Does your brand show up on ChatGPT?

It depends on how many times you ask

Digital has taught us to measure everything. It has been the innovation of the last 20 years, the big difference from traditional marketing. Today, perhaps, we need to rethink the concept. Because a brand’s visibility inside ChatGPT (or any other LLM) is not measured, it is estimated, the way you would with a survey. If you treat it as a ranking, you are looking at a single respondent and mistaking it for the market.

Try an example. Ask ten passers-by where to eat well in a city you don’t know. You won’t get 10 identical lists, probably not even two. The historic trattoria near the cathedral, though, will be named by 7 of them. That is what being visible means, and it has little to do with sitting at the top of a list. AI assistants, the systems like ChatGPT or Gemini that answer questions by writing new text every time, behave like those ten passers-by. The data now confirms it.

• LESS THAN 1 IN 100 The probability that two answers from ChatGPT, Claude or Gemini contain the same list of brands (SparkToro and Gumshoe.ai study, January 2026; not specific to Italy, but consistent with what I see in the Italian market).
• 63.4% Google searches in Italy that end without a click (SparkToro on Similarweb data, January–April 2026).
• 35.3% Italian internet users who say they use generative AI to choose a product, up from 19.6% the year before (IAB Italia, 2026).

Why does ChatGPT give a different answer every time you ask the same thing?

Because an AI assistant builds a new answer every time, and two new answers rarely match. An AI does not look up a ready-made ranking.

Rand Fishkin put this to the test quite directly. 600 volunteers asked ChatGPT, Claude and Google AI 12 questions, for almost three thousand runs in total. In less than 1% of cases did 2 answers contain the same list of brands. For the same order as well, the odds drop to about 0.1%. The lists even varied in length, from 2 or 3 names to more than 10.

The study is not specific to Italy, but the mechanism it describes (an answer generated from scratch at every request) does not depend on language. What varies from market to market is the group of brands that stays stable, and that has to be measured in your own market.

Another independent data point comes from SISTRIX, which tracked over 82,000 queries across 6 countries for 17 weeks. According to their analysis, from one week to the next the cited sources change by 56% in Google’s AI Mode (59% in Italy) and by 74% in ChatGPT search.

There is another important detail: in the same Fishkin experiment, 142 people asked to phrase the same need in their own words wrote almost entirely different questions. So the list changes at every run, and on top of that the question itself changes from customer to customer. So how can a monthly report say “third place on ChatGPT”? Third in which run? At nine in the morning or at three in the afternoon? With a question written by whom?

What stays stable when everything else changes?

Yes, something does stay stable, as I mentioned a moment ago: membership in the group of brands the assistant considers when it answers. In Fishkin’s study, a major cancer hospital appeared in 97% of ChatGPT’s answers, but ranked first in only 25 cases out of 71. Always present, almost never in the same position. SISTRIX confirms the pattern from another angle: for 86% of the queries analysed in AI Mode, there is a core of sites that returns week after week, even as the rest of the list shifts.

Let me add one more data point, from NP Digital, Neil Patel’s company. After analysing nine million answers about more than four hundred major brands (a non-Italian sample), it found that instability depends on the individual brand far more than on the AI platform: from 0.04 to 1.0 between one brand and another, from 0.08 to 0.32 between one platform and another.

If what stays stable is the brand, not the individual platform, the question a marketer should ask is “how much are we talked about, and where?”, not “how do I optimise this page for ChatGPT?”. This matters because it moves the budget conversation. I’ll get to that shortly.

Gianluca Diegoli talks about the “zero shelf” (it reminded me of Google’s Zero Moment of Truth, remember that one? :D): the moment, even before searching, when a person asks an AI assistant and gets a list of brands. If you are not on it, you do not exist for that customer. In his piece for Tendenze (in Italian) he reports the IAB Italia data quoted above, and adds a caveat I agree with: someone who says in a survey that they use AI to shop may have done it only once. Still, as far as I’m concerned, the direction is clear: for anyone selling consumer goods, the “zero shelf” is already open. Keep that in mind, and don’t wait too long to move.

How do you read an AI visibility report without being sold a story?

An AI visibility report is only as good as the sample behind it. Before looking at the dashboard, it pays to ask four questions.

1. How many times was each question repeated?
2. Who wrote the questions, and are they similar to what your customers would ask?
3. How often is the data collected?
4. Is the underlying method verifiable?

Ask these questions before choosing an AI tracking tool. For example, Fishkin points out in his study that with 60–100 repetitions per question, the presence rate becomes a reasonable measure. If the tool tracks once a week, you will have solid data in 2 years. A bit late, don’t you think?

There is another distinction that almost always gets lost in reports: questions that contain the brand name measure your reputation among people who already know you. The ones that matter for revenue are the others, where the customer describes a problem and does not yet know who you are. That is why the monitoring I use runs every day on unbranded questions. A single day tells you almost nothing; a quarter’s trend tells you a lot. The useful figure is a presence rate with its range of variation, and that figure only makes sense when read over time.

Whose budget is it?

Which brings us back to the budget conversation: AI assistants cite the brands that are widely talked about, so the money to build presence should come from the brand budget, not the SEO one. Hard to swallow, I know. But it is no longer (only) a technical matter, and there’s no way around it.

In Italy almost 2 Google searches out of 3 end without a click: 63.4% between January and April 2026. The traffic a marketing manager has to justify is falling for reasons their website does not control, and the question “are we on ChatGPT?” ends up where it always does… in the budget meeting.

Let’s go back to the NP Digital data and use it to understand where presence comes from. On the same sample of major brands, off-site sources multiply a brand’s visibility:

Assistants repeat what the web discusses in depth, and the web discusses a brand when someone has had a reason to: a video that explains a problem well, an article in a trade publication. This craft is called reputation, and it is funded the way reputation is funded, with an investment that pays off over years. We are talking strategy, not tactics: brand investments should be treated as costs amortised over several financial years, not judged on monthly return (to quote Diegoli).

If AI visibility is treated as an SEO line item, at the end of the period you will mostly have a report, and little more. Doing the things that actually build presence takes the brand budget, with its timelines. If no one in the company has decided who owns AI visibility, vendors are deciding for you, and they usually adapt to whatever they have to sell to whoever is listening. Things are changing, but you still hear “the digital team handles that” far too often. Start accepting that digital alone can no longer handle it.

What if the assistant talks about you and gets it wrong?

I’ll close with one more piece of evidence about which table AI presence belongs on. Showing up in answers matters little if the brand is described incorrectly. Being there and how you are portrayed are 2 separate problems, and the figures quoted in this article only measure presence. None of those studies checks whether the assistant correctly reports prices, range, locations or differences from competitors, and a customer who reads a wrong answer usually doesn’t write in to flag it: they take it in and make it their own.

You can do fact checking by hand: ask an AI assistant to describe your company to someone who doesn’t know you, and read the answer as if you were that person. Oh, and fact checking can be automated too (I use BIKMA).