The State of B2B AI Search Visibility: 2026 Benchmark

AI search is now part of the B2B buying journey. When a buyer asks ChatGPT, Claude, Gemini, or Perplexity "what are the best CRM platforms for a mid-market company?" or "who are the best fractional CFO firms for startups?", the answer is no longer determined solely by traditional search rankings. AI systems synthesize websites, documentation, third-party sources, reviews, community discussions, and structured data into a single answer — a process we break down in how AI models decide which companies to mention.

That creates a new competitive question: can AI systems accurately understand your company, place you in the right category, and confidently include you when buyers ask for recommendations? Monroya's 2026 benchmark measures exactly that across 158 B2B companies in 12 categories.

The result is clear: company size, age, funding, and brand recognition do not reliably predict AI search visibility. Structural signals do. The highest score is Monroya Ai at 84/100. The lowest is 22/100, held by Groq. The average across all 158 companies is 50.9/100, and 17 of 158 companies (11%) fall into Monroya's "Not Ready" tier. If you're wondering why strong Google rankings don't carry over, start with does ranking well on Google mean I'll show up in AI answers.

Categories included: B2B SaaS marketing agencies, AI Visibility Platforms, Marketing Automation and Email Tools, Customer Support & Help Desk Software, CRM / sales tools, Revenue Intelligence Platforms, Fractional CFO & outsourced finance firms, Wealth Management & Financial Advisory Software, HR Tech / Recruiting Software, Compliance & Security Automation Software, Project Management Software, Fastest-growing companies. New categories are added roughly every one to two weeks using the same methodology — the live boards sit under industry rankings.

The clearest pattern in the benchmark is the absence of a consistent relationship between company size, company age, and AI visibility. Large, established companies do not automatically score higher. Neither do smaller or newer ones. Companies of dramatically different scale sit next to each other at both ends of every category — the same dynamic we documented in how small companies beat bigger brands in AI search and in can small companies compete with big brands in AI search results.

• B2B SaaS marketing agencies: Heinz Marketing leads at 70, while Animalz sits at 38 — a spread of 32 points inside one category.
• AI Visibility Platforms: Monroya Ai leads at 84, while Peec Ai sits at 36 — a spread of 48 points inside one category.
• Marketing Automation and Email Tools: Klaviyo leads at 70, while MailerLite sits at 42 — a spread of 28 points inside one category.
• Customer Support & Help Desk Software: LiveAgent leads at 80, while Kustomer sits at 39 — a spread of 41 points inside one category.
• CRM / sales tools: Pipedrive leads at 66, while Copper CRM sits at 38 — a spread of 28 points inside one category.
• Revenue Intelligence Platforms: Revenue Grid leads at 59, while Backstory sits at 44 — a spread of 15 points inside one category.
• Fractional CFO & outsourced finance firms: Burkland Associates leads at 71, while airCFO sits at 41 — a spread of 30 points inside one category.
• Wealth Management & Financial Advisory Software: Practifi leads at 63, while Envestnet Tamarac sits at 39 — a spread of 24 points inside one category.
• HR Tech / Recruiting Software: Lever leads at 63, while ADP sits at 34 — a spread of 29 points inside one category.
• Compliance & Security Automation Software: Hyperproof leads at 63, while Vanta sits at 33 — a spread of 30 points inside one category.
• Project Management Software: Teamwork leads at 59, while Trello sits at 34 — a spread of 25 points inside one category.
• Fastest-growing companies: Synthesia leads at 66, while Groq sits at 22 — a spread of 44 points inside one category.

Well-known enterprise names appear near the bottom of some categories and near the top of others. Scale is neither a guarantee nor a barrier: company size and age are simply not reliable predictors in either direction.

AI search visibility is a company's ability to be accurately understood, represented, surfaced, and potentially recommended by AI-powered search and conversational systems when buyers ask category-relevant questions.

Traditional SEO asks "can this page rank?" AI search visibility asks "when a buyer asks an AI system for recommendations, does the system understand this company well enough to include it accurately?" Those are related but different problems — a company can rank well in Google and still be nearly invisible in AI answers. For the measurement side of this, see what AI share of voice is and how it's measured, and for why single-number scores mislead, read why AI visibility scores are broken.

Across every category, one structural weakness appeared more consistently than any other: missing trust and authority signals. No visible author, no credentials, limited sourcing, few citations, weak editorial attribution, minimal methodology, thin evidence for important claims, and generic educational content. These gaps showed up in emerging brands and household names alike. Our analysis of what content actually gets cited by AI search tools covers the same territory from the content side.

Higher scorers generally combined seven things:

1. Answer-first content. Key questions answered directly, not buried under marketing copy.
2. Substantive information. Enough original detail for an AI system to understand expertise, category, products, customers, and differentiators.
3. Clear authorship and expertise. Who wrote it, and why they're credible.
4. Citations and sourcing. Claims backed by identifiable sources, research, data, or methodology.
5. Accessible content. Retrievable without heavy client-side rendering or gated interactions.
6. Structured data. Schema that helps machines interpret organizations, products, articles, and relationships.
7. Consistent positioning. The same description of the company across its own site and third-party sources — which is why Reddit and G2 are winning the AI share of voice battle.

A company cannot be understood from content an AI retrieval system cannot access. The benchmark evaluates whether GPTBot, ClaudeBot, PerplexityBot, and Google-Extended can reach meaningful content — and whether that content is actually returned, not just permitted. A robots.txt file that technically allows a crawler does not guarantee the crawler receives what it needs. Retrieval behavior differs by provider, as we show in ChatGPT, Claude, Gemini, and Perplexity don't search the same way.

Category averages move as sites change and as we re-scan. The pattern that holds across every category is the one behind what actually influences AI B2B software recommendations: clear positioning, retrievable content, and visible evidence beat brand recognition.

Monroya classifies 17 of 158 companies — about 11%, meaning a score below 40 — as "Not Ready." That does not mean the product is bad or that the company will never appear in AI answers. It means the structural weaknesses measured here are significant enough that its digital presence may not give AI systems the signals needed for reliable representation.

AI visibility is not a measure of product quality, revenue, customer satisfaction, valuation, brand popularity, or headcount. It measures how effectively a digital presence supports AI-era discovery and evaluation — and how quickly that can move is covered in how long it takes to improve your AI visibility.

• Clear category positioning — AI can quickly determine what the company does and where it belongs.
• Specific use cases — what problems it solves, and for whom.
• Buyer-oriented content — answers to questions buyers actually ask, not promotional messaging.
• Evidence of expertise — authors, credentials, citations, research, methodology, original insight.
• Fresh information — important pages maintained and updated.
• Accessible information — core content retrievable without JavaScript-only rendering.
• Structured information — schema and consistent entity data.
• Differentiated positioning — an explicit reason to choose them over alternatives.

The first move is usually not "publish more content." The higher-value question is whether an AI system can clearly understand who you are, what you do, who you serve, why you're different, and why you're credible. A practical sequence:

1. Fix company positioning. Homepage and core product pages should explicitly answer: what is it, who is it for, what problem does it solve, why is it different.
2. Strengthen authorship. Name authors and demonstrate relevant expertise.
3. Add evidence and citations. Back important claims with sources, research, data, and methodology — see whether backlinks still matter for AI search.
4. Improve answer-first content. Put direct answers near the top of important pages.
5. Expand use-case coverage. Build pages that answer specific buyer questions.
6. Improve structured data. Organization, product, software, article, and FAQ schema.
7. Verify AI crawler accessibility. Confirm content is actually retrieved, not just allowed.
8. Build third-party authority. Credible mentions, reviews, and research references off your own domain.

The tactical version of this sequence is in the complete GEO guide for B2B marketing teams and in five ways companies are optimizing for ChatGPT and AI Overviews right now.

The benchmark combines multiple signals into a 0–100 score across four areas:

A higher score indicates stronger overall readiness for AI-driven discovery and evaluation. It is a benchmarking indicator, not a guarantee that a specific model will recommend a company for a specific query. Full scoring detail lives on the methodology page.

The 2026 benchmark is based on Monroya's own scans of 158 B2B companies across 12 categories. It does not use survey responses, self-reported visibility, vendor claims, estimated visibility, or extrapolated scores for companies that were not scanned. Every company is evaluated the same way:

1. Structured data. Presence and completeness of structured data, including relevant JSON-LD.
2. Content freshness. Signals indicating whether important content is current.
3. AI crawler accessibility. Access and returned content for GPTBot, ClaudeBot, PerplexityBot, and Google-Extended.
4. AI company representation. How ChatGPT, Claude, Gemini, and Perplexity describe the company for category-relevant buyer questions — correct categorization, accurate description, relevant use cases, meaningful differentiators, inclusion in consideration sets. Our prompt-design work is documented in we tested 500 AI prompts so you don't have to.
5. Human interpretation. Does the digital presence give AI enough information to understand and represent the company accurately?

This is not a universal ranking of the "best" B2B companies. A high score does not guarantee a recommendation, and a low score does not mean poor products or services. AI outputs vary by model, prompt, geography, search context, conversation history, available sources, retrieval behavior, and date of query. Treat the benchmark as a point-in-time snapshot of AI search readiness — scores will change as sites are updated and as models change how they retrieve, something we track in how often AI models update what they know about a company.

1. Brand size is not a reliable predictor. Large companies can score poorly; small companies can score highly.
2. Digital structure matters. Machine-readable expertise, positioning, and evidence correlate with higher scores.
3. Trust signals are the recurring weakness. Authorship, sourcing, and citations are missing across many companies.
4. Crawler access matters. A company cannot benefit from content AI systems can't retrieve.
5. Category averages hide big gaps. Two companies chasing the same buyers can be 30 points apart.
6. AI visibility is a competitive intelligence problem. The question isn't only whether AI mentions you — it's which competitors it recommends, why, and which sources support those answers.

The benchmark currently covers 12 categories, with more added roughly every one to two weeks using the same methodology. Companies are never added retroactively with estimated scores — a company receives a score only when it has been scanned.