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Three things every CMO needs to know
Buyers use ChatGPT, Gemini, Perplexity, Copilot, and Claude to identify vendors and shortlist before speaking to sales. Forrester reports that 94% of business buyers now use AI at some point during their buying process. Brands named in those responses have a structural advantage; brands absent face a gap that traditional SEO metrics cannot see.
In our own Q2 2026 study of 134 insurance brands, two of the five AI assistants we tested (Perplexity and Copilot) accounted for 100% of the 1,742 total organic citations recorded (1,348 of which resolved to a brand within the scored cohort, with the remaining 394 resolving to sources outside it). ChatGPT, Gemini, and Claude produced organic mentions but effectively zero organic citations in that category during the study window. A separate vendor case study on Fortinet, a company with 700,000+ customers, reported a starting AI citation share of 0.6% in cloud security (October 2025, before that vendor's own optimization work). Existing organic investment does not automatically transfer to AI citation.
Share-of-model and citation baselines are established within 5–10 days using an audit methodology identical to the one behind our own AIVI™ benchmark data below. Commercial ROI (AI-referred traffic, assisted conversions, influenced pipeline) becomes reportable as it accumulates, typically over a 60–180 day attribution window.
Our own evidence: the AIVI™ Q2 2026 Insurance Benchmark
Rather than lead with vendor case studies we can't fully verify, here is what we found when we ran our own documented methodology against a real category. We measured 134 U.S. insurance brands across five AI assistants (ChatGPT, Gemini, Claude, Perplexity, and Copilot) using 300 stratified prompts, producing 1,500 model responses and 4,328 organic brand mentions. Every score below is organic-only; paid placements were excluded and disclosed separately.
The finding that should reset expectations: all 1,742 organic citations in this study came from Perplexity or Copilot. ChatGPT, Gemini, and Claude mentioned brands but rarely-to-never attached a clickable, source-attributed citation in this category during the audit window. If your AI visibility strategy assumes citation behavior is uniform across assistants, this benchmark says otherwise, and it's why we score AIVI™ on a weighted five-component index (Share of Model 35%, Recommendation strength 25%, Position weighting 20%, Citation Influence 15%, Top-3 rate 5%) rather than a single citation-count metric.
Read the full AIVI™ Q2 2026 Insurance Benchmark → · See the AIVI™ scoring methodology →
What vendor-reported case studies add to the picture
These are third-party, vendor-published figures, not Brainpan's own primary-research data. We checked each against its original source, linked beside each figure, and corrected or removed several that didn't hold up (see the Industry Evidence section below for the full accounting). Figures whose original source we could not locate are not included. Treat them as directional evidence, not benchmarks to plan against.
All results in this section are drawn from published third-party case studies and are presented as directional evidence of what properly executed AI visibility programs have produced elsewhere, not as guarantees and not as a substitute for your own audited baseline.
Published case studies, checked against their sources
Siege Media reports 124K ChatGPT-referred sessions over a 6-month window and 3,400 conversions in a separate 1-month window for this account. The two figures cover different periods and don't combine into a single conversion rate, and the source doesn't say all 3,400 conversions were AI-attributed. We removed the "2.7%" figure that earlier appeared here.
Siege Media case study · Mentimeter Vendor-reportedLeadWalnut reports a starting AI citation share of 0.6% (ranked #31) in cloud security as of October 3, 2025, for a company with 700K+ customers. That baseline is evidence that a large customer base doesn't automatically translate into AI citation. It comes from LeadWalnut's own case study of its work for Fortinet, which gives conflicting timelines for the results that followed (5 and 7 days in one place, 2 to 3 weeks in another), so we don't cite any improvement figures.
LeadWalnut case study Vendor-reportedAEO Engine reports +559% organic sales growth and +306% organic traffic growth over 4 months for this client. The source lists the figures without describing the tactics used, and does not isolate how much is attributable to AI-referred traffic versus broader organic search gains, so we don't present it as an AI-citation-specific result.
AEO Engine client results Vendor-reportedThe AI visibility ROI framework
We measure AI visibility using the same five-component AIVI™ taxonomy behind the benchmark above, plus downstream performance metrics. Denominators matter: "share" numbers are only comparable when the query set and response count behind them are stated.
For a defined query set, your brand's share of all eligible organic brand mentions across the competitive set. Mention rate, the percentage of eligible responses that name your brand at least once, is a related but separate measure. The broadest visibility signal; not the same as being cited or recommended.
Of the responses that mention you, what share include a clickable, source-attributed citation, and how influential is that citation in the response's overall recommendation? Our own benchmark shows this varies enormously by assistant, so plan per-engine, not blended.
How often you're positioned as a recommended option (not just mentioned), and how often you land in the top 3 of a ranked or comparative response, each stated against the response count it's measured from.
Sessions, engaged sessions, and conversions from AI platform referrals (GA4 and Adobe Analytics each provide an AI-assistant referral classification under different names, so confirm how yours is configured; traffic with no referrer data stays unattributed). Report assisted and last-click attribution separately because they tell different stories, and AI-referred conversion performance has shown real year-over-year volatility: in Adobe's U.S. retail dataset, AI-referred visits converted to purchases 42% better than non-AI visits in March 2026, versus 38% worse in March 2025. That is a retail purchase measure, not evidence of B2B lead or pipeline lift.
ROI = (Incremental gross profit attributable to the program − Program cost) / Program cost
Pipeline expected value = Influenced pipeline × win probability × gross margin (attribution confidence stated)
The baseline (share of model, citation share, recommendation rate) is measurable from day one. Commercial ROI accumulates as AI-referred traffic, assisted conversions, and influenced pipeline build. It is typically reportable within a 60–180 day attribution window for pipeline and closed-won deals.
Want to see what this looks like before committing to a program?
See a sample AI visibility baseline (PDF) →Illustrative program timeline
1–10
Written report. Citation baseline across up to eight platforms, scored on AIVI™. Competitive gap map. Corrected structured-data templates. Prioritized 90-day roadmap.
1
Schema corrections deployed for entity clarity and search eligibility. AEO content restructuring begins on priority pages. Experimental, low-cost technical hygiene steps (like publishing an llms.txt file) may be added here. Evidence that llms.txt reliably increases citation likelihood is currently weak, and mainstream crawlers don't consistently prioritize it, so we treat it as optional housekeeping rather than a headline lever.
2–3
FAQ and HowTo structured data added where relevant for entity clarity and search eligibility. Note Google fully removed HowTo rich results (Sept 2023) and FAQ rich results (May 2026), so this is a validation and clarity step for AI systems, not a guaranteed AI-citation tactic. We also begin A/B testing entity citations on third-party sources (G2, Crunchbase, Wikipedia, structured blog hubs) to see which external references AI systems weight most in your category. Initial share-of-model measurement vs. baseline.
4–6
Published vendor case studies (see Supporting Industry Signals above) report citation and visibility share growth ranging widely by program and category. AI-referred sessions begin appearing in analytics. Separately, AI visibility often shows up as direct-traffic and branded-organic-search lift even where no clickable AI referral occurred ("dark" AI-influenced traffic), so referral-only reporting will understate real impact.
7–12
Established source authority and broad third-party corroboration may make competitors harder to displace over time. This is a plausible mechanism, not a proven guarantee. AI-referred and influenced-pipeline data becomes reportable for CFO review under the attribution framework above.
Timeline is illustrative. Actual results depend on starting position, content volume, category competitiveness, and execution velocity.
The argument by stakeholder
AI visibility investment typically requires sign-off from multiple stakeholders. Here is the core argument for each.
Forrester reports that 94% of business buyers now use AI at some point during their buying process (up from roughly 89% a year earlier). Brands absent from AI-generated responses risk losing consideration-set inclusion before the first sales conversation. This is a structural channel question, not just a marketing optimization opportunity.
AI visibility produces measurable, attributable outputs: share-of-model improvement tracked from an audited baseline, AI-referred traffic in existing analytics platforms, and pipeline influence reportable against program cost using ROI = (incremental gross profit − program cost) / program cost. The audit establishes the baseline at a defined fixed cost before any larger commitment is made.
AI-referred lead quality is promising but volatile: in Adobe's U.S. retail dataset, AI-referred visits converted to purchases 42% better than non-AI visits in March 2026, after converting 38% worse in March 2025. That is a retail purchase measure, so treat it as a signal to test in B2B, not a lead-quality benchmark. A buyer who arrives via an AI recommendation often carries specific, pre-qualified intent, but this is a channel to monitor and instrument, not yet a guaranteed conversion multiplier to plan a forecast around.
AI visibility integrates with and amplifies existing SEO and content investment. The foundation work (structured data, content architecture, entity clarity, and third-party citation building on sites like G2 and Wikipedia) improves performance across both traditional and AI-native search simultaneously.
AI visibility depends on systems and data a CDO owns: who controls structured data and CMS changes, how AI-referred traffic and self-reported discovery join analytics and CRM reporting, and what measurement governance and consent rules apply. A baseline audit gives the data team a defined scope to decide what to instrument, who owns schema changes, and how results connect to pipeline reporting.
The cost of inaction likely compounds. Established source authority and broad third-party corroboration may make competitors harder to displace once they've built citation presence. That's a plausible mechanism based on how retrieval-augmented systems favor well-corroborated sources, not a proven guarantee. Brands that establish an audited baseline now are at minimum not flying blind on a channel that's already influencing 94% of B2B buying processes.
The AI Visibility Audit is a written diagnostic delivered within 5–10 business days. It establishes your real citation baseline across ChatGPT, Gemini, Perplexity, Copilot, and Claude, scored on the same AIVI™ methodology behind our own published benchmark, with competitive gap analysis, schema recommendations, and a prioritized 90-day roadmap your team can execute the day it arrives.
brainpan.ai/contact/ · kwalsh@brainpan.ai · +1 703-951-7195
Kevin Walsh
Kevin Walsh is the founder of Brainpan.AI, where he builds the AI Visibility Engine, GEO/AEO strategy, schema systems, and citation optimization programs for brands that need to be retrieved, cited, and trusted by AI answer engines. Every third-party statistic on this page links to its original source and was checked against it; figures we could not source were removed, and corrections are documented inline.