What Is Share of Model in AI Search?

If you have ever typed a question into ChatGPT or Perplexity and noticed that some brands get mentioned constantly while others never show up, you have already witnessed Share of Model in action. It is one of those metrics that sounds technical at first but is actually pretty intuitive once you understand what it is measuring. And honestly, if you care about how your brand shows up in AI search, this is the number you should be watching.

Defining Share of Model

Share of Model (SoM) is a metric that measures how frequently a brand is mentioned or cited in AI-generated responses across platforms like ChatGPT, Gemini, Perplexity, and Google AI Overviews. It tells you, as a percentage, how often your brand appears when AI systems answer questions relevant to your industry or product category.

Think of it like this: if you run a project management software company and you test 100 relevant prompts across multiple AI tools, and your brand gets mentioned in 34 of those responses, your Share of Model for that prompt set is 34%. Simple. But getting that number up? That is where it gets interesting.

This is different from Share of Voice, which measures how visible a brand is across paid and organic channels. Share of Model is specifically about AI-generated outputs, and that distinction matters more every day.

Why This Metric Exists Now

The honest reason Share of Model exists as a concept is that traditional SEO metrics simply do not capture what is happening in AI-driven search. A brand can rank on page one of Google and still be completely absent from every ChatGPT response about its category. Those are two different visibility problems requiring two different solutions.

The scale of AI search makes this urgent. ChatGPT processes approximately 2.5 billion prompts daily and receives 5.7 billion monthly website visits. Perplexity AI answers 780 million queries per month, reflecting a 239% growth within one year. And consumer behavior is shifting fast: the proportion of Swiss consumers using AI tools for product research nearly doubled from 27.4% in 2024 to 52.9% in 2025. Markt-Kom

If half of your potential customers are researching products through AI tools, and your brand never comes up in those conversations, that is a real business problem. Share of Model gives you a way to measure and track that gap.

How Share of Model Is Actually Measured

Measuring SoM is more hands-on than pulling a rank report. The basic process works like this:

1. Define a set of relevant prompts that reflect how your target audience searches for products, services, or information in your category. These are the kinds of questions real people ask AI tools. Understanding what prompts are and how they work is actually a useful starting point here.
2. Run those prompts across multiple AI platforms - at minimum ChatGPT, Perplexity, and Google AI Overviews, since each has its own retrieval logic and training data.
3. Record every brand mention in the responses, including your own brand and competitors.
4. Calculate inclusion percentages by dividing the number of responses that mention your brand by the total number of prompts tested.
5. Repeat regularly, because AI models update their training data and retrieval sources continuously, so your SoM can shift without you changing anything.

You can do this manually at a small scale, but for anything serious you will want a structured tracking system. There is a more detailed breakdown of how to measure Share of Model if you want to go deeper on the methodology.

What Actually Drives Your Share of Model

This is where it gets a bit more nuanced. AI models do not just randomly pick brands to mention. They rely on what they have learned during training and, increasingly, on real-time retrieval through processes like Retrieval Augmented Generation (RAG). AI models like ChatGPT and Perplexity use RAG to pull in current information when generating responses, which means the sources they retrieve from matter a lot. Understanding where these AI tools fetch their sources from gives you a real edge.

The main factors that influence SoM include:

• Entity authority: How well-established and recognized your brand is as a real-world entity, both in training data and in structured data sources like knowledge graphs.
• Knowledge graph strength: Whether your brand has a clear, consistent presence in sources that AI models trust, such as Wikipedia, Wikidata, and authoritative industry publications.
• Content depth: Whether your website and published content actually answer the kinds of questions people ask AI tools, with enough detail and clarity to be useful as a source.
• Cross-platform consistency: Whether your brand information is accurate and consistent across the web. Conflicting information confuses AI models just as much as it confuses people.
• Citations and mentions from trusted sources: Being referenced by credible third parties signals to AI models that your brand is worth including. This is why citations and mentions from authoritative sources carry real weight.

"Tracking Share of Model helps you understand how AI models perceive your brand and gives you the data you need to adapt your marketing strategy accordingly." - Ben Wood, Performance Director at Hallam

Share of Model vs. Traditional SEO Metrics

I think the clearest way to explain the difference is this: traditional SEO tells you where you rank when someone searches. Share of Model tells you whether you exist at all in the AI conversation about your category. Both matter, but they measure fundamentally different things.

With SEO, you are optimizing for a ranked list. With SoM, you are optimizing to be included in a synthesized answer. The AI does not show a list of ten results and let the user choose. It picks what it considers the most relevant, credible information and weaves it into a response. If your brand is not in that response, you are invisible to that user at that moment, regardless of your Google ranking.

Is SEO becoming less relevant? That is a fair question, and the difference between GEO and SEO is worth understanding if you are trying to figure out where to focus your efforts. The short answer is that both still matter, but they require different strategies.

AEO.ltd describes SoM as assessing a brand's visibility within the outputs of generative AI models, which is a clean way to put it. It is not about where you rank. It is about whether you are part of the answer.

How to Improve Your Share of Model

The good news is that improving SoM is not some mysterious black box. It connects directly to content quality, technical structure, and brand authority work. As one analysis puts it, SoM turns AI optimization from guesswork into measurable practice.

Practical steps that actually move the needle:

• Build topical depth on your site. Cover your subject area thoroughly. AI models favor sources that demonstrate genuine expertise across a topic, not just a single well-optimized page.
• Earn mentions from credible third-party sources. Press coverage, industry publications, academic references, and community discussions all contribute to how AI models perceive your authority.
• Keep your information consistent and accurate everywhere. Your brand name, description, and key facts should be the same across your website, social profiles, directories, and any other public source.
• Use structured data. Schema markup helps AI systems understand what your content is about and who you are as an entity.
• Write content that directly answers questions. AI tools are built to answer questions. Content that clearly addresses specific questions in your category is more likely to be retrieved and cited.
• Run regular audits. Because AI models update continuously, your SoM can change even when you have not changed anything. Regular testing keeps you aware of shifts before they become problems. An AI visibility audit checklist is a practical place to start.

The Bigger Picture: Why SoM Matters for Brand Visibility

There is a broader shift happening here that goes beyond any single metric. AI visibility is becoming a core part of how brands are discovered, evaluated, and trusted. When someone asks an AI tool for a recommendation and your brand comes up consistently, that builds familiarity and credibility in a way that a search ranking never quite did.

Conversely, if your competitors are being mentioned in AI responses and you are not, you are losing ground in a channel that is growing fast. This is not a future problem. It is a current one, and Share of Model is the metric that makes it visible and trackable.

There are also legitimate debates in this space worth acknowledging. The shift toward AI-driven metrics raises real questions about how transparent AI models are in how they select and represent brands. Not every AI mention is accurate, and not every omission is fair. That is part of why monitoring matters - you need to know not just whether you are mentioned, but how you are being described. Understanding how ChatGPT decides who gets mentioned sheds some light on the mechanics behind those decisions.