🤷‍♀️.

Now we can get back to the part that really matters: what is the brand trying to understand, and what will it actually be able to do with the answer?

When we say “visible,” what exactly do we mean?

Most misunderstandings begin with the word “visible.” One company may say it wants to be visible in ChatGPT when what it really wants is a citation and a link to its website. Another just wants its name to appear on a list of possible solutions. A third is mainly worried about how its business is being described.

All three expectations are legitimate. They just do not call for the same work or the same metrics.

We also need to look at where the observation is taking place. An answer from ChatGPT is not an answer from Claude, Gemini, or Perplexity. A generated answer inside Google Search follows yet another logic. And within the same tool, the result may depend on Web access, language, location, the context of the conversation, or simply how the question was phrased…

Access conditions vary by platform as well. For AI Overviews and AI Mode, Google’s generative features inside Search, Google says the fundamentals of SEO still apply and that no special llms.txt file or AI-specific markup is required to appear. OpenAI, on the other hand, says a site must allow OAI-SearchBot to be eligible for ChatGPT search results. Being accessible, however, does not guarantee that the site will appear in an answer.

Same question, four outcomes The brand does not receive the same kind of visibility
The tip is reused The content is used, but the brand remains invisible.
The article is cited The source appears, but the brand is not named in the answer.
The brand is mentioned It appears, but an important detail is wrong.
The moisturizer is recommended The product is selected, and the answer explains why it fits the request.

In all four cases, the brand is somewhere along the path to the answer. What that visibility actually does for the brand, however, is not remotely the same.

Amanda Natividad works at SparkToro on zero-click marketing, or what happens when people get information without necessarily visiting a brand’s website. In a recent article, she suggests treating an AI Visibility report as a mirror of the brand’s public record.

We liked that image because it shifts the focus. Instead of stopping at the number of appearances, we start reading the story being told. Is the category correct? Are the products actually available in the market being discussed? Are the meaningful differences understood, or flattened into a generic description?

But the mirror is imperfect. Sometimes it looks more like one of those funhouse mirrors. A strange answer on a Tuesday morning does not suddenly reveal what “the market” thinks about a company. It only shows what one system produced under those particular conditions. That can still be interesting, sometimes very instructive, but we have to resist turning it into a universal truth.

What can a brand actually work on?

There’s another uncomfortable truth here: a brand can do a great deal right and still not get the answer it hoped for. It doesn’t choose which sources the AI will use, the order in which brands will appear or the final wording. Any promise of total control sounds, let’s say, rather utopian.

The brand still has plenty to work on. It can make its digital presence much easier to understand with accurate product pages, current information, structured data where it serves a real purpose, accessible evidence, original content, reviews and consistent descriptions across partners and retailers.

Much of this work already has familiar names: SEO, content, public relations, reputation and data quality. GEO does not replace any of them. It makes us pay attention to what happens after information leaves its original page, gets mixed with other information and is rewritten by a third-party system.

And that’s often where we find the real problems. An important detail isn’t stated clearly anywhere. An old description is still circulating on a retailer’s website. The brand itself uses several terms for the same offer. Or the evidence is real but almost impossible to connect to the product it is supposed to support.

A conversational AI visibility strategy often looks less like a big technical launch and more like a lot of painstaking, detail-oriented work. Someone has to find old information, correct contradictions, align the language used to describe the offer, document the evidence, and check what partners are publishing. Each task sounds fairly ordinary. Together, they require time, method, rigor, and real coordination across teams. And because products, markets, and sources keep changing, the work is never completely finished.

And how on earth do you measure AI Visibility?

One score is tempting, and that is exactly the problem 😅. We understand why companies want a number: it makes it easier to track change, compare periods, and present a result quickly. A dashboard showing 72 out of 100 is easier to put in a slide than a collection of contradictory answers.

But what exactly is inside that 72? Citations, mentions, recommendations, order of appearance, accuracy of the description? Across which conversational AI tools, in which countries, with how many phrasings and how many repeated runs? Okay, hold on. If we do not know what the score counts or the conditions under which it was calculated, 72 out of 100 mostly creates an impression of precision without telling us what was actually measured.

A small-scale experiment published by SparkToro and Gumshoe.ai suggests that lists and rankings may vary considerably from one run to another. The authors are careful about the limits of their findings. We read the experiment as a reason to repeat observations, not as the discovery of a universal rule.

We keep a record of the questions, their variations, the tool used, the date, language, territory and whether the Web was available. We also decide what we’re going to count. A citation is not a mention, a mention is not a recommendation and appearing frequently does not prove that a brand has earned a stable position… all without turning the marketing team into a research lab, of course.

How does J2A approach it?

After trying several ways to organize the subject, and after tests, tests and more tests, we settled on four dimensions. They’re not revolutionary or particularly glamorous, which may be precisely why they work for us.

Surface

The conversational AI tools, features, languages, markets, and situations we want to observe.

Presence

What we mean by “visible”: a source used, a citation, a mention, a recommendation, or an accurate description.

Levers

The information, evidence, inconsistencies, and third-party sources the brand can realistically influence.

Evidence

The protocol that helps separate an isolated answer from a pattern worth acting on.

This framework brings us back to very concrete questions. What are we going to look at? Why does that surface matter to the brand? What will we do if we find an error? And what conclusion can we honestly defend?

At J2A, we will mostly use the phrase brand visibility in conversational AI rather than the market’s acronyms. It may be less convenient in a headline, but it is closer to the subject we want to address: understanding where a brand appears, how it is described, and what it can improve in the information available about it.

GEO, AEO and LLMO will remain part of our vocabulary. We just don’t want to turn them into three new silos while SEO, content, data, reputation and media teams are already trying to work together.

If you need a place to begin, start with a situation that genuinely matters to your brand. Choose where you will observe it, define what you will count as presence and, before the first test, write down what the result can prove and what it cannot. It may seem like a modest starting point, but it will probably give your team a more useful conversation than one more acronym.

What about your brand?

Choose one situation that matters, then frame your first observation using J2A’s four dimensions: surface, presence, levers, and evidence.

Talk to J2A
View the article’s sources

Established facts: primary sources

Observations: secondary sources