How AI Assistants Choose Which Businesses to Recommend

A growing share of the question "who should I hire for this" never reaches a search results page any more. It gets asked to ChatGPT, Gemini, Perplexity, or Google's AI Overviews, and the answer names two or three businesses directly. That shift changes what reputation damage costs, because there is no page two in an AI answer. This piece covers what actually feeds those recommendations and why policy-violating reviews carry more weight here than anywhere else.

How do AI assistants decide what to recommend?

They summarize public data about businesses matching a category and location, then surface the ones that best fit the question as asked. Review content, ratings, listing data, and web pages describing the business all feed that summary. There is no ad auction, no submission process, and no ranking dashboard to check.

That last point causes most of the confusion. Businesses ask how to get listed, and the honest answer is that there is no list. The system is reading whatever public material exists about businesses in your category and assembling an answer from it. Your leverage is over what that material says.

What data actually feeds the answer?

Four sources do most of the work: structured business listing data, review text, your own website, and third-party pages that mention you. Review text is the densest of the four because it describes actual experience rather than marketing claims.

Do Google reviews affect ChatGPT and Gemini answers?

Yes, substantially. Review text is the richest public description of what a business is genuinely like, so it shapes both which businesses get named and how they get described. Gemini draws on Google's own Maps and review data directly, and assistants with live web access encounter the same review content on the way through.

What matters is not only the star average but the recurring themes. Summarizers are built to find patterns in text, so three reviews describing the same specific problem carry far more weight than the rating drop those three reviews caused. A business can hold a 4.5 average and still be described as the one with billing disputes, because the theme is more quotable than the number.

Why do fake reviews hurt more here than in search?

A search results page is a list the user can scroll past. An AI answer usually names two or three businesses and stops. Being characterized badly in that format is close to being excluded outright, and the user never sees the alternative you would have been ranked alongside.

There is a second effect that compounds the first. Search shows the review alongside your rating, your photos, your responses, and nine competitors, and users weigh all of it. An AI answer compresses everything into a sentence or two, which means a single vivid theme can become the whole characterization. A fake review alleging something specific and memorable does disproportionate work in that compression, because specific and memorable is exactly what a summarizer keeps.

Can I optimize for AI recommendations?

Partly, and the levers are less exotic than the topic suggests. Accurate and consistent listing data, pages that state plainly what you do and where you do it, and a genuine recent review history all help. Manipulating reviews does not help and risks the profile these systems read.

Concretely, the things worth doing are: make sure your category and service area are correct everywhere they appear, write pages that answer the questions people actually ask rather than pages of positioning language, keep hours and contact details current across directories, and keep earning genuine reviews from real customers at a steady pace.

The thing not to do is treat this as a new channel to game. Buying reviews to improve how an assistant describes you is the same policy violation it has always been, with the same enforcement risk attached, and the profile suspension that follows removes you from every channel at once.

Does responding to a review help?

Very little, at least for this purpose. Summarizers weight the review body far more heavily than an owner reply, so a well-written response rarely offsets a damaging claim in an AI answer even when it works well for human readers.

Keep responding anyway, because real prospective customers read replies and a thoughtful one demonstrably affects their decision. Just do not expect it to change the sentence an assistant produces. For that, the source has to change.

What actually changes the input?

Removing reviews that violate platform policy is the one action that alters the underlying data rather than adding to it. When a fake review comes down, it stops being available to summarize, and the theme it was contributing disappears with it.

This is the practical case for treating fake reviews as urgent rather than annoying. A policy-violating review left in place is no longer just a number on your profile. It is training material for how an entire class of systems describes your business, and it keeps being read long after the rating impact has been absorbed. That is the reasoning behind our Google review removal work, and it applies with equal force on Yelp and Trustpilot, both of which are widely crawled.

The effect is sharpest in high-consideration categories where people ask an assistant for a shortlist rather than browsing. Financial advisors, contractors, and medical practices are all in that group, and all of them see a larger gap between what one bad theme costs in search and what it costs in a two-name answer.

What should businesses expect next?

Expect the share of decisions made this way to keep rising, and expect the underlying data to stay roughly the same. The interfaces are new. The inputs are the same public listing and review data that has driven local discovery for years.

That is genuinely good news for businesses that have kept their listing data clean and their review profile honest, because the work transfers. It is worse news for anyone who has been carrying a handful of fake reviews on the theory that the rating hit was survivable, since those reviews now do a second job.

One practical habit is worth adopting now: ask the assistants about your own business periodically, in the phrasing a customer would use, and record what comes back. It costs a few minutes, it is the only visibility into this channel that currently exists, and it surfaces mischaracterizations while they are still traceable to a specific review. Businesses are routinely surprised by what a summary emphasizes, and the surprise is usually a review they had stopped thinking about.