From answer to checkout: what the evidence says about getting a B2B company recommended by AI assistants

Author: Paul Maxwell, founder of AEO HQ. Version 1.0, September 2026. Every number and quotation in this paper was checked against its source on September 27 and 28, 2026.

Executive summary

Many B2B buyers now begin vendor research in an AI assistant. This paper reviews the evidence published between September 2023 and September 2026 on two linked questions: what makes an assistant name a company, and what has to happen between that answer and a purchase. It grades the evidence behind each finding and keeps findings separate from AEO HQ's recommendations.

The evidence supports a short chain.

1. Retrieval is the gate. ChatGPT, Google's AI features, Copilot, Claude, and Perplexity each document that the pages they cite come from a search index, their own or a partner's. A page that is missing from the relevant index cannot be cited through search.
2. Among retrieved pages, relevance and position matter most. In controlled tests, relevance to the question and position among the retrieved sources drove citation, and specific facts such as prices and dates helped. Most rewriting tactics sold as generative engine optimization (GEO) did not replicate. The best-known result, that GEO "can boost visibility by up to 40%", was measured for a page already placed in a simulated engine's context, and a later benchmark found statistically significant gains in only 3 of 54 cases.
3. Other sites carry the recommendation. Assistants cite third-party pages far more often than a company's own, and in the largest study, mentions of a brand on other websites and on YouTube tracked its visibility in AI answers far more closely than backlinks did. This evidence is correlational.
4. Recommendations are distributions. The same prompt rarely returns the same list twice, so AI visibility has to be measured as a rate over many runs, not read from one answer.
5. Buyers verify, then buy elsewhere. B2B buyers use assistants to build shortlists and then check what they were told, most often on the vendor's own website. No major assistant documents a way to sell a professional service inside the chat, so the purchase happens on the seller's own checkout.

Two sets of limits frame all of this. Google's spam policies cover attempts to manipulate AI answers, and Bing warns that such content may be removed from its search experiences. The U.S. Federal Trade Commission's rule on fake reviews covers AI-generated reviews and incentives tied to a review's sentiment.

The program that follows from the evidence is ordinary and specific: be indexed, state the offer as checkable facts, keep those facts consistent everywhere, earn independent corroboration, make the checkout completable without a sales call, measure with repeated runs, and stay inside the rules. Section 4 grades each step. No study has measured how long any of this takes, and this paper makes no promise about timing.

AEO HQ sells answer engine optimization (AEO) audits and implementation, so it has an interest in these conclusions. Section 7 describes that interest.

1. Introduction

1.1 Why the question matters

Three recent surveys show the scale of AI use in B2B buying. In a March 2026 survey of 1,076 B2B software buyers, 51% said they start their research with an AI chatbot more often than with Google, up from 29% eleven months earlier (vendor survey). Among 519 U.S. B2B professionals who use AI at work, agencies and service providers were the category most often researched with AI, at 51% (vendor survey, March–April 2026). Gartner found that 45% of 646 B2B buyers had used AI during a recent purchase (August–September 2025). The figures differ because the samples and definitions differ, as section 3.5 shows. All three show substantial use.

For a B2B company, and especially one that sells services, two practical questions follow: what makes an assistant name the company when a buyer asks for options, and what has to be true, once the company is named, for the buyer, or an agent acting for the buyer, to complete a purchase. Advice on both questions is plentiful and mostly untested. Google lists services "Promising improvements for AI experiences and search formats (also known as 'AEO' or 'GEO' tools)" among those site owners should evaluate carefully, and it says third-party tools "can't guarantee performance". This paper sets out what the evidence supports, what it contradicts, and where it is silent.

1.2 Definitions

• Answer engine optimization and generative engine optimization. Two names for overlapping work aimed at being cited, described accurately, and recommended in answers written by AI systems. Google calls AEO and GEO "both terms you may see used to describe work specifically focused on improving visibility in AI search experiences". This paper uses AEO for the practice and GEO where the research literature uses that name.
• AI assistant. ChatGPT, Google's AI Overviews and AI Mode, Gemini, Claude, Perplexity, and Microsoft Copilot.
• Parametric knowledge and retrieval. Parametric knowledge is what a model learned in training. Retrieval is the step in which an assistant searches an index and reads pages before it writes. Basing an answer on those pages is called grounding.
• Query fan-out. Turning one question into several searches. Google describes AI Overviews and AI Mode as "issuing multiple related searches across subtopics and data sources".
• Mention, citation, and recommendation. A mention is a company's name in an answer. A citation is a link to one of its pages shown with the answer. A recommendation is a mention offered as an option for the buyer's need.
• Agentic commerce. Buying in which an AI agent carries out some of the shopping or checkout steps for a person.

1.3 Scope

The review covers unpaid visibility in the six assistants above, not advertising inside them. Its focus is B2B companies, with particular attention to firms that sell services at a fixed price, because that is the case AEO HQ is built around and the case the research covers least. Legal points refer to the United States. The diagram below shows the path the paper follows.