The AI Visibility Metrics Your Board Will Ask About

A board benchmark needs a defined cohort. Full-audit Combined High inclusion in the AI Visibility Index 2026 ranged from 36.5 percent in professional services to 70.1 percent in BFSI. Combined is internal synthesis; the sector cohorts and prompts differ. These figures cannot substitute for the brand’s current inclusion or recommendation scores.

Highlights

• A board metric needs a defined prompt cohort, denominator, and reporting period.
• Brand inclusion, recommendations, citations, and factual quality answer different questions.
• The Index's Combined synthesis is separate from a buyer-facing model result.

Report the three named AI visibility scores with their denominators, then show ORHL conditions and relevant sentiment examples. Each answers a different leadership question: whether the brand is named, recommended when named, supported by a citation, or described with a material error. Keep platform, country, period, and sample scope visible.

Inclusion: are you in the answer at all?

Brand Inclusion Score equals valid tracked responses naming the brand divided by all valid tracked responses, multiplied by 100. Show the count behind the rate, invalid-response exclusions, and the platform and prompt cohort. A larger sample cannot compensate for prompts that do not match the business question.

Recommendation: are you the one being chosen?

Brand Recommendation Score equals responses recommending the brand divided by responses naming the brand, multiplied by 100. A neutral mention raises inclusion but not recommendation. Recommendation Rate uses a different denominator, eligible recommendation-intent responses, and must be labelled separately. Neither score measures buyer action.

Citation: is your evidence being used?

Brand Citation Score equals valid tracked responses with a brand-owned or brand-supporting citation divided by valid tracked responses, multiplied by 100. Preserve the supporting URLs and source types. A citation may help verification without directing the reader to the brand, so report the Zero Leads reader-path flag separately.

ORHL reduction: are the failures shrinking?

Report Omitted and Replaced states separately from Hallucinated and Zero Leads flags. Show eligible counts and rates against the same prompt cohort. A lower count after reducing the sample is not improvement. A cited page is evidence for review, not a proven cause of the condition.

Sentiment: how are you described?

Sentiment describes the tone or characterization in the captured answer. Preserve examples and distinguish factual error from supported criticism. Report it beside the three named scores when material; sentiment alone does not establish a lost sale or explain a recommendation.

The AI visibility metrics to keep away from the board

Avoid blended scores without formulas, changing prompt universes, and isolated screenshots presented as trends. The Index’s 63 complete-four-model audits had a median 33.3-point span between highest and lowest observed High-inclusion rates, but the prompt panels were unmatched. Keep platform differences visible and use comparable engagement data for decisions.

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