Getting onto the AI shortlist: from mentioned, to recommended, to first

The shortlist is short, and most businesses are not on it

A results page lists every business that matches. An answer names a few and stops, so each place on it is scarce. In the probe that accompanied our audit of 9,483 small business websites, one buyer question from each business's own trade and metro went to Claude with web retrieval on. Of 9,240 businesses, 14.9% were named, and 17.7% among the 5,928 answers that gave a clearly differentiated list rather than a hedge. One question, one assistant, September 2026: a direction, not a rate. It still says what the starting point is. For most businesses, the ordinary state is to be left off the list for their own trade in their own town.

What happens on that list comes in three rungs: a mention, a recommendation, and first place. The rungs are worth separating because they fail for different reasons, and a fix aimed at the wrong one changes nothing you can see. Our methodology records two of them, whether the business was named and whether it was named first, and its score counts only the first rung: the share of twelve questions in which the business was named.

Rung one: mentioned, which needs you to be found and placed

Before an answer can name you, the assistant has to be able to read your pages and tell that they describe one business, in one place, doing the thing the buyer asked about. Three kinds of failure stop it at this rung, and the audit counted each.

• Access. 7.2% of the sites served our crawler a bot-protection challenge instead of the site, and 3.2% disallowed AI crawlers in robots.txt on purpose. The crawler access check reads your robots.txt for each answer-side token.
• Identity. Across the homepage, contact page and about page, 43.1% of the sites told a different story about who and where the business is; 36.9% showed more than one phone number, and 34.1% gave no postal address a machine could parse.
• Description. 17.8% had no schema.org markup at all, and a further 13.7% had business markup missing the fields that would make it usable. Structured data for AI covers which markup describes the business.

None of these is about persuasion. They decide whether the business exists, as one thing, for a reader that is not a person. Fix them first, because every later rung assumes them.

Rung two: recommended, which needs a reason the answer can repeat

A recommendation comes with a reason: the one to call for emergency work, the one that does automatic lessons, the one with evening hours near the station. That reason has to come from somewhere the assistant read, and on your own site it comes from text. The service named the way buyers type it, the place and the hours, a price or a range, and customers' own words about the job are the materials a reason is made of.

Most sites leave those materials out. In the audit, 61.5% had no question-and-answer content of any kind, 50.7% had at least one question as a heading with no answer beneath it that a machine could isolate, and 64.7% showed a crawler no review signal. The one controlled study we cite, Aggarwal et al. (KDD 2024), points the same way from the other side: on its benchmark, adding citations, quotations and statistics to a source raised its visibility in generated answers by up to about 40%. Specific, attributable text is what a reason can be lifted from.

The practical form is the answer block: a question as a heading, answered in the first sentence under it, with the conditions after. Writing to be cited shows how to build one, and reviews as an AI signal covers putting customers' words where a crawler reads them.

Rung three: first, which nobody outside the assistant controls

The order of names in an answer changes from run to run, and the choice is made inside systems no business or agency can reach. So first place is measured, not promised. What a business can do is be the one whose page answers the exact question, with the reason already stated, and then count how often it leads across runs and assistants.

Read the first-position rate per question rather than in total. A business that comes first on its own name and never on a problem question leads with people who already know it. One that comes first on a narrow question, a service in one town at one time of day, has found the question its pages answer best, and the next job is to write the same kind of page for its other services.

Which rung you are on, read from the panel

The panel is the one described in setting up prompt monitoring: the same questions every month, four assistants, three runs each. The seven GEO KPIs sets out how each rate is counted.

What does not put a business on the list

• Keywords repeated down the page. The same study found keyword stuffing did not help on its benchmark.
• Pages for towns you do not serve, over the same text. They describe places the business is not, and they compete with your real location page.
• A "best in town" page on your own site that ranks you first. It is your own opinion of yourself, and it gives an answer nothing another source said.
• Reviews you wrote or rewarded without saying so. Google's review snippet rules exclude them, and the platforms remove what they detect.
• Anyone who promises a place in the answer. Nobody outside the companies that build the assistants can deliver it.