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title: "AI visibility is the new market share | Zaraftis"
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  "og:description": "A new KPI for the era when buyers ask AI before they ask Google."
  "og:title": "AI visibility is the new market share | Zaraftis"
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  "twitter:title": "AI visibility is the new market share | Zaraftis"
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[← Blog](https://zaraftis.com/blog)

# AI visibility is the new market share

A new KPI for the era when buyers ask AI before they ask Google.

By [Gaurav Henry](https://www.linkedin.com/in/gaurav-henry), CEO, ZaraftisUpdated Oct 20, 202611 min read

**TL;DR.** AI visibility is the share of category questions where an AI engine names your brand. Attention metrics have a long record of moving ahead of market share, and AI visibility is the newest one. Track it on a fixed set of buyer questions every week, the way you track market share.

Market share has been the most useful number in marketing for sixty years for one reason: it correlates with everything that matters and is hard to fake. If your category has ten dollars of revenue and you have three of them, you have 30%, and that 30% keeps showing up downstream in your pricing power, your hiring, your moat, and your acquisition multiples. The number is a summary statistic for “how much of the market thinks of you as the answer.”

The trouble with market share in 2026 is that buyers now ask an AI engine that gives one confident answer to one question. When a buyer is deciding which two or three companies to look at, the share of those answers that name you is the share that decides whether you make the list. Call it AI visibility.

## What is AI visibility?

AI visibility is the percentage of questions in your category where an engine names your brand in its answer. It comes in two forms. The first is how often you show up at all: the share of answers that name you. The second, share of voice, is how often you show up compared to your rivals: your mentions divided by every brand’s mentions in the same answers. The first tells you whether you are present. The second tells you how you stack up against the brands named alongside you.

Written as a formula:

```
visibility(brand, prompt_set) =
   count(answers that mention brand) /
   count(answers)

share_of_voice(brand) =
   count(brand mentions) /
   count(all brand mentions in the same answers)
```

Being named in an answer is not the same as being cited as a source for it. A mention is the engine writing your name into the answer it shows the buyer: “the main options are Acme, Beta, and Gamma.” A citation is the engine pulling a page from your own website into the sources it read to write that answer. The two go together less often than you would think, because most of the time the engine names you based on what other sites say about you, not your own pages. Both are worth tracking, but they are different things. This post is about the mention, because the mention is what the buyer actually reads.

The thing that makes AI visibility worth more than a share-of-voice number you might already track in PR or social is the set of questions you measure against. Getting that set right is most of the work. A weak set gives you a number that feels good and means nothing. A good set gives you a number that tells you what your sales team will hear next quarter. (For why the question, not the keyword, is now the thing you measure, see [The end of keyword rankings](https://zaraftis.com/blog/the-end-of-keyword-rankings).)

## How do you choose the questions to measure?

The questions you measure against should be the ones your buyers actually type into AI engines, roughly weighted by how often they come up. Here are three places to find them, most useful first.

**One: ask your sales team.** In five minutes they will tell you the questions prospects open with. “We’re comparing you against \[X] and \[Y], can you tell us how you differ on \[Z]?” That same question, with the names taken out, is what buyers now ask AI engines too, often before they ever get on a call. Add the comparison questions that lead to demos, the “best \[X] for \[situation]” questions in your category, and the “alternative to \[competitor]” ones. You can write thirty to sixty of these in an afternoon for any clearly defined B2B category.

**Two: borrow from your search data.** The questions people have always typed into Google are still the questions they ask. The trick is to rewrite them out of keyword shorthand (“notion alternative product roadmap”) into a real question (“What are the best alternatives to Notion for managing a product roadmap?”). Phrasing matters more than people think. The same question, asked two ways, can get two different answers.

**Three: write down the questions you and your team ask.** Marketers are buyers too. Watch how you research a tool when you are about to buy one. Your buyers phrase things the way you do, and that’s more honest than any survey, which tells you what you wish people asked.

Start with the questions that matter most and grow the set. Zaraftis plans track 25 to 150 prompts, so most teams begin with a few dozen, run them weekly, and add questions as patterns show up.

## What does good AI visibility look like?

The research is young, but three public studies give a sense of scale.

A clear category leader gets named in most answers. In a 2026 audit of Gemini, GPT and DeepSeek across ten consumer categories and more than 2,000 questions, each model’s favorite brand appeared in its top five recommendations in 70% to 76% of answers ([Rienecker et al., 2026](https://arxiv.org/abs/2603.18300)). A leader still won’t hit 100%, because AI engines give varied answers and there are usually two or three competitors named alongside you.

B2B software is less concentrated. A 2026 study put 250 buyer questions, five times each, to GPT-5.2, Gemini 3 Flash and Perplexity across five industries. In SaaS, the most-named brand, HubSpot, appeared in about 40% of answers on average. Salesforce, the larger company by revenue, appeared in about 34% ([Żatuchin, 2026](https://arxiv.org/abs/2606.23057)). In a crowded B2B category, being named in four answers out of ten can make you the leader, and a strong challenger sits a few points behind.

The same brand can also look very different from engine to engine. In research published in Harvard Business Review, the detergent brand Ariel’s share of model ran from almost 24% on Llama to less than 1% on Gemini ([HBR](https://hbr.org/2025/06/forget-what-you-know-about-seo-heres-how-to-optimize-your-brand-for-llms)). In the 2026 study, the three engines agreed on the top brand for only about six questions in ten.

Read together, those numbers give you rough positions:

- **Category leader:** named in roughly 40% (crowded B2B) to 75% (consumer) of answers, and the first name on more questions than any rival.
- **Strong challenger:** named alongside the leader on most questions, a few points behind it.
- **In the market, not in the conversation:** named now and then, but not one of the names the engine reaches for first.
- **Invisible:** almost never named, at least on some engines. This happens even to brands with decent SEO and a real product. They show up when someone searches Google for them and vanish the moment a buyer asks an AI engine instead. The CEO has no idea, because no dashboard they look at measures the gap.

Hypothetical example

Brand X has 38% share of voice for SaaS analytics questions and shows up in 84% of them. Competitor Y has 4% share of voice and shows up in 19%. Both rank in the top three on Google for the same searches. Y’s CMO has no idea this gap exists. X’s CMO checks it every week.

## Why does AI visibility predict new business?

The reason a marketing leader should care is that AI visibility behaves like an attention metric, and attention metrics have a long record of moving ahead of demand.

This isn’t the first attention metric shown to lead demand. The idea predates AI by decades. Les Binet and Peter Field studied 171 campaigns from 1980 to 2010 and found that a brand whose share of voice runs ahead of its share of market tends to grow: about half a point of market share for every ten points of excess share of voice ([Nielsen](https://www.nielsen.com/insights/2025/what-is-share-voice/)). Les Binet’s later work on share of search, presented at the IPA’s EffWorks Global 2020, found the same thing ([IPA](https://ipa.co.uk/news/binet-presents-fast-cheap-predictive-share-of-search-metric)): how often people search for your brand by name, a pure measure of attention, runs ahead of market share. AI visibility is the next link in that chain. The place people pay attention changed from television to Google to the AI engine. The rule that attention comes before demand did not.

Here’s how it happens. A buyer asks an AI engine a question, the engine names two or three brands, and the buyer remembers one of those names and looks it up directly a few days or weeks later. That later visit shows up in your analytics as direct or branded search traffic. By the time the lead reaches your sales team, the AI answer that started it has left no trace. You see the new business. You do not see what caused it.

AI visibility is the missing trace. It is the early form of the branded interest that turns into new business later. If you are a CMO trying to explain why branded search is up while your paid spend is flat, the cause is often a rise in AI visibility from a couple of months earlier.

## The objection: “Mentions are not conversions”

Here is the pushback that comes up almost every time Zaraftis shows someone this number. “Sure, AI names us. So what? They didn’t click. They didn’t fill out a form. We can’t tie a sale back to a mention.”

Part of that is fair and worth taking seriously. AI visibility is genuinely hard to tie to revenue, for two reasons. People who hear about you in an AI answer rarely click straight through; they look you up later, and that visit gets filed as direct or branded search, so the trail is lost. And a launch, a funding round, or a wave of interest in your whole category can lift your AI mentions and your sales at the same time, which means the two moving together does not prove one caused the other. Anyone who promises you a clean line from an AI mention to a closed deal is overselling.

But being hard to trace doesn’t mean it isn’t happening. This is the same argument people made against measuring share of voice in advertising forty years ago, and it was wrong then for the reason it is wrong now: an effect your software cannot see is still an effect. AI visibility is a brand number, not a click number. You measure it because the brands that win it tend to win later, and you check it the way you check any early signal, by watching whether it moves ahead of branded search, direct traffic, and what your sales team says about where leads are coming from. When those line up, you have your answer.

One caveat, because the easy version of this story overstates it. Traffic that arrives from an AI answer is not always ready to buy. The largest public study so far looked at 973 online stores over 12 months. It found ChatGPT referrals converted below Google organic search and above only paid social, with stronger results in complex product categories ([Marketing Science](https://pubsonline.informs.org/doi/10.1287/mksc.2025.0489)). There’s no comparable public study for B2B software yet. AI visibility is a sign of brand strength, not a sales channel you can squeeze. Read it as the first and it holds up. Treat it as the second and it will let you down.

## How do you start without overcomplicating it?

You don’t need a big framework. The smallest version that works:

1. Pick the questions that matter most (Zaraftis plans track 25 to 150).
2. Run them every week on ChatGPT, Google AI Overviews, Claude, Gemini, Google AI Mode, Copilot and Grok.
3. Track three numbers: how often you show up, your share of voice against rivals, and whether the mentions are positive or negative.
4. Put the trend lines in your monthly marketing review.
5. When a number moves, look at which questions moved and why. To see which sites carry your competitors when you are missing, see [why ChatGPT names your competitor](https://zaraftis.com/blog/why-chatgpt-names-your-competitor).

Any tool that watches AI answers can give you the raw numbers, Zaraftis included. The hard part is sticking to it. You need a set of questions you trust, you need to run them on a real schedule, and you need to make the number something your team is actually held to. Until AI visibility is on someone’s goals for the quarter, it will not get the attention it needs.

## What this means for strategy

If AI visibility works like market share, the question for any brand becomes: what would it take to raise ours by 10 points over the next two quarters? That question has real answers, and they are the same things that get you cited as a source. You write stronger reference content on the sites engines trust. You clean up your structured data. You build a proper comparison page for every competitor that matters. You make sure your brand is described the same way everywhere on the open web. None of that is exotic, and all of it shows up in a number you can watch, which market share never did. (The specific steps are in the [AI-readability checklist](https://zaraftis.com/blog/ai-readability-checklist); why they work is in [How AI engines decide which brands to cite](https://zaraftis.com/blog/how-ai-engines-decide-which-brands-to-cite).)

This part surprises strategy teams. Market share, in most categories, is a number you get once a year, in a private report whose method you cannot fully check. AI visibility is a number you can get every week, measured a way you control, on a dashboard you own. It is the same idea finance leaders already trust, in a form you can actually act on.

If your CEO hasn’t asked about your AI visibility yet, they will soon, and it helps to have an answer ready.

## Frequently asked questions about AI visibility

**Q: What is the difference between AI visibility and share of voice?**

**A:** AI visibility, on its own, is how often AI answers in your category name your brand at all. Share of voice is the same data measured against your rivals: your mentions divided by every brand’s mentions in those answers. The first tells you whether you show up. The second tells you how you compare to the brands named next to you. Both are measured against a fixed set of questions, which is what makes the number mean something instead of being for show.

**Q: Is being mentioned the same as being cited?**

**A:** No. A mention is the engine naming your brand in its answer. A citation is the engine using a page from your own website as a source for that answer. They often differ, because most mentions come from what other sites say about you, not your own pages. Visibility tracks mentions. Citation share is a separate number that tracks whether your own pages are the ones the engine reads from.

**Q: How do I work out AI visibility?**

**A:** Visibility is the number of answers that name your brand divided by the total answers across your questions. Share of voice is your brand’s mentions divided by all brand mentions in the same answers. Run a fixed set of buyer questions on ChatGPT, Google AI Overviews, Gemini, Copilot and Google AI Mode every week and track both.

**Q: What is a good AI visibility number?**

**A:** It depends on your category. In a 2026 study of consumer categories, each engine’s favorite brand appeared in 70% to 76% of answers. In a 2026 study of B2B software, the most-named SaaS brand appeared in about 40%. The useful comparison is against the brands named next to you on the same questions, engine by engine.

**Q: Does AI visibility actually drive new business?**

**A:** It’s an early signal, not a proven cause. The link is indirect: a buyer asks an AI engine, remembers the brands named, and looks one up later, so the visit shows up as direct or branded search and the original AI answer leaves no trace. It is an early sign of brand strength, not a last-click sales number, and the link can be muddied by anything that lifts both mentions and demand at once.

**Q: How many questions do I need?**

**A:** Start with a few dozen of your most important buyer questions and grow from there. Zaraftis plans track 25 to 150 prompts. Pull them from your sales team, from your search data rewritten as real questions, and from the questions your own team asks when picking tools.

**Q: How often should I measure it?**

**A:** Weekly. AI answers shift as engines update what they read and how they rank it, and a drop can show up overnight. Put the trend lines in your monthly marketing review and tie them to someone’s goals for the quarter, or the number will not get the attention it needs.

### Get your AI visibility number, this quarter.

Zaraftis tracks AI visibility, share of voice, citation share, and sentiment on ChatGPT, Google AI Overviews, Claude, Gemini, Google AI Mode, Copilot and Grok. White-label reporting available for agencies who want to put the same number in front of their clients. Or [book a call](https://zaraftis.com/contact) and we’ll show you what the engines say about you.

[**Start free trial →**](https://app.zaraftis.com/auth/signup)

Read next

[Research · 8 min read<h4>Why ChatGPT names your competitor: data from 7,915 AI answers</h4>We looked at the sites AI cites in answers that name a competitor and leave you out. Other companies' pages came first.](https://zaraftis.com/blog/why-chatgpt-names-your-competitor) [Playbook · 10 min read<h4>The AI-readability checklist: 12 things to fix this week</h4>Twelve specific fixes that change how AI crawlers read your site. Most of them are boring, and every one counts.](https://zaraftis.com/blog/ai-readability-checklist)