AI models work like digital recruiters: they do not simply take your website at its word. They run reference checks through fan-out queries. These are rapid background searches in which AI scans the internet for evidence of your experience, expertise, authority and trustworthiness (E-E-A-T) to verify your claims. For marketers, this means AI visibility is no longer determined only by what you say about yourself on your own site, but by what external sources confirm about your authority.
A good recruiter does not make a decision based on the interview alone. The real selection happens during the reference check: what do outside sources say about this person, beyond the polished story they tell themselves?
The same thing happens in AI-driven search, including ChatGPT, Perplexity and Google’s AI Overviews. This is how AI-driven search works: the model makes background searches—fan-out queries—to verify your claims. As a B2B marketer, this is the concept you need to understand to stay visible in 2026. If you are a B2B marketer, this is the concept you need to understand to remain visible in 2026.
The technology: what is a fan-out query?
When a user asks an AI model a question, something fascinating happens behind the scenes. AI does not simply accept the question; it carries out a series of rapid follow-up actions.
A fan-out query, simply explained: it is an action in which a search query branches out to dozens of different places at once. In the background, AI runs searches you cannot see to gather evidence, check facts and find context.
Suppose a prospect asks: “What is the most reliable logistics software for the pharmaceutical industry?” The AI may start a fan-out like this:
- Query 1: “What are the reviews of brand X on independent platforms?”
- Query 2: “Does brand X have certifications for the pharmaceutical industry?”
- Query 3: “What do experts on LinkedIn say about brand X’s implementation time?”
The benchmark: E-E-A-T as a diagnostic framework
As AI runs those fan-out queries, it assesses one key criterion: E-E-A-T. This is no longer a vague SEO term; it gives your brand something concrete to work with.
- Experience: can AI find real-world examples or case studies from you in that second search layer?
- Expertise: are your people quoted in trade media or authoritative blogs?
- Authoritativeness: do other reliable organisations refer to you as a go-to source?
- Trustworthiness: are your legal details in order, and is sentiment in external reviews positive?
In that second search layer, the model decides whether your brand is credible enough to cite in its final answer to the customer. If it finds no E-E-A-T evidence in the fan-out, it simply leaves you out.
The buyer journey in the AI funnel
The traditional buyer journey no longer takes place only on your website. Through fan-out queries, AI gathers the information a customer needs to move through the funnel.
| Journey stage | AI’s fan-out focus | What you need |
|---|---|---|
| Awareness | Looks for educational sources and definitions. | Expertise: thought leadership on external platforms. |
| Consideration | Looks for comparisons and Reddit discussions. | Experience: visible customer cases and community buzz. |
| Decision | Looks for certifications, terms and critical reviews. | Trust: an impeccable reputation on third-party sites. |
The new question for marketers
The question is no longer: “How do I rank for this keyword?” The real question is: which search steps does the model take in response to my customer’s question, what evidence does it expect to find along the way, and where does my brand lack enough trust to become visible?
If a model repeatedly finds no independent confirmation in its fan-out queries, you do not have an SEO problem; you have an authority problem.
In the coming period, I will report on this website about my measurements using the Quolity Chrome extension. I have logged thousands of these fan-out queries to discover patterns. The conclusion: do not just look at your own site. Build an online presence that can pass every AI model’s reference check.
That is the question at the heart of this article: what does an AI model hear about you when it runs a reference check?
This translation was made by AI.