BlueTuskr reported on September 9, 2026 that, across “dozens” of industrial brands it manages, 85% were receiving AI-referred traffic and 76% had generated revenue directly from that traffic. The agency also said AI-referred traffic across the analyzed accounts had more than tripled year over year.
The 76% figure is useful, but only inside that boundary. BlueTuskr did not disclose the exact number of brands, the industry mix, geography, observation window, analytics configuration, revenue threshold, attribution model, or attribution window. It is therefore evidence about BlueTuskr’s managed portfolio, not evidence that 76% of industrial companies generally make money from AI search.
That distinction matters because the current Google results mix several different measurements under “AI referral revenue”: traffic growth, conversion lift, revenue per visit, and the share of brands recording any revenue. This page keeps those denominators separate and states what BlueTuskr’s figure can and cannot support.
Direct answer — How much AI-referral revenue are industrial brands generating?
BlueTuskr says 76% of the industrial brands in its managed dataset had generated some revenue from AI-referred traffic, while 85% were receiving AI traffic. The agency did not disclose the exact sample size, revenue threshold, attribution method, or observation window, so the result should not be generalized to all industrial companies.
What BlueTuskr actually measured
BlueTuskr’s September 9 release describes a managed-client dataset rather than a market-wide survey. It says “dozens” of industrial brands were analyzed and reports three portfolio-level findings:
- 85% were receiving traffic from AI platforms.
- 76% had generated revenue directly from AI-referred traffic.
- AI-referred traffic had more than tripled year over year across the analyzed accounts.
The release also says 37% of AI-referred traffic came from ChatGPT, making it the largest named source in the reported mix. Perplexity supplied 31%, Gemini 15%, Copilot 9%, and Claude 8%.
Those percentages describe the source mix of AI-referred traffic in the analyzed accounts. They do not show the share of total site traffic, total revenue, or total conversions attributable to AI. Without those base values, a channel can grow quickly while still remaining small in absolute terms.
Why 76% does not mean 76% of industrial companies
The denominator is the most important constraint. BlueTuskr says the dataset covers industrial brands it manages. That creates at least three selection boundaries.
First, these are companies using a specialist ecommerce and digital-marketing agency, which may differ from the wider industrial market in site maturity, analytics implementation, paid-media activity, ecommerce capability, and willingness to invest in digital acquisition.
Second, the exact count behind “dozens” is not published. A percentage such as 76% can move materially depending on whether the sample contains, for example, 25 brands or 75. IVRIS therefore does not convert “dozens” into an invented sample size.
Third, the release does not disclose how “generated revenue directly from that traffic” was operationalized. It could refer to last-click revenue, session-based attribution, a configured analytics channel grouping, or another attribution rule. Those methods are not interchangeable.
The stronger signal may be channel penetration, not revenue magnitude
The 85% traffic figure is easier to interpret than the 76% revenue figure because it asks a simpler question: did the brand receive any measurable traffic from AI platforms?
On that measure, BlueTuskr’s portfolio suggests AI referrals are already common among the industrial ecommerce sites it manages. The separate 76% revenue measure then indicates that most of those sites also recorded at least some revenue attributed to AI-referred sessions.
But “some revenue” is not the same as material revenue. The release does not publish revenue per visit, conversion rate, average order value, assisted-conversion value, customer lifetime value, or AI’s share of total ecommerce revenue.
That means the study is useful for answering whether the channel is present and monetizing somewhere in the funnel, but not for estimating how financially important the channel is relative to search, paid media, marketplaces, email, distributors, or direct traffic.
Source mix shows ChatGPT and Perplexity dominate this dataset
BlueTuskr’s source breakdown places ChatGPT at 37% of AI-referred traffic and Perplexity at 31%. Together, those two sources account for 68% of the reported AI-referral mix.
Gemini contributes 15%, Copilot 9%, and Claude 8%. The five percentages sum to 100%, suggesting the release is presenting a closed distribution across the named AI sources for the analyzed traffic.
That distribution is useful for prioritization, but it should not be treated as a universal AI-search market-share estimate. Referral visibility depends on the brands, products, prompts, audience behavior, browser and app environments, and the engines’ willingness to expose clickable source links.
What B2B industrial teams can take from the data
The defensible takeaway is operational: AI-referred traffic is measurable enough in BlueTuskr’s industrial portfolio that most analyzed brands are seeing it, and most have attributed at least some revenue to it.
For B2B and industrial ecommerce teams, that creates a practical measurement checklist. Confirm that AI referrers are preserved in analytics, separate source-level traffic from generic “organic” buckets, compare assisted and last-click outcomes, and record the revenue threshold used before declaring the channel commercially meaningful.
It is also worth separating discovery from transaction behavior. Industrial purchases often include technical validation, procurement review, distributor involvement, quotation requests, repeat orders, and offline sales activity. A session that starts in ChatGPT or Perplexity may influence revenue without completing an ecommerce transaction in the same visit.
That makes attribution design particularly important for industrial companies. The headline question is no longer only whether AI can send traffic. It is whether the organization can measure how that traffic contributes across a longer commercial journey.
What the BlueTuskr data does not establish
The release does not establish that AI referrals outperform Google organic search, paid search, email, marketplaces, or direct traffic. It does not establish that AI caused the reported year-over-year growth in revenue. It does not establish that 76% of the industrial market earns AI-attributed revenue.
It also does not disclose enough methodology to reproduce the result independently. The exact account count, company composition, analytics rules, attribution window, bot filtering, geographic mix, and historical comparison basis remain undisclosed.
Those gaps do not make the figures unusable. They define the level at which the figures can be used responsibly: as a first-party agency observation across its managed industrial-brand portfolio.
FAQ
What percentage of BlueTuskr’s industrial brands received AI traffic?
BlueTuskr reports that 85% of the industrial brands in its managed dataset were receiving AI-referred traffic.
What percentage generated revenue from AI referrals?
BlueTuskr reports that 76% had generated revenue directly from AI-referred traffic. The exact revenue threshold and attribution method were not disclosed.
Which AI platform sent the most referral traffic?
ChatGPT led the reported source mix at 37%, followed by Perplexity at 31%, Gemini at 15%, Copilot at 9%, and Claude at 8%.
Does this mean 76% of all industrial companies earn revenue from AI search?
No. The result applies to BlueTuskr’s managed dataset of “dozens” of industrial brands. The exact sample size and market representativeness were not disclosed.
How fast did AI referral traffic grow?
BlueTuskr says AI-referred traffic across the analyzed accounts more than tripled year over year. The release does not publish the underlying traffic counts or the exact comparison window.
