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 60 B2B companies in six 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 Burkland Associates at 71/100. The lowest is 34/100, shared by ADP and Trello. The average across all 60 companies is 52.5/100, and roughly 1 in 5 — 13 of 60 — 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, CRM & sales tools, marketing automation & email, fractional CFO & finance firms, HR tech & recruiting software, and project management software. 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.

• Fractional CFO: Kruze Consulting, a startup-focused firm, scored 63 — ahead of Indinero at 55.
• B2B marketing agencies: Animalz, a well-known agency, scored 38 — the lowest score in the entire benchmark. Simple Tiger scored 63.
• HR tech: ADP, founded in 1949 with more than 10,000 employees, scored 34 — tied for the lowest of all 60 companies.
• Project management: Basecamp scored higher than Notion and Trello despite being older and smaller in profile.
• CRM: Apollo.io outscored Freshworks despite Freshworks having far greater scale and recognition.

There is an important exception: Microsoft tied for the top CRM score at 66. That does not contradict the finding — it shows scale is not inherently a disadvantage either. 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 all six categories, 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.

1. B2B SaaS marketing agencies — avg 56.7

Highest: Heinz Marketing (70). Lowest: Animalz (38). Not Ready: 1 of 10. Agencies produced the highest category average — notable, since their own products are marketing, content, search, and demand gen. They also produced the widest internal gap. Being a recognized marketing brand does not automatically translate into AI visibility; the expertise has to be presented in a format AI systems can retrieve, attribute, and connect to buyer questions.

2. CRM & sales tools — avg 55.5

Highest: Microsoft Dynamics 365 / Pipedrive (66). Lowest: Copper CRM (38). Not Ready: 1 of 10. Microsoft's 66 shows a very large enterprise can perform well; smaller competitors in the same board also score strongly. Enterprise scale is neither a guarantee nor a barrier. The real question is whether the digital ecosystem explains what the product does, who it serves, how it differs, and which use cases it supports — the pattern behind what actually influences AI B2B software recommendations.

3. Marketing automation & email — avg 53.7

Highest: Klaviyo (64). Lowest: MailerLite (42). Not Ready: 0 of 10. A relatively narrow spread. Strong product recognition does not eliminate structural gaps; in crowded categories, clearly communicating capabilities, use cases, integrations, and differentiated positioning matters most.

4. Fractional CFO & finance firms — avg 51.4

Highest: Burkland Associates (71) — the top score in the entire benchmark. Lowest: airCFO (41). Not Ready: 4 of 10, the highest share in any category. Buyers here ask contextual questions ("which fractional CFO works with SaaS companies?", "which firm specializes in venture-backed startups?"), so a generic "we provide CFO services" description is rarely enough to be represented accurately.

5. HR tech & recruiting software — avg 49.5

Highest: Lever (63). Lowest: ADP (34). Not Ready: 3 of 10. This category produced one of the largest gaps between brand recognition and measured AI visibility: ADP, one of the most established companies in the space, tied for the lowest score of all 60. If that seems surprising, see why isn't ChatGPT recommending my company.

6. Project management software — avg 48.4

Highest: Teamwork (59). Lowest: Trello (34). Not Ready: 3 of 10. The lowest category average, despite containing Notion, Trello, and Atlassian's Jira. Buyers increasingly ask for comparisons by use case — "best project management software for agencies", "best Jira alternative for small companies" — and the companies answering those questions specifically get represented more often.

Monroya classifies 13 of 60 companies — roughly 22% — 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 60 B2B companies across six 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 six 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.