B2B AI Citation Rate by Industry.
B2B AI citation rate by industry varies more than most marketing teams expect, and the variation is large enough to change where you prioritise investment in AI visibility. That is also why this report does not quote a single average B2B citation rate: an average taken across industries describes a company that does not exist. What it does instead is show the two things SourceRank AI has measured and published by industry: how concentrated the engines' recommendations are, and which sources they read to make them.
How Did We Measure B2B AI Citation Rate by Industry?
Two published SourceRank AI studies sit behind every number on this page, and both use the same five answer engines: ChatGPT, Gemini, Microsoft Copilot, Google AI Mode and Google AI Overviews.
- Who AI recommends (full study): 264 buyer questions, 12 in each of 22 B2B categories, phrased the way a buyer asks for a provider, measured on 10 and 11 October 2026 in US English. 1,249 answers were counted, and every firm each answer named was recorded.
- What AI engines cite by industry (full study): the latest measurement run for ten companies in ten different industries, 924 tracked questions, 4,570 answers and 18,201 cited links, taken between 17 and 22 September 2026. Every cited source was sorted into one of seven kinds.
Citation rate, in the sense this page uses it, is the share of answers that name a company. A firm counts once per answer however often the answer repeats it.
How Concentrated Are AI Recommendations in Each Industry?
AI engines almost always name firms when a buyer asks for one: 1,232 of 1,249 answers, 98.6%, named at least one provider. In every one of the 22 categories the most named firm was named by all five engines. What changes from industry to industry is how far ahead that leader sits and how many other firms share what is left.
| Category | Most named firm | Its share of answers | Top 3 share of all mentions | Distinct firms named |
|---|---|---|---|---|
| Small business insurance providers | The Hartford | 53 of 57, 93.0% | 46.1% | 33 |
| 3PL and ecommerce fulfillment | ShipBob | 52 of 58, 89.7% | 40.8% | 68 |
| Middle-market investment banks | Houlihan Lokey | 50 of 59, 84.7% | 22.2% | 148 |
| Managed detection and response (MDR) | Arctic Wolf | 46 of 56, 82.1% | 41.3% | 42 |
| Freight brokers | C.H. Robinson | 47 of 58, 81.0% | 29.4% | 98 |
| Commercial roofing contractors | Tecta America | 36 of 50, 72.0% | 32.8% | 115 |
| CNC machining and contract manufacturers | Xometry | 32 of 54, 59.3% | 26.0% | 175 |
| Managed IT service providers | Dataprise | 33 of 56, 58.9% | 21.1% | 152 |
| Medical billing and revenue cycle management | athenahealth | 33 of 58, 56.9% | 18.5% | 142 |
| Lower middle market private equity firms | The Riverside Company | 19 of 57, 33.3% | 11.3% | 200 |
| Industrial automation system integrators | JR Automation | 16 of 56, 28.6% | 9.8% | 210 |
| Commercial solar installers and EPC contractors | Solar Landscape | 12 of 55, 21.8% | 8.4% | 169 |
Twelve of the 22 categories are shown; the full study has all of them with the firm-by-firm tables. Measured on 10 and 11 October 2026.
Two patterns fall out of the table. First, the leader's share of answers runs from 93.0% to 21.8%, so the same citation rate means very different things in different categories: a firm named in a fifth of answers would be the leader in commercial solar and far behind in small business insurance. Second, concentration differs by a factor of about five. The three most named firms took 46.1% of all mentions for small business insurance and 8.4% for commercial solar. In the fragmented categories most named firms appear once: in industrial automation, 135 of 210 firms were named in a single answer.
What Do AI Engines Cite in Each Industry?
The second study looks at the sources behind the answers. Vendor and agency websites, each company's own site and its competitors', were the most cited kind of source in all ten industries, in a narrow band from 72.7% to 86.3% of answers. What the engine reaches for second is where industries split.
| Industry | Vendor or agency sites | Leading non-vendor source | Its share of answers |
|---|---|---|---|
| Software supply chain security | 401 of 500, 80.2% | Docs and standards | 312 of 500, 62.4% |
| Coworking and flexible office space | 524 of 709, 73.9% | Directories and marketplaces | 320 of 709, 45.1% |
| Financial market data and news | 178 of 245, 72.7% | News and media | 92 of 245, 37.6% |
| Healthcare benefits, employer buyers | 223 of 270, 82.6% | Government and regulator | 63 of 270, 23.3% |
| Answer engine optimization (SourceRank AI) | 367 of 500, 73.4% | Social and video | 67 of 500, 13.4% |
| Ecommerce development | 408 of 539, 75.7% | Social and video | 70 of 539, 13.0% |
| B2B outbound and lead generation (Danish Lead Co) | 441 of 590, 74.7% | Social and video | 74 of 590, 12.5% |
| Construction and industrial staffing | 392 of 454, 86.3% | Government and regulator | 49 of 454, 10.8% |
| Private equity and M&A deal sourcing (DealSource Systems) | 424 of 499, 85.0% | Social and video | 51 of 499, 10.2% |
| Healthcare benefits, clinic buyers | 218 of 264, 82.6% | Government and regulator | 26 of 264, 9.8% |
Measured 17 to 22 September 2026, one company per industry. Accounts other than our own are shown by industry only.
In a local, bookable category the engine reaches for a marketplace. In a technical category it reaches for documentation and specifications. In a market data category it reaches for the news. In regulated categories it reaches for a government or regulator site. In the professional services categories it reaches for social and video, which in practice means LinkedIn, YouTube and Reddit. The same source kind can be worth vastly different amounts depending on the industry: documentation was cited in 62.4% of answers for the software supply chain security company and in 0.1% for the coworking operator, a gap of 442 times.
Which Factors Separate High-Visibility Firms from the Rest?
Four factors explain most of the variation, and their weight differs by industry.
Category structure. Where one or two household names dominate buyer awareness, as in small business insurance or PEO providers, the engines concentrate on them. Where there is a long tail of credible specialists, as in solar installers or industrial automation integrators, the answers fragment, which leaves far more room for a challenger to be named.
The second source the industry runs on. Your own site carries the citation load everywhere, but the surface beside it changes by industry, as the table above shows. A coworking operator ignoring marketplaces is missing the source cited in 45.1% of the answers about it; a software company buying directory listings is buying into 2.6%.
Content density and entity clarity. Industries that publish structured, specific content, documentation, comparison pages, Q&A resources, give retrieval models more to work with, and a company the engines can resolve as one unambiguous entity is easier to name. In the October study, the challengers that got named did it by publishing specific comparison and situation pages that the search-grounded engines read and cite.
Regulated categories are not the ceiling they are often said to be. Every answer about small business insurance providers and employee benefits brokers named at least one firm (57 of 57 and 58 of 58). The least willing category was sell-side M&A advisory, where 52 of 58 answers named an advisor. What changes in regulated categories is the second source: government and regulator sites, cited in up to 23.3% of answers for a healthcare benefits account.
How Do Citation Rates Vary Across AI Engines?
Engine behaviour differs as much as industry behaviour does, and a brand that does well on one engine can be missing from another.
| Engine | No source list | Vendor or agency site | Social and video | Community | Government and regulator |
|---|---|---|---|---|---|
| Microsoft Copilot | 0 of 924, 0.0% | 99.8% | 8.2% | 3.7% | 1.8% |
| Google AI Overviews | 30 of 875, 3.4% | 95.0% | 25.9% | 16.2% | 3.7% |
| Google AI Mode | 118 of 924, 12.8% | 79.8% | 11.7% | 7.6% | 2.9% |
| ChatGPT | 117 of 923, 12.7% | 76.4% | 0.5% | 0.0% | 11.9% |
| Gemini | 525 of 924, 56.8% | 41.2% | 1.5% | 2.5% | 1.1% |
All ten industries pooled, September 2026. Shares are of each engine's answers, and an answer can cite several kinds.
ChatGPT cited a community site in 0 of its 923 answers, while Google AI Overviews did in 142 of 875. ChatGPT reached for government and regulator sources more than any other engine. Gemini carried no source list on more than half its answers, which makes it the hardest engine to audit. That is why a brand's overall AI citation rate needs to be measured on each engine rather than proxy-measured from one. Perplexity is not in SourceRank AI's measured set, so it is not in this table.
How Do You Improve AI Citation Rate From a Low Baseline?
Brands outside their category's leading group can close the gap by working through four stages in sequence.
- 1. Establish a measurement baseline. You cannot improve what you have not measured. Put your buyers' questions to each engine before making any content changes, and record which firms each answer names. The free SourceRank AI score checks whether your site is structurally ready to be cited; a SourceRank AI audit runs the measured baseline across five engines.
- 2. Fix entity and schema foundations first. Schema markup errors and entity disambiguation gaps undercut everything built on top of them, so fix the foundations before investing in new content.
- 3. Build the content and the second source your industry runs on. For most B2B categories that means comparison pages, situation-specific pages and case studies on your own site, then the surface your industry's answers lean on next: documentation for technical categories, marketplaces for local and bookable ones, news for market data, regulator alignment for regulated ones.
- 4. Build third-party entity presence. Mentions on LinkedIn, in industry associations, analyst reports and press coverage carry the entity signal that engines leaning on what the model already knows rely on most. Contact the SourceRank AI team if you want a prioritised entity-building plan for your sector.
Frequently asked questions
What is AI citation rate and how is it calculated?
AI citation rate is the percentage of relevant buyer prompts in which an AI engine names or links to your brand. SourceRank AI calculates it by running a structured prompt battery across the five engines it measures, ChatGPT, Gemini, Microsoft Copilot, Google AI Mode and Google AI Overviews, and counting the answers that name the company against the total answers measured. A SourceRank AI audit measures your own.
Why does B2B AI citation rate vary so much by industry?
Because two things vary by industry. The first is category structure: in October 2026 the most named firm took 93.0% of answers in small business insurance and 21.8% in commercial solar. The second is the source mix: after vendor sites, engines lean on marketplaces in local categories, documentation in technical ones, news in market data and regulator sites in regulated ones.
How can a manufacturing or logistics company improve its AI citation rate?
Start by building the content infrastructure that retrieval models need: structured FAQ content, case studies, technical documentation, and schema-marked service pages. Then invest in entity signals, including LinkedIn presence, industry directory listings, press coverage, and any external mentions that establish your brand as a discrete, authoritative entity. The SourceRank AI services page covers the full improvement roadmap.
Does industry type permanently cap a brand's AI citation rate?
No. Industry sets how concentrated the shortlist is, not a hard cap. Even in regulated categories the engines name firms in almost every answer, and in fragmented categories, such as industrial automation where 135 of 210 named firms appeared only once, there is room for a challenger with specific, citable pages to be named consistently.
How often do AI citation rates change?
Citation rates shift continuously as engines update their indices, retrain, or change retrieval logic, and the sources cited move between runs even when nothing on the web has changed. Measure on a fixed cadence and judge the trend over several runs rather than one. Contact the team at SourceRank AI if you need frequent monitoring for a specific campaign.
Is a 5% citation rate good or bad for a B2B brand?
It depends on the category and on who leads it. In SourceRank AI's October 2026 study, the most named firm appeared in anywhere from 21.8% of answers (commercial solar) to 93.0% (small business insurance). A 5% rate trails the leader in every category we measured, but in a fragmented category it can still put you among the firms the engines name more than once. Context is everything, which is why SourceRank AI benchmarks against the competitors named on your own question set rather than reporting raw citation rates in isolation.
How does Copilot differ from other engines for B2B citation?
In SourceRank AI's September 2026 runs, Microsoft Copilot returned a source list on every one of its 924 answers and cited a vendor or agency site in 922 of them, 99.8%, the highest of the five engines. If you measure Copilot through a third-party service rather than the app, check which location each answer was written for: in our October study the service we used ran Copilot sessions from random countries, so 64 of 264 answers came back written for a buyer abroad and were excluded.
Where can I see how my brand compares to the sector benchmarks?
The two published studies give the category and industry picture: who AI recommends and what AI engines cite by industry. Your own position against named competitors comes from a SourceRank AI audit, which runs your buyers' questions across ChatGPT, Gemini, Microsoft Copilot, Google AI Mode and Google AI Overviews. The free SourceRank AI score checks whether your site is structurally ready to be cited.