ResearchAI Share of Voice Statistics

AI Share of Voice Statistics 2026: 64 Teardowns Analyzed

64 free AI-visibility teardowns of B2B companies, 450 AI answers each: the median company was named in 14% of its answers, its most-named competitor in 57%.

Data last changed Sample: 64Markdown version

Key findings

  1. Across 64 free AI-visibility teardowns of B2B companies, the median company was named in 14% of its 450 AI answers (a median of 61.5 answers).

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  2. Across 64 free AI-visibility teardowns of B2B companies, the median top competitor (the most-named rival in each teardown) was named in 57% of the teardown's 450 AI answers (a median of 254.5 answers), 43 percentage points more than the median company itself (14%).

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  3. 22 of 64 B2B companies given a free AI-visibility teardown (34%) were named in fewer than 10% of their 450 AI answers (under 45).

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  4. B2B companies with a review-site profile are more likely than B2B companies without a review-site profile to be named in AI answers: median 21% vs 10% of answers (27 teardowns with a review-site profile vs 37 without one, of 64 free AI-visibility teardowns of B2B companies).

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  5. B2B companies with a site AI crawlers can read are more likely than B2B companies with a site AI crawlers cannot read to be named in AI answers: median 16% vs 7% of answers (41 teardowns with a readable site vs 23 with an unreadable site, of 64 free AI-visibility teardowns of B2B companies).

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  6. B2B companies reporting under $10M in revenue and B2B companies reporting $10M or more in revenue are about as likely to be named in AI answers: median 14% vs 12% of answers, a gap under 3 percentage points (40 teardowns of companies reporting under $10M vs 24 reporting $10M or more, of 64 free AI-visibility teardowns of B2B companies; revenue is self-reported by each company).

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  7. 59 of 64 B2B companies given a free AI-visibility teardown (92%) were cited as a source in at least one AI answer that did not name them.

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By revenue band (prospect-reported)

Revenue band (prospect-reported)TeardownsShare of teardownsMedian prospect shareMedian top-competitor shareProspects under 10%
Under $1M†1625%15%57%5 of 16 (31%)
$1M to $10M2438%14%58%7 of 24 (29%)
$10M to $50M†1930%12%52%7 of 19 (37%)
$50M or more†58%8%60%3 of 5 (60%)

Revenue band is entered by the prospect and not verified. Share of teardowns uses all 64 teardowns; the medians and the under-10% share use the teardowns in that row. † Fewer than 20 rows: shown for completeness, not used in any finding.

By month

MonthTeardownsShare of teardownsMedian prospect shareMedian top-competitor shareProspects under 10%
June 2026†813%16%54%2 of 8 (25%)
July 2026†1117%12%61%5 of 11 (45%)
August 2026†1320%14%57%3 of 13 (23%)
September 2026†1523%13%56%6 of 15 (40%)
October 2026†1727%12%56%6 of 17 (35%)

Share of teardowns uses all 64 teardowns; the medians and the under-10% share use the teardowns in that row. † Fewer than 20 rows: shown for completeness, not used in any finding.

By category

CategoryTeardownsShare of teardownsMedian prospect shareMedian top-competitor shareProspects under 10%
B2B SaaS2844%13%55%11 of 28 (39%)
Fintech2031%12%58%7 of 20 (35%)
Cybersecurity†1625%15%59%4 of 16 (25%)

Share of teardowns uses all 64 teardowns; the medians and the under-10% share use the teardowns in that row. † Fewer than 20 rows: shown for completeness, not used in any finding.

Share of answers naming the prospect

Prospect named inTeardownsShare of teardowns
Under 5%711%
5% to under 10%1523%
10% to under 20%2742%
20% to under 30%1523%
30% or more00%

Bands do not overlap: each includes its lower bound and excludes its upper bound. Share of teardowns uses all 64 teardowns.

FAQ

  • How often is a typical B2B company named in AI answers about its category?

    Across 64 free AI-visibility teardowns of B2B companies, the median company was named in 14% of its 450 AI answers (a median of 61.5 answers). Source: Free 150-prompt teardowns, Jun-Oct 2026.

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  • Are B2B companies with a review-site profile named more often by AI?

    B2B companies with a review-site profile are more likely than B2B companies without a review-site profile to be named in AI answers: median 21% vs 10% of answers (27 teardowns with a review-site profile vs 37 without one, of 64 free AI-visibility teardowns of B2B companies). Source: Free 150-prompt teardowns, Jun-Oct 2026.

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  • Are B2B companies with a crawler-readable website named more often by AI?

    B2B companies with a site AI crawlers can read are more likely than B2B companies with a site AI crawlers cannot read to be named in AI answers: median 16% vs 7% of answers (41 teardowns with a readable site vs 23 with an unreadable site, of 64 free AI-visibility teardowns of B2B companies). Source: Free 150-prompt teardowns, Jun-Oct 2026.

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Methodology

  • Source: Free 150-prompt teardowns, Jun-Oct 2026 (64 teardowns).
  • Coverage: 64 free teardowns run from June 1, 2026 to October 31, 2026. Each ran 150 buying prompts on ChatGPT, Gemini and Perplexity (450 answers per teardown).
  • Definitions: prospect share is the share of a teardown's answers that name the prospect; top-competitor share is the same for the most-named competitor in that teardown. Medians are taken across teardowns.
  • Excluded: prospect identities are private and excluded; only counts are published. No rows were dropped.
  • Self-reported fields: revenue band is prospect-reported (entered by the prospect when requesting the teardown) and is not verified. Every other field is counted from the answers.
  • Denominators: shares inside a teardown use its answers; shares of prospects use all teardowns; group comparisons use the teardowns in each group.
  • A finding is published only when every share in it has a denominator of at least 20 rows, and gaps under 3 percentage points are reported as about the same.
  • Refresh: updated as new teardowns are completed. The data on this page last changed on November 3, 2026.
  • Percentages are rounded to the nearest whole percent (halves round up) from exact counts, so a column of shares can add to 99% or 101%. Means are rounded to one decimal place. Medians of an even number of values are the mean of the two middle values. Gaps in percentage points are the difference between the two published whole-percent shares.

Dataset

  • Name: Free 150-prompt teardowns, Jun-Oct 2026
  • Description: Counts from 64 free teardowns: 150 buying prompts per prospect, each run on three AI engines (450 answers per teardown). Source: Free 150-prompt teardowns, Jun-Oct 2026 (64 teardowns). Prospect identities are private and excluded; only counts are published.
  • Variables measured: Share of answers naming the prospect; Share of answers naming the top competitor; Answers citing the prospect without naming it; Prospect has a review-site profile; Prospect site readable by AI crawlers; Revenue band (prospect-reported)
  • Measurement technique: Each teardown ran 150 buying prompts on ChatGPT, Gemini and Perplexity and counted the answers that name the prospect and its most-named competitor.
  • Markdown version: https://www.theaeocompany.co/research/teardowns.md

Suggested citation

The AEO Company. "AI Share of Voice Statistics 2026: 64 Teardowns Analyzed." November 2026. https://www.theaeocompany.co/research/teardowns

URL: https://www.theaeocompany.co/research/teardownsMonth: November 2026