How buyers use AI assistants at each stage of the funnel

Guide · Market Growth · 6 min read · last verified 2026-07-25

Buyers now run the whole funnel through an AI assistant

B2B buyers use AI assistants differently at each stage of the funnel: in awareness they ask open questions ("what is X," "who does X"); in consideration they ask for a field ("best X for Y"); in shortlist they force a choice ("X vs Y," "alternatives to Z"); and in validation they check trust ("is X legit," "X reviews"). The stage that decides your pipeline is the shortlist — because the list of vendors a buyer will actually evaluate is now assembled inside the AI answer, before a human ever visits your site. If your brand is absent at that moment, you are not losing a ranking; you are losing the consideration set. That is why AI visibility has to be measured per intent, not as one blended score.

The four stages, mapped to what buyers actually type

Each stage has a recognisable question shape and a different job for the AI answer. The table maps them, and shows where absence hurts most.

Treat these as four distinct measurement surfaces, not one funnel. The same brand can be strong in awareness ("the model knows we exist") and invisible in shortlist ("it never lists us against our closest rival") — and only the second gap loses revenue. Mapping which question belongs to which stage is its own discipline; see how buyer questions cluster into intent for the underlying taxonomy.

Awareness: "what is X / who does X"

At the top of the funnel, buyers ask AI to orient them: what is this category, what problem does it solve, who plays in it. The assistant answers by defining the space and naming a handful of representative brands. Your goal here is to be one of the names the model reaches for when it describes the category — call it category association (derived from how these answers are structured, not an observable ranking). This is the layer where AI assistants reshape category discovery: the model, not a search results page, decides who represents the space.

What earns that association is corroboration across sources the model trusts, not polish on your own site. Brands are cited more through third-party sources than through their own domains, according to industry analyses of AI citations, and reference sources carry weight: according to one analysis of ChatGPT citations, Wikipedia accounts for roughly 7.8% of them. So awareness visibility is built by being described consistently, in plain language, across the places the model reads.

To do: make sure a clear definition of what you do and who you serve exists in structured, quotable form — on your site and, more importantly, in the third-party sources AI assistants pull from.

Consideration: "best X for Y"

Now the buyer wants options. "Best X for Y," "top tools for [use case]," "X for [industry]." The assistant responds with a list — sometimes ranked, sometimes grouped by use case. This is the longlist, and it is generated, not retrieved: the model composes it from what it has read about many vendors. The queries here are unmistakably commercial; understanding what buyer intent looks like in AI search helps you tell a "best-of" question from a definition question and score each on its own terms.

According to industry analyses of AI citations, comparison and "best-of" articles take the largest single share of AI citations (about 33%), which tells you where the model looks when it builds these lists. Getting onto the longlist is less about claiming you're best and more about being present, with evidence, in the "best X for Y" content that already exists — ideally third-party roundups, not only your own pages.

The honest caveat: these lists vary between assistants and even between runs of the same prompt, because the underlying selection is probabilistic and not fully observable. Appearing once is not the same as reliably appearing — which is exactly why a single spot-check misleads.

Shortlist: "X vs Y" and "alternatives to Z" — where absence costs the most

This is the stage that decides deals. Buyers ask "X vs Y," "alternatives to Z," "is X better than Y" — and the assistant narrows the field to two, three, maybe four names and contrasts them. The shortlist is being formed inside the answer, before the buyer clicks anything.

If you're not named here, you don't get a chance to compete. There is no page two, no "see more" — the buyer takes the two or three names the assistant offered and starts evaluating those. Absence at this stage isn't a soft loss of traffic; it's exclusion from the consideration set. And it compounds: the more consistently the answer settles on the same short list, the more your absence becomes the default — the dynamic behind why absence compounds in AI search.

Two moves matter most. First, make sure strong "X vs Y" and "alternatives to Z" material exists for your brand against your real competitors, with specific, checkable contrasts — comparison pages are what these answers draw on. Second, watch the "alternatives to [your competitor]" queries; that is where a challenger gets named, or doesn't.

Validation: "is X legit / X reviews"

Late in the journey, once a shortlist exists, buyers de-risk. "Is X legit," "X reviews," "X pricing," "X complaints." The assistant summarises reputation and risk from reviews, forums, and discussion. Here the danger isn't only absence — it's an unanswered doubt. If the model has thin or negative material to work with, that is what it repeats back to the buyer.

Review platforms and community discussion feed these answers heavily — see how review platforms feed AI answers — so validation visibility is largely earned off-site: real reviews, pricing the model can find, and answered objections. You can't fabricate this, and you shouldn't try; but you can make sure the honest, positive evidence exists and is findable.

Why you measure visibility per intent, not as one number

Because the four stages behave independently, a single "AI visibility score" hides the gap that actually costs you money. You can look healthy on awareness and be missing from every shortlist. The useful unit of measurement is a benchmark question set — a fixed list of the real questions your buyers ask at each stage — scored per intent, and re-measured on the same locked benchmark so movement is real and not model drift.

This is the loop Magrios runs: measure how AI answers see you across awareness, consideration, shortlist, and validation questions; find the intents where you're absent (usually the shortlist); act on those gaps; then re-measure on the locked benchmark, with a source link behind every claim so you can see why the answer named who it named. The score isn't the point — the per-intent gap and its trend over time are.

Two honesty notes worth keeping. AI answer selection is not fully observable, so treat every mechanic here as reported or derived, not a guaranteed formula — no tool can promise placement. And the Princeton GEO study (KDD 2024) is a useful guide to what moves visibility once you know where you're absent: citing sources boosted visibility by +40%, adding statistics by +37%, and adding quotations by +30%, while keyword stuffing actually hurt (−10%). Fix the gap first; then earn the answer with evidence.