Share of Model: The AI Era’s Answer to Market Share

Andrew McPherson

Author Introduction

Andrew McPherson is the Director of CiteCompass, a New Zealand-based AI visibility platform helping B2B organisations win citations across AI search engines. With deep expertise in Answer Engine Optimisation, Generative Engine Optimisation, and structured data strategy, Andrew guides mid-market companies through the transition from click-based discovery to influence-based visibility – ensuring their brands are the ones AI systems trust and recommend.

Outline

What Share of Model measures for B2B brands
Why SoM matters more than search rankings
How AI platforms select brands for citation
SoM varies across ChatGPT, Perplexity, and Google
Measuring SoM with structured query sets
Optimising content, schema, and freshness for SoM
Competitive benchmarking through SoM analysis
CiteCompass approach to SoM tracking and improvement

Key Takeaways

SoM measures your brand’s share of AI-generated recommendations (Influencers Time)
94% of B2B buyers now use LLMs during purchase research (Leadscale)
AI platforms cite only 3-4 brands per response on average (Leadscale)
60% of searches now end without a click (Bain & Company)
Brands on 4+ platforms are 2.8x more likely to appear (Organisator)
85% of AI brand mentions come from third-party pages (AirOps)
Only 30% of brands stay visible between consecutive answers (AirOps State of AI Search)
CiteCompass tracks SoM across platforms and buyer journey stages (CiteCompass)

Introduction: Your Brand’s Visibility Has Moved to AI Answers

The way B2B buyers discover and evaluate solutions has fundamentally changed. Research from 6sense and Forrester (2025) found that 94% of B2B buyers now use large language models during their purchase journey. Meanwhile, Bain & Company research confirms that approximately 60% of searches now end without a click – the user gets their answer directly from the search interface without visiting any website.

This shift means that when a procurement manager asks ChatGPT, Perplexity, or Google AI Overviews “What are the best enterprise security platforms for financial services?” the AI response shapes their consideration set before they ever visit your site. If your brand is not among the 3-4 companies cited in that answer, you are effectively invisible at the discovery stage. Share of Model (SoM) is the metric that quantifies this new reality.

This article explains what Share of Model is, why it matters more than traditional search rankings for B2B visibility, how to measure it, and what practical steps you can take to improve your brand’s presence in AI-generated recommendations.

What Is Share of Model?

Share of Model measures your brand’s percentage of mentions in AI-generated responses for queries relevant to your category, products, or expertise. When potential customers ask ChatGPT, Perplexity, Google AI Overviews, Claude, or Microsoft Copilot questions about solutions in your space, SoM quantifies how often AI systems include your brand in their answers compared to competitors.

The metric functions as the AI visibility equivalent of traditional market share or share of voice in paid search. If 100 queries about project management software generate AI responses, and your brand appears in 23 of those responses, your SoM for that query set is 23%. Unlike market share (which measures actual revenue) or share of voice (which measures advertising impression volume), SoM measures mindshare in AI recommendation systems. The concept was coined in 2024 by Jack Smyth, Chief Solutions Officer at Jellyfish, as the AI equivalent of traditional share of voice.

SoM differs fundamentally from organic search rankings because AI systems do not provide ranked lists. They synthesise answers, often mentioning multiple brands within a single response. Your SoM reflects both solo mentions (where you are the only brand recommended) and competitive mentions (where you appear alongside alternatives). The metric reveals whether AI systems perceive your brand as a category leader, a viable alternative, or irrelevant to specific query contexts.

Why Share of Model Matters for B2B Brands

Traditional B2B marketing tracked awareness through search impressions, website traffic, and sales pipeline metrics. AI systems disrupt this funnel by answering customer questions before users click through to vendor websites. Research from Leadscale shows that AI platforms cite only 3-4 brands per response on average, with the top 20 domains capturing 66% of all AI citations. This creates a winner-takes-all dynamic where a small number of brands dominate AI-generated answers and the rest are effectively invisible.

Impact Across the Buyer Journey

In the awareness stage, prospects use AI systems for preliminary research, asking broad questions like “What types of solutions exist for this problem?” If your SoM is low in these foundational queries, prospects never learn your brand exists. In the consideration stage, buyers ask comparative questions. Inclusion in these responses signals that AI systems view you as a credible alternative. In the decision stage, stakeholders ask specific questions about capabilities, pricing, and integrations. High SoM here means your content is authoritative enough for AI systems to cite confidently.

Category Definition and Thought Leadership

SoM also functions as a category definition metric. If you are pioneering a new product category or positioning yourself in an emerging space, your SoM across category-defining queries indicates whether AI systems recognise that category and associate your brand with it. A cybersecurity startup introducing “supply chain attack prevention” needs high SoM for queries about that specific threat type. Without category-level SoM, prospects searching for your specific solution will not find you through AI discovery.

When industry analysts or prospects ask AI systems “Who are the experts in this topic?” the brands mentioned hold thought leadership SoM. This variant measures whether AI models associate your company with expertise, not just products. For professional services firms and knowledge-intensive businesses, thought leadership SoM drives inbound lead generation and premium pricing power.

The Invisible Competitor Threat

Competitor benchmarking through SoM reveals relative AI visibility. You may dominate organic search for your brand name but hold negligible SoM for category queries. Conversely, a competitor with weaker SEO might achieve higher SoM through superior technical documentation, pricing transparency, or integration ecosystem content. Research from AirOps (2026) found that only 30% of brands stay visible from one AI answer to the next, and just 20% remain present across five consecutive runs – making consistent SoM a significant competitive differentiator.

How Share of Model Works Across AI Systems

SoM varies significantly across different AI platforms because each system retrieves and ranks sources differently. Understanding these architectural differences is essential for optimising your presence across the full landscape of AI discovery.

Platform-Specific Retrieval Patterns

Google AI Overviews prioritise high-authority web content indexed by Google Search, favouring sites with strong backlink profiles and topical authority. ChatGPT uses Bing search integration and browsing capabilities, pulling from recently crawled web pages. Research from Organisator found that ChatGPT’s web browsing correlates with 87% of Bing’s top organic results. Perplexity emphasises freshness and citation transparency, obtaining 46.7% of its citations from Reddit. Google AI Overviews relies on Reddit for 21% and YouTube for 18.8% of its citations.

These architectural differences create platform-specific SoM variations. A software company might dominate Google AI Overviews due to comprehensive documentation and strong SEO but achieve low SoM in Perplexity if it lacks recent thought leadership content or community presence. According to Yext’s 2025 AI Citations Study, 86% of citations in AI-generated responses come from sources brands can control – such as websites, listings, and help content – giving brands a powerful opportunity to shape what customers see.

Query Type and Context Effects

Query formulation also influences SoM. Broad category queries (“What are the best CRM platforms?”) tend to favour established brands with high domain authority and extensive review coverage. Niche technical queries favour brands with detailed technical documentation and active developer communities. Problem-focused queries favour brands producing thought leadership content addressing that specific problem, even if they are not the market leader.

Geographic and language contexts create additional SoM variation. AI systems trained primarily on English-language content may underrepresent brands with strong presence in non-English markets. Industry-specific queries activate different source sets, meaning your SoM can vary dramatically depending on whether the buyer asks about manufacturing solutions versus healthcare software.

Freshness and the Multi-Surface Advantage

Temporal factors also impact SoM. AI systems increasingly prioritise freshness signals, giving higher weight to recently published or updated content. AirOps research found that pages not updated quarterly are three times more likely to lose citations, and that over 70% of all pages cited by AI have been updated within the past 12 months. This creates opportunity for challengers: consistent publishing cadence and documentation updates can shift SoM even without changing actual market share.

The multi-source retrieval pattern underlying RAG (Retrieval-Augmented Generation) systems means SoM reflects composite authority across your AI Data Surfaces. High SoM requires strong performance across crawled web content, structured feeds and APIs, and navigable site experiences. Research consistently shows that brands earning both citations and mentions are 40% more likely to resurface across multiple AI runs than citation-only brands.

How to Measure and Optimise Share of Model

Measuring SoM requires systematic query set design, response collection, and competitive analysis. The approach mirrors how G2’s Kevin Indig recommends tracking mention rate, top-three recommendation rate, and competitive presence to understand how often your company appears in AI-generated responses.

Design Your Target Query Universe

Start by defining the specific questions prospects ask when researching solutions in your category. Build a representative query set covering three tiers. Tier 1 includes high-intent category and comparison queries where you want maximum visibility. Tier 2 covers mid-funnel use case and problem queries where thought leadership matters. Tier 3 encompasses long-tail technical and implementation queries where documentation quality drives inclusion. A typical B2B company tracks 50-150 queries across these tiers, representing the actual questions prospects ask during buying cycles.

Collect and Benchmark Responses

Query each AI system (Google AI Overviews, ChatGPT, Perplexity, Claude, Copilot) with your target query set and record which brands appear in each response. Track mention type: citations with source attribution, mentions without attribution, and recommendations. Calculate SoM per query as your mentions divided by total queries, and competitive SoM as competitor mentions divided by total queries. Aggregate across query tiers to understand where you are strong and where competitors dominate.

As Search Engine Land’s measurement guide explains, the formula is straightforward: Your Share = (Your Citations / Total Citations) x 100. Track SoM longitudinally to measure optimisation impact. Monthly snapshots reveal whether content updates, schema implementation, and feed optimisation increase your mention frequency.

Optimise Citation Authority

Citation Authority is foundational to SoM. AI systems cite sources they trust, and trust correlates with entity confidence, content freshness, and cross-surface consistency. Improve Citation Authority by implementing comprehensive schema markup, maintaining synchronised information across all AI Data Surfaces, and earning third-party mentions through reviews, analyst coverage, and media citations. Research from AirOps found that sequential headings and rich schema correlate with 2.8 times higher citation rates.

Expand Topic Coverage

Comprehensive topic coverage expands SoM across query diversity. If you are mentioned for category queries but absent from use case queries, you lack content addressing specific customer problems. Develop pillar-and-cluster content architectures covering broad topics (pillars) and specific applications (clusters). Use FAQ-structured content to directly answer common questions, increasing the likelihood of inclusion in AI responses to those exact queries.

Build Entity Confidence

Entity confidence signals improve SoM by helping AI systems disambiguate your brand from competitors or similarly named entities. Implement Organisation schema with consistent naming, logo, and contact information. Build entity relationships through structured data linking your brand to products, services, locations, and key personnel. Research from Organisator confirms that brands mentioned on four or more platforms are 2.8 times more likely to appear in ChatGPT responses, and that Wikipedia content accounts for approximately 22% of the training data of large language models.

Prioritise Freshness Signals

Freshness signals boost SoM in time-sensitive queries. Update content regularly and include dateModified timestamps in both web pages and structured feeds. Publish changelog feeds documenting product updates, security patches, and new features. Maintain active blog publishing cadence addressing current industry topics and emerging trends. AI systems prioritise recent sources when answering questions requiring current information.

Strengthen Off-Site Presence

Research from AirOps shows that approximately 85% of brand mentions in AI search originate from third-party pages rather than the brand’s own domain. This means reviews, community discussions, partner content, and external validation play a major role in how AI systems describe you. Actively participate in relevant Reddit communities, industry forums, and review platforms. Earn media coverage and analyst mentions. Encourage customers to leave detailed reviews on platforms such as G2, Capterra, and TrustRadius. Visibility gaps often point to off-site credibility issues as much as on-site content problems.

Conduct Competitive Gap Analysis

Identify where competitors achieve higher SoM and why. If a competitor dominates pricing queries, audit their pricing transparency and feed implementation. If they win technical queries, evaluate their documentation depth and schema markup. If they appear frequently in thought leadership contexts, analyse their content strategy and publication partnerships. Use competitor SoM data to prioritise your optimisation roadmap, focusing first on the changes most likely to influence real buying decisions.

CiteCompass Perspective on Share of Model

CiteCompass tracks Share of Model as a primary metric for evaluating AI visibility optimisation effectiveness. The platform monitors brand mentions across multiple AI systems, providing granular SoM data by query type, AI platform, geographic region, and time period. This visibility enables B2B companies to understand their actual AI presence rather than relying on assumptions about what AI systems recommend.

Traditional analytics platforms measure website traffic, search rankings, and conversion rates. These metrics reveal who visited your site but not who never discovered you because AI systems excluded you from their responses. SoM quantifies this invisible loss: prospects who asked relevant questions but never saw your brand mentioned. As G2 research highlights, half of B2B software buyers now start their research on an AI search platform rather than Google – a statistic that jumped 71% in just four months between April and August 2025.

CiteCompass enables competitive SoM benchmarking, tracking both your mentions and competitor mentions across the same query sets. This reveals relative AI visibility – you might hold 15% SoM while your top competitor holds 60%, indicating significant disadvantage in AI-mediated discovery. Or you might dominate category queries but barely register in use case queries, revealing content gaps. Benchmarking transforms SoM from an abstract metric into actionable intelligence about where to focus optimisation efforts.

The platform also segments SoM by mention quality. A brief mention without citation provides less value than a detailed recommendation with source attribution. Tracking cited versus uncited mentions reveals whether AI systems view your content as authoritative enough to reference explicitly. Low citation rates despite decent mention frequency indicate trust signal deficiencies that CiteCompass Professional Services can diagnose and address through targeted schema improvements, entity disambiguation work, and content freshness strategies.

Understanding SoM also informs strategic positioning decisions. If your SoM is high in a specific niche but low in broader category queries, you may choose to lean into that niche rather than competing head-to-head with dominant incumbents. If your SoM is strong in technical queries but weak in executive-level queries, you may prioritise thought leadership content targeting senior decision-makers. SoM data transforms AI visibility from a technical optimisation challenge into a strategic marketing input.

What Changed Recently in Share of Model Tracking

January 2026: Perplexity introduced citation transparency features that distinguish between high-confidence citations and supplementary mentions, enabling more nuanced SoM measurement beyond binary mention tracking.

December 2025: Google AI Overviews expanded globally and began showing brand mentions in Knowledge Panel-style cards within AI responses, creating a new high-visibility mention format that significantly impacts effective SoM.

November 2025: ChatGPT enhanced browsing capabilities with deeper site navigation, enabling AI agents to access gated documentation and trial experiences, shifting SoM toward brands with accessible technical content.

October 2025: Microsoft announced Copilot integration with enterprise knowledge bases, creating separate SoM measurement requirements for public AI systems versus enterprise-internal AI assistants.

Sources

1. Bain & Company. (2024). Goodbye Clicks, Hello AI: Zero-Click Search Redefines Marketing. https://www.bain.com/insights/goodbye-clicks-hello-ai-zero-click-search-redefines-marketing/ – Research confirming 60% zero-click search rate and LLM adoption patterns across consumer segments.

2. Leadscale. (2026). AI Search and AI Agents in B2B Buying: Answers Before Clicks. https://leadscale.com/insights/demand-generation/strategy-foundation/ai-search-agents-b2b-buying/ – Analysis of 6sense/Forrester data on 94% B2B LLM adoption and AI citation dynamics.

3. AirOps. (2026). The 2026 State of AI Search: How Modern Brands Stay Visible. https://www.airops.com/report/the-2026-state-of-ai-search – Research on brand visibility fluctuation, freshness signals, and citation rate correlations with structured content.

4. AirOps. (2026). AI Visibility Metrics That Matter: What to Track and Why in 2026. https://www.airops.com/blog/ai-visibility-metrics – Analysis of citation share, mention rate, and the finding that 85% of brand mentions originate from third-party pages.

5. Organisator. (2026). Share of Model – Why AI Visibility Is the New Marketing KPI. https://www.organisator.ch/en/marketing/2026-02-27/share-of-model-warum-ki-sichtbarkeit-der-neue-marketing-kpi-ist/ – Comprehensive analysis of platform-specific citation patterns, Wikipedia’s role in training data, and multi-platform brand presence effects.

6. G2 / Webflow. (2026). The Visibility Shift: How to Measure Brand Presence in AI Answers. https://learn.g2.com/the-visibility-shift-how-to-measure-brand-presence-in-ai-answers – Research on B2B buyer shift to AI platforms and measurement frameworks for AI visibility.

7. Yext. (2026). What Brands Need to Know About AI Search Going Into 2026. https://www.yext.com/blog/2026/01/what-brands-need-to-know-about-ai-search-2026 – 2025 AI Citations Study finding that 86% of AI citations come from brand-controlled sources.

8. Search Engine Land. (2025). How to Measure Brand Visibility in AI Search. https://searchengineland.com/guide/how-to-measure-brand-visibility – Practical guide to calculating AI share of citations and building continuous monitoring workflows.

9. Influencers Time. (2026). AI Search Monitoring for Brand Visibility in LLMs. https://www.influencers-time.com/ai-search-monitoring-enhancing-brand-visibility-in-llms/ – 2026 guide to monitoring share of model visibility across AI surfaces and measurement best practices.

10. Forrester Research. (2025). Messaging for a Zero-Click World. Referenced via Informa TechTarget press release – Finding that B2B buyers are adopting AI-powered search at three times the rate of consumers.

11. CiteCompass. (2026). AI Visibility Suite. https://citecompass.com/ai-visibility-suite/ – Platform overview for monitoring and improving AI search visibility across the buyer journey.