What 860 AI-generated buyer answers across 61 B2B software categories reveal about who gets recommended, who gets ignored, and where AI answers actually get their evidence.
Broadcastwell Research · Published July 2026 · Data collected July 18 to 23, 2026
Between July 18 and 23, 2026, Broadcastwell ran 10 standardized buyer questions through AI search with live web results for each of 85 B2B software companies across 61 categories, producing 860 scored answers and 5,160 traceable source citations.
On engines: this study holds one engine constant (Claude with live web search) across all 860 runs so results stay directly comparable across categories. The current AI Visibility Diagnostic runs five engines (ChatGPT, Claude, Perplexity, Google AI Overviews and Google AI Mode) and compares them. The single-engine constraint applies to this study only. The AI Visibility Diagnostic method is published in full at github.com/Broadcastwell/audit-method. The current measurement protocol is on the methodology page .
One limitation matters up front: all 860 answers came from a single engine, Claude with live web search, run once per prompt. This is a snapshot of one system on the days it ran, not a claim about AI search in general. The fuller limitations are in the Methodology section below.
The results describe a market that has quietly re-concentrated. The median B2B company is named in just 20% of AI answers for its own category, and 35% of companies are never named at all. Meanwhile, in most categories a single brand captures 80% or more of all AI mentions. The average answer recommends only 2 vendors: the shortlist has shrunk from ten blue links to two names.
Median company rate: 2 of 10 answers across 85 companies; 30 of 85 companies were never named. The 80% leader figure is a median leader rate, equivalent to 8 of 10 answers across 85 company sweeps. Interval not reported in the published source.
The evidence behind those answers is not what most marketing teams assume. Among the 100 most-cited domains, 77.4% of citations resolve to vendor-authored content, companies writing the category's answer material themselves. Review platforms account for 10.2%, independent media 6.5%, and analyst firms 5.9%.
Observed citation shares among the top 100 domains: 1,454 vendor, 192 review, 123 media, 110 analyst and 0 community citations, out of 1,879 classified citations. Interval not reported in the published source.
The practical implication: in B2B software, AI visibility is not won on community platforms or earned media alone. It is won by publishing the reference content that AI systems retrieve, and by being present on the small set of review platforms they consistently trust.
02
Key Findings
FINDING 01
35% of B2B companies are invisible in their own category.
Across 85 companies, the median company was named in 20% of the 10 buyer answers for its own category. 30 of 85 companies (35%) were named in zero answers. Only 31% of companies were named in at least half.
The median is 2 of 10 answers across 85 companies; observed proportions are 30 of 85 named zero times and 26 of 85 named in at least half. Bar ranges are visibility-bin definitions. Interval not reported in the published source.
Named in 0% of answers
30 companies
Named in 1-20%
17 companies
Named in 21-49%
12 companies
Named in 50% or more
26 companies
FINDING 02
AI answers are winner-take-most.
In each category we identified the single most-mentioned brand across that category's answers. The median category leader appears in 80% of its category's answers. In 50 of 85 company sweeps (59%), the leader appears in 80% or more of answers. In only 3 of 85 does the leading brand appear in fewer than half.
Median leader rate: 8 of 10 answers across 85 company sweeps. The observed high-visibility leader proportion is 50 of 85. Bar ranges are visibility-bin definitions. Interval not reported in the published source.
Leader takes under 50%
3 companies
Leader takes 50-79%
32 companies
Leader takes 80% or more
50 companies
FINDING 03
77% of AI evidence is vendor-authored. Reddit: 0%.
We classified the 100 most-cited domains (1,879 citations, 36% of all citations). 77.4% of those citations resolve to vendor-owned domains: comparison posts, category guides and 'best X software' listicles written by the vendors themselves. Review platforms such as G2, Capterra, TrustRadius and SourceForge account for 10.2%; independent media 6.5%; analyst firms 5.9%, with gartner.com the single most-cited third-party domain in the study (110 citations). Community sources such as Reddit, Quora and Stack Overflow account for 0.0% of citations among the 100 most-cited domains (1,879 citations, 36% of the total).
Observed citation shares among the top 100 domains: 1,454 vendor, 192 review, 123 media, 110 analyst and 0 community citations, out of 1,879 classified citations. Interval not reported in the published source. The top-100 share uses 1,879 of 5,160 total citations.
Vendor-authored content
77.4%
Observed citation share: 1,454 of 1,879 classified citations. Interval not reported in the published source.
Review platforms
10.2%
Observed citation share: 192 of 1,879 classified citations. Interval not reported in the published source.
Independent media
6.5%
Observed citation share: 123 of 1,879 classified citations. Interval not reported in the published source.
Analyst firms
5.9%
Observed citation share: 110 of 1,879 classified citations. Interval not reported in the published source.
Community (Reddit, Quora, Stack Overflow)
0.0%
Observed citation share: 0 of 1,879 classified citations. Interval not reported in the published source.
FINDING 04
There is no single source of truth. The evidence layer is fragmented.
The 5,160 citations spread across 1,753 unique domains. The top 10 domains account for only 12% of citations, the top 100 for 36%, and 56% of all cited domains appear exactly once. AI answers are assembled from a long tail of category-specific pages, which means visibility is winnable page by page.
Observed citation shares: top 10 domains, 609 of 5,160 citations; top 100, 1,879 of 5,160. The singleton-domain rate uses 1,753 domains; its exact numerator is not reported in the published table. Interval not reported in the published source.
12%
share of citations held by the top 10 domains
Observed citation share: 609 of 5,160 citations. Interval not reported in the published source.
56%
of cited domains appear only once
Observed domain proportion; base: 1,753 cited domains. The exact singleton numerator is not reported in the published table. Interval not reported in the published source.
1,753
unique domains cited
FINDING 05
Question type decides who gets named, and 40% of use-case answers name nobody.
'Best X' questions named the measured company 41% of the time; 'X vs Y' comparisons 20%; use-case questions 23%. But in use-case questions, 40% of answers named no vendor at all: unclaimed answer space. Across all 860 answers, 13.1% named zero vendors, and the average answer named just 2.05 vendors (median 2).
Observed question-shape rate within the 860-answer study; exact per-shape counts are not reported in the published tables. Interval not reported in the published source. The all-answer zero-vendor rate uses 860 answers; its exact numerator is not reported in the published table.
Measured company named
'Best X' questions
41%
Observed question-shape rate within the 860-answer study; exact per-shape counts are not reported in the published tables. Interval not reported in the published source.
'X vs Y' comparisons
20%
Observed question-shape rate within the 860-answer study; exact per-shape counts are not reported in the published tables. Interval not reported in the published source.
Use-case questions
23%
Observed question-shape rate within the 860-answer study; exact per-shape counts are not reported in the published tables. Interval not reported in the published source.
Answers naming no vendor at all
'Best X' questions
5%
Observed question-shape rate within the 860-answer study; exact per-shape counts are not reported in the published tables. Interval not reported in the published source.
'X vs Y' comparisons
5%
Observed question-shape rate within the 860-answer study; exact per-shape counts are not reported in the published tables. Interval not reported in the published source.
Use-case questions
40%
Observed question-shape rate within the 860-answer study; exact per-shape counts are not reported in the published tables. Interval not reported in the published source.
FINDING 06
Being cited is not the same as being recommended.
Only 47% of answers that named a company also cited that company's own website as a source. And 97 answers cited a company's domain as evidence without ever naming the brand in the recommendation. Naming and citation are separate battles: one is about being retrievable, the other about being the answer.
Observed conditional naming/citation rate within the 860-answer study; the joint naming/citation count is not reported in the published tables. Interval not reported in the published source.
47%
of naming answers also cite the company's site
Observed conditional naming/citation rate within the 860-answer study; the joint naming/citation count is not reported in the published tables. Interval not reported in the published source.
97
answers cited a domain without naming the brand
Volume II extends this work by classifying every absence: The Absence Ladder.
03
Segment breakdown
Data table: Segment, Companies, Answers, Avg named, Avg cited, Named in zero answers
Segment
Companies
Answers
Avg named
Avg cited
Named in zero answers
Product & Dev Tools
21
210
47.1%
33.8%
2
Sales & Marketing Tech
21
220
16.7%
22.4%
10
Ecommerce & Site Search
12
120
18.3%
15.8%
5
HR & Operations
10
100
26.0%
24.0%
4
Customer Experience
9
90
28.9%
22.2%
4
Data & Analytics
9
90
16.7%
12.2%
5
Vertical & Niche SaaS
3
30
36.7%
40.0%
0
All segments
85
860
27.5%
24.0%
30
Avg named and Avg cited are means of per-company rates, not pooled answer proportions. Company and answer bases are shown in each row; all segments cover 85 companies and 860 answers. Interval not reported in the published source.
Average share of a company's ten category answers in which it was named or its domain cited. Vertical & Niche SaaS covers three companies, so treat that row as directional. Sales & Marketing Tech shows 220 answers because one company in that segment was swept twice.
04
The Citation Index
Most-named brands across all 860 answers
Data table: #, Brand, Mentions, Share
#
Brand
Mentions
Share
1
6sense
123
14.3%
2
Demandbase
101
11.7%
3
Algolia
63
7.3%
4
Gong
37
4.3%
5
Bloomreach
30
3.5%
6
Zendesk
29
3.4%
7
Terminus
27
3.1%
8
Constructor
27
3.1%
9
Clari
26
3.0%
10
Freshdesk
21
2.4%
Observed answer shares. Each row gives the numerator in Mentions; all shares use 860 scored answers. Interval not reported in the published source.
Most-cited third-party domains
Data table: Domain, Citations, Type
Domain
Citations
Type
gartner.com
110
Analyst
g2.com
57
Reviews
peoplemanagingpeople.com
31
Media
sourceforge.net
30
Reviews
trustradius.com
24
Reviews
thedigitalprojectmanager.com
22
Media
netguru.com
21
Media
thecmo.com
21
Media
capterra.com
20
Reviews
Full top-100 domain table available in the dataset downloads below.
05
Methodology
01Every rate is reported with its numerator and denominator
02Every rate carries a 95 percent interval
03The interval describes this run, not a future outcome
A point estimate on its own hides the spread. Every rate carries its numerator, denominator and interval.
Engine. All answers were generated with a frontier large language model (Claude) with live web search enabled, so every answer reflects real-time retrieval, not training data alone. This edition holds the engine constant across all 860 runs so results stay directly comparable across categories; a four-engine study of this size would require 3,440 runs and introduce cross-engine variance we could not yet control for. The AI Visibility Diagnostic now runs five engines (ChatGPT, Claude, Perplexity, Google AI Overviews and Google AI Mode) and compares them; the single-engine constraint applies to this study only. The Q4 2026 edition will extend the multi-engine comparison to the full research sample.
Prompts. For each company we generated 10 standardized buyer questions in its exact category: a mix of best-of, direct comparison, and use-case questions, phrased the way a real buyer types them.
Scoring. Each answer was scored on two binary outcomes: whether the measured company was named, and whether its domain appeared among the cited sources. Every cited URL was logged, producing 5,160 citations across 1,753 domains. One company was swept twice, returning 20 answers rather than 10, which is why 85 companies produce 860 scored answers. Volume II's per-company analysis uses a single score per company and therefore reports 850.
Sample. 85 B2B software companies across 61 categories, skewing toward challenger brands (the companies most likely to need this measurement). Category leaders enter the data through mentions, not through selection.
Classification. The 100 most-cited domains (36% of all citations) were classified by hand into vendor-authored, review platform, independent media, analyst, and community. Separately, all 5,160 cited URLs were string-matched against reddit.com, quora.com and stackoverflow.com; none appeared in any answer. The community figure therefore covers the full citation set, not only the classified sample.The classified-domain proportion is 1,879 of 5,160 citations. The full-corpus community check found 0 of 5,160 cited URLs. Interval not reported in the published source.
Limitations. Single engine, single run per prompt. AI answers vary run to run: repeated runs of the same question can return different leaders, so all figures are point-in-time estimates, not fixed rankings. The sample skews toward challengers, which lowers average named rates relative to a random sample of all vendors. Percentages are rounded.
06
Dataset Downloads
most_named_brands_top50.csv
Every brand named 2+ times across the 860 answers, with mention counts.
Company identities in the underlying sweep data are withheld. Aggregates and brand-level mentions of widely known market leaders are published; per-company results are anonymized.
07
Suggested Citation
Broadcastwell Research (2026). The 2026 State of Generative Engine Optimization, v1.0. https://doi.org/10.5281/zenodo.21537014
@report{broadcastwell2026geo,
title = {The 2026 State of Generative Engine Optimization},
author = {{Broadcastwell Research}},
year = {2026},
version = {1.0},
doi = {10.5281/zenodo.21537014},
url = {https://broadcastwell.com/state-of-geo}
}
Published open access under CC BY 4.0 with a DOI. This study has not been peer reviewed. The full dataset is public so anyone can recompute every number in it.
The cited Volume I research and datasets are published open access under CC BY 4.0 and have not been peer reviewed.
08
Frequently Asked Questions
What is Generative Engine Optimization (GEO)?
GEO is the practice of making a brand visible and citable in AI-generated answers (ChatGPT, Perplexity, Claude, Google AI Overviews) the way SEO made brands visible in search results. This report measures the current state of that visibility in B2B software.
How were the 860 answers generated?
10 standardized buyer questions per category, run through a frontier AI model with live web search between July 18 and 23, 2026, then scored for brand naming and domain citation.
Can I reuse these findings?
Yes, with attribution. Use the suggested citation on this page. The downloadable datasets are provided for research and editorial use.
Will this be updated?
Yes. The State of GEO is an annual flagship report with a multi-engine edition planned for Q4 2026.
Measure your category across five engines.
See which buyer questions leave you out, the sources visible in those answers, and the three changes worth testing first. The Category Audit covers ten questions; the Diagnostic extends to 35 questions and 525 scheduled observed answers. AI answers vary, so repeated observations and disagreements are reported.