What is Share of Model in AI Search and Why Does it Matter?

What is share of model in AI search?

Share of model (SoM) is a metric that measures a brand’s visibility and authority within LLM outputs at scale. It’s defined as the number of mentions of a brand by one or multiple LLMs in proportion to the total brand mentions within the same category. It tracks how frequently and favorably a brand, product, or service is mentioned, cited, or recommended by AI platforms relative to its key competitors.

In traditional search, visibility is determined by rankingsRankings
Rankings in SEO refers to a website’s position in the search engine results page.
Learn more on SERPs. Within AI-powered search experiences, share of model is determined by whether you’re included in an LLM’s generated answer relative to the opportunities in your category. But, share of model isn't a single universal number for your brand. It’s always relative to where you are looking.

For example, to measure your presence in the HR tech space, you wouldn't look at a single query. Instead, you would analyze the AI's responses across hundreds of golden prompts related to enterprise HR. If your brand is mentioned in 30% of those total categorical responses, that is your share of model.

This share may be different across different LLMs and categories. In this example, your brand might own a 60% share of model for the payroll category within ChatGPT, but only a 15% share for recruitment prompts within Gemini.

Remember, when an AI generates a response, it’s acting as a curator, evaluating the trustworthiness, relevance, and readability of different content sources and recommending and citing only the ones it sees as experts on a given topic. Share of model basically measures your brand’s ability to appear in the response.

Share of model vs. share of voice vs. AI market share

As AI marketing terminology expands, it’s important to distinguish between similar-sounding metrics. It’s also important to note that these terms are all still used somewhat interchangeably, and as the industry evolves, these definitions may change, and the lines may continue to blur a bit.

So are they interchangeable terms? It really depends on who you ask, but generally, while they overlap, share of model, share of voice, and AI market share measure different aspects of your digital footprint.

Share of voice

Share of voice (SoV) is a broad marketing metric that historically measured the percentage of advertising a brand owned in a specific market compared to competitors. In the digital age, this evolved to include organic search visibility, measuring how often and where your brand appears on SERPs for a set of keywords compared to the total available search volumeSearch Volume
Search volume refers to the number of search queries for a specific keyword in search engines such as Google.
Learn more.

• Scope: Includes paid ads, organic listings, social media mentions, and PR
• Focus: Volume of visibility across all channels
• Limitation: A brand can achieve high SoV through paid spend, but may have low trust or authority

While this term has its roots in SEO, some folks have started using share of voice to describe AI and answer engine visibility as well. While some use share of voice and share of model as synonyms, there are some key differences between the two. Specifically, share of voice generally refers to SEO visibility, while share of model refers to your influence and visibility in AI answer engines.

Share of model

Share of model is specific to generative AI and answer engines. It is a qualitative and quantitative measure of presence within AI-generated responses aggregated across a specific set of prompts or topics. Unlike a single ranking, it represents your competitive percentage of mentions within a specific category across one or multiple AI models.

• Scope: LLMs (ChatGPT, Gemini, Claude) and Search Generative Experiences (Google AIO, Bing Chat)
• Focus: Presence, sentiment, and citations within generated text
• Differentiation: High organic rankings don’t guarantee high share of model. Your brand needs to be seen as a trusted source by LLMs.

AI market share

AI market share typically refers to the dominance of the AI platforms themselves, for example, OpenAI’s market share vs. Google’s. But, in the context of brand visibility, it can also be used to describe a brand’s aggregate visibility across all AI platforms.

In our 2026 AEO / GEO Benchmarks Report, we defined AI market share as a brand’s aggregate visibility across all major AI platforms and citations. While share of model tracks your performance within a specific LLM or set of prompts, AI market share represents your total visibility.

It’s the aggregated sum of your mentions and citations, providing a high-level benchmark of who is truly winning the category. Essentially, it’s measuring: out of all the times AI engines, holistically, could have mentioned a brand in your industry, how often was it yours?

Why is share of model important?

The shift toward AEO isn’t a fad; it’s a fundamental change in user behavior. In 2024, Gartner predicted that organic search volume would drop 25% by 2026 as users embrace generative AIGenerative AI
Generative AI is a class of AI that creates content like text, images, and code rather than analyzing existing data, powering tools like AI search.
Learn more solutions for immediate answers.

As search volume becomes more fragmented across different models and interfaces, traditional search rankings matter less than being the trusted source for AI and LLMs. Share of model is your north star for becoming that trusted source for AI.

A north star for performance

No matter how complex a strategy or system is, you still need simple measurements and dashboards that prove the impact. Executives need a metric they can review monthly, follow trends over time, and use to work smarter, and teams need a shared north star that aligns product, content, and brand efforts around the same outcome. Share of model offers just that.

The rise of zero-click search

Users want frictionless experiences. If an AI can immediately summarize a product's pros and cons, there’s no incentive for the user to click through five different blogs. If your brand is not present in that summary, you lose the opportunity to influence the buyer journey early on. Share of model is the only metric that accurately tracks success in this zero-click environment.

Trust and third-party validation

When an AI cites a brand, it basically serves as third-party validation. Users often perceive AI responses as objective summaries of data. Being included in the model’s response builds inherent authority. However, being excluded from the response signals that your brand doesn’t have the relevance or authority of the competitionCompetition
Businesses generally know who their competitors are on the open market. But are they the same companies you need to fight to get the best placement for your website? Not necessarily!
Learn more.

Content optimization

AI models prioritize content that is accurate, comprehensive, and well-structured. Focusing on share of model forces brands to create higher-quality content that actually serves user needs and answers their questions, rather than content designed solely to game a search algorithm. Ultimately, share of model helps align marketing efforts with better customer experiences.

How do I measure share of model?

Unfortunately, measuring share of model isn’t as easy as tracking keywordKeyword
A keyword is what users write into a search engine when they want to find something specific.
Learn more rankings. Because AI responses are created in real-time, vary based on context, and are personalized to each user’s specific queries, there is no static position or ranking to track. Plus, that level of personalization means there are theoretically infinite possibilities of user prompts and AI outputs.

That said, here are some dos and don’ts of measuring share of model.

Don’t: Prompt AI chatbots

Note: this is not a realistic long-term or short-term solution for measuring your share of model. This is just a baseline, quick-win tactic some brands have been adopting for one-off evaluations to see how their brand appears in chatbots for one or two key prompts.

By asking a specific question like: “What are the top CRM tools for small businesses,” or “How does [Brand A] compare to [Brand B],” you can see if the AI mentions your brand. If your brand appears, that could be a sign that your strategy is working. If it doesn’t, it could point you towards some opportunities to optimize. But it’s important to note that these results can be easily skewed based on your previous chats and the context the AI has about you.

For instance, if you’re logged into ChatGPT with your company email and ask what the top CRM tools are, ChatGPT will have details about your brand from previous conversations, including size, audience, and company goals, which it will use to inform its output. However, if you did the same search in an incognito browser from a free account, you’d get a very different, and likely more generic answer because the LLM wouldn’t have any context on what you’re hoping to see.

While this does give you a quick pulse check into how you’re viewed in AI search, it’s not an honest or reliable view because the scope is too small and it’s likely skewed. You’re only getting data on how one user (you) is searching, which doesn’t necessarily reflect what other users are seeing. In addition, studies have shown that AIs can be inconsistent with their recommendations, so even if you appear in one conversation, there’s no guarantee you’ll stay there.

Manual prompting is also subject to personalization bias and hallucinationHallucination
An AI hallucination is a factually incorrect response generated by an LLM, occurring when AI confidently produces fabricated or unfounded information.
Learn more. An AI might give a different answer to you than it does to a prospect in a different region. Plus, manually logging these interactions across thousands of keywords and multiple LLMs is impossible for enterprise teams.

Do: Leverage an enterprise AEO platform

To accurately measure share of model at scale, you need technology built for the task. This is where enterprise AEO platforms come in.

With enterprise AEO platforms, you can measure your share of model across LLMs, providing a unified view of how your brand is being cited, mentioned, and recommended across both traditional search engines and AI answer engines.

This allows you to track:

• AI visibility: How often your brand appears in AI Overviews and chatbot responses.
• Citations: Which specific URLs the AI is using as sources, allowing you to identify your most authoritative content.
• Sentiment: Whether the AI describes your brand positively, neutrally, or negatively.

Competitive gap: Which competitors are winning AI market share and dominating key conversations in your vertical.

How do I improve my share of model?

The goal is to make your content the most trusted, extractable, and relevant source of information for the AI. Here are a few ways you can ensure that your content is viewed as that trusted source by AI.

Implement schema and structured data

If you can make it easier for LLMs to parse and understand your content, you should. LLMs can visit your content quickly and frequently. The longer it takes to understand what the content is about and its value, the less likely it is to take the time to visit your site, greatly reducing the chances that you’re recommended or cited in its responses.

Schema markup , always a helpful technical aspect of SEO, is now essential for AEO because it helps AI bots quickly scan and understand your content. By implementing a robust schema strategy, you explicitly tell the AI what your content means in a language they understand. You are not just providing text; you are providing an additional layer of structured dataStructured Data
Structured data is the term used to describe schema markup on websites. With the help of this code, search engines can understand the content of URLs more easily, resulting in enhanced results in the search engine results page known as rich results. Typical examples of this are ratings, events and much more. The Conductor glossary below contains everything you need to know about structured data.
Learn more that the AI can easily grab and insert into a response.

For example, if an AI is building a comparison table of pricing for HR platforms, it will prioritize pages where pricing is clearly defined in structured data over pages where pricing is hidden in a PDF or a dense paragraph. Plus, if your page is missing key information, the AI is likely to just make up the answer or pull in details from unverified sources like review sites or Reddit conversations to fill in the blanks.

Schema makes it clear what kind of page an LLM is visiting and what information is on it, reducing the chances for hallucinations and ensuring that you’re dictating your brand details, not outside sources.

Structure your content for success

While schema is a machine-readable coding structure, this approach deals more with the actual organization and hierarchy of your page’s content. AI models have an easier time parsing content that has a logical structure. Like humans, bots are better able to understand content that follows a clear structure and is presented in concise, digestible chunks.

To improve share of model, audit your content formatting:

• Use clear headings: Frame your section headers as questions or topic statements to make the value and takeaways of the section clear up top.
• Structure your content for success: Keep structure in mind using H1, H2, and H3 tags to show which content is most important, and which are subtopics of larger queries.
• Leverage bulleted lists: LLMs (and humans) love lists. They’re easy to parse and easy to reproduce in an answer, increasing the chances that pieces of your content get cited wholesale.
• Direct answers: Start sections with a concise, direct answer to the question before expanding on the details. In short, lead with the value. Don’t make readers wait for the information they need.
• Create content for query fan-out: Don’t just create content that answers a single question on a key topic. Anticipate user needs and speak to related topics that they might have questions about. If you’re writing an article on the top CRM software, don’t just rank the top players; speak to nuances like ideal company size, pricing, integration capabilities, and ease of use.

This structure helps the model extract the answer and weave it into its response. If your content is a wall of text, the model may skip it in favor of a competitor who presents the same information with detailed header tags, logically structured information, and big blocks of text broken up into clear, bulleted lists.

Create quality content

Quality in the era of AEO means building brand authority around key topics and covering those topics holistically and with a unique perspective. AI models are trained to prioritize authoritative, expert information.

Topic clusters

Instead of writing one-off blog posts, create a network of linked content that covers every aspect of a specific subject. This signals to the AI that your domain is an authority on the entire topic, not just a single keyword.

Plus, this approach makes it easier for search engines and LLM bots to find more of your content. If you have a piece of content that has internal linksInternal links
Hyperlinks that link to subpages within a domain are described as "internal links". With internal links the linking power of the homepage can be better distributed across directories. Also, search engines and users can find content more easily.
Learn more to other content, the bots will seamlessly crawl that piece of content as well, and so on, helping you ensure your authoritative content gets discovered across your linked pages.

For example, a financial institution shouldn't just write about mortgage rates. They should cover mortgage applications, refinancing, types of loans, and credit score impacts, all linked together. This reinforces the idea that your brand is an authority on this topic and increases the likelihood that the AI will reference your brand for any query related to mortgages.

Prompt generation and targeting

These days, marketers have to think like prompt engineers. Research the types of prompts your audience uses. Are they looking for comparisons? Best of lists? Step-by-step guides?

Use platforms like Conductor to identify the questions users are asking, then seamlessly generate and optimize content specifically designed to answer those prompts. By aligning your content strategy with user intent, you’ll increase the probability of your content being sourced in an AI response.

How do I safeguard share of model?

Improving share of model isn’t a one-and-done process. To sustain a strong share of model, you’ll need to consistently monitor your AI search performance and create and optimize content to outperform the competition.

AI models continually update their training data and retrieval sources. A drop in technical health or content freshness can result in your brand being ignored by LLMs going forward.

Invest in a monitoring platform

You can’t protect what you don’t watch. Technical issues like slow page load times, server errors, or broken schema can cause an LLM to drop your site from its retrieval set.

Conductor Monitoring provides 24/7 tracking for your technical environment, alerting you to critical issues before they impact your standing in search and AI models. Real-time alerts ensure that if a change or update breaks your schema or blocks a crawler, you know immediately.

Regularly audit and optimize content

Just because an article performed well in AI search for a while doesn’t mean it will a week or a month from now. Over time, topics change with users asking new questions, and insights become outdated. If you don’t update your content accordingly, LLMs will stop referencing your content and start referencing the competition.

Just like in SEO, you’ll need to regularly audit your content, optimize underperforming content, and prune anything outdated or lacking value. Regular audits should focus on:

• Freshness: Are the data, insights, or perspectives outdated? Do you have more reliable information you can pull from to optimize this piece?
• Relevance: Have user questions evolved? What does this topic look like today compared to when it was first written?
• Competitor movement: Has a competitor published a more comprehensive guide? Do they have an angle on this topic we haven’t covered?

Using Conductor, you can track these changes and more to receive intelligent recommendations on which pages need to be refreshed to maintain their position in the answer engine ecosystem.

Conductor mini case study: Sonos

Sonos’s data-driven approach to AI search visibility is a perfect example of how brands can optimize for the technical and conversational nuances of modern AEO to improve share of model.

Originally, the brand’s global SEO efforts were managed through manual processes and fragmented tools, offering limited visibility into how AI agents were interacting with their site. But by leveraging Conductor Monitoring’s Log File Analysis, Sonos discovered that AI bots from platforms like ChatGPT, Gemini, and Perplexity were heavily crawling specific, overlooked sections of their site that traditional search engines often bypassed.

Using AI Search Performance in Conductor Intelligence, Sonos optimized this content to ensure it wasn't just searchable, but citable. They used real-time sentiment analysis and citation tracking to refine their presence in the conversations, threads, and publishers that AI engines reference most.

The results of this shift were a measurable impact on their share of model and AEO performance:

• Category-leading visibility: Sonos emerged as one of the top five Consumer Discretionary brands for AI market share in the 2026 AEO / GEO Benchmarks Report.
• Dominant AI presence: A significant increase in brand citations and mention rates across ChatGPT, Gemini, and Perplexity.
• Technical clarity: Full visibility into AI crawler behavior, allowing the team to prioritize content updates that specifically fuel AI-driven discovery.

By aligning its technical health and content strategy with the logic of AI discovery, Sonos has established itself as a go-to authority for AI search in its industry and built a strong foundation for continued share of model success.

Share of model in summary

Share of model is shaping up to be the definitive metric for the next era of digital marketing. It moves beyond clicks and traffic to measure your brand’s true influence on each model in an AI-first world.

By understanding how LLMs function, prioritizing structured and authoritative content, and leveraging an enterprise AEO platform to measure and protect your visibility, you can ensure your brand remains the answer your customers are looking for.