AI Search Optimization for B2B Software: A Comparative Study of 30,000 ChatGPT, Gemini, and Google AI Overviews Answers

B2B software companies targeting overseas markets will encounter buyers asking AI search, “Which tool is right for our needs?” Do ChatGPT, Gemini, and Google AI Overviews give the same answers? To find out, Anymorph asked all three engines 1,471 B2B software questions in US English every day from October 17 to 23, 2026, and compared 29,847 answers. By matching answers to the same questions asked on the same day, we measured which companies each engine recommended first and which sources it used as evidence.

Research Results at a Glance

Methodology

Scope

The questions covered the types of things buyers ask before adopting software, such as recommendations for tools suited to their needs, tool comparisons, and questions about features and integrations. Because we asked all three engines the same questions on the same days, we could compare their answers one to one. All figures combine multiple projects; results for any specific company are not included.

Measurement Approach

• Recommended companies: We counted the companies named in each answer and the order in which they first appeared, based on the list of companies tracked by each measurement project. The company named first in an answer is referred to as the “first company.”
• Sources: For ChatGPT, we counted inline citations linked next to answer text; for Gemini and Google AI Overviews, we counted the sources shown with the answer.
• Overlap: The share of all companies mentioned (or domains cited) by two answers that appeared in both.
• Site types: We manually classified about 1,600 frequently cited domains. These domains accounted for 79.5–82.1% of citations for each engine; the rest were left unclassified. Page formats were automatically classified using URLs and titles.

All Three Engines Named the Same Company First in 37.0% of Cases

Answer Format by Engine

Gemini named companies most frequently and in the greatest numbers.

Recommended Companies: Pairwise Comparison of Engines

We counted only cases where both engines named at least one company. The companies mentioned by the two engines overlapped a little over half the time, but the company named first differed about half the time. Even Gemini and Google AI Overviews, both from Google, named the same company first in only 53.1% of cases.

Looking at All Three Engines Together

Among the 4,235 question–date pairs where all three engines named a company, all three named the same company first in 1,569 pairs (37.0%). In 744 pairs (17.6%), all three named a different company first. Of all companies mentioned by the three engines for a question, 37.2% were mentioned by all three. Even when we broke down the measurement projects by field, the share of cases where all three engines named the same company first ranged from 24% to 50%, never exceeding half.

Even the Same Engine Changed Its First Company from Day to Day

Even when we asked the same engine the same question again the next day, the first company changed in 34.0–37.4% of cases. For questions where company names appeared every day over a week, an answer for a single day included about 4 companies on average, while about 6 companies appeared over the 7 days. A ranking measured on a single day is only a snapshot of that day; you need to look at a full week to see the range of companies competing for a question.

The Sources Used as Evidence Barely Overlapped

We counted only cases where both engines included sources. Even though both engines answered the same question on the same day, the average overlap among their cited domains was no more than 10%. All three engines cited at least one domain in common in 23.4% of cases. The companies they recommended overlapped a little over half the time, but each engine selected different pages to support its recommendations.

Each Engine Cited Different Types of Sites

Software company sites were the most-cited type across all three engines. In 81.5% of ChatGPT answers, at least one inline citation linked to a software company site. Review and comparison platforms that collect user reviews accounted for no more than 1.4% for any of the three engines.

Google AI Overviews Cited Video and Communities

Google AI Overviews cited YouTube videos in one out of every three answers and Reddit posts in one out of every six. For the same questions, YouTube appeared in ChatGPT inline citations just 9 times. By contrast, ChatGPT cited GitHub repositories in 9.2% of answers, relying on developer resources more than any other engine.

Each Engine Cited Different Page Formats

Nearly half (48.7%) of ChatGPT’s inline citations pointed to documentation and guide pages, such as product documentation, help pages, and developer guides. Looking only at software company sites, 55.1% of the pages cited by ChatGPT were documentation or guides. Gemini cited comparison and recommendation articles most often; even among software company sites, these articles—such as “recommended tools” lists and “alternatives” comparisons—accounted for 42.5% of Gemini citations. Google AI Overviews cited a more even mix of article formats, with video and communities and social media together accounting for 25.8%.

ChatGPT Searched for Documentation and Integration Information

ChatGPT used 16,383 search queries across 9,829 answers (1.7 per answer, with a median query length of 12 words). The most common terms in its queries were:

One in four queries looked for documentation, APIs, or integrations. This aligns with the finding that documentation and guide pages accounted for the largest share of ChatGPT inline citations. One in five queries included the current year.

What B2B Software Companies Can Do

Measure Each Engine Separately

All three engines named the same company first in just 37.0% of cases. A company recommended first by ChatGPT cannot be assumed to be recommended first by Gemini or Google AI Overviews. Measure important questions separately for each engine, and tailor your approach to each one.

ChatGPT: Make Public Documentation and Help Content Available

Documentation and guide pages accounted for 48.7% of ChatGPT inline citations, and 25.4% of its queries looked for documentation, APIs, and integrations.

• Make product documentation and help content available as web pages that can be read without logging in.
• Describe integrations, APIs, SDKs, and supported platforms in plain language on feature-specific pages.
• Put pricing, security certifications, data storage locations, and self-hosting support on separate pages that are easy to find. 13.6% of queries looked for pricing, and 13.3% looked for enterprise requirements.
• If you offer open-source software or an SDK, keep the README in your GitHub repository up to date. ChatGPT cited GitHub in 9.2% of answers.

Gemini: Publish Comparison and Recommendation Articles on Your Own Site

Comparison and recommendation articles made up 35.9% of Gemini sources and were the most-cited page type among software company sites.

• Publish category comparison articles on your own site that address “which tool is right for which team.”
• State your conclusion in the first paragraph, then compare features, pricing, integrations, and implementation scale in a table using consistent criteria.
• Explain your comparison criteria and cite your sources, and include the date the page was updated.

Google AI Overviews: Provide Answers Through Video and Communities

Google AI Overviews cited YouTube in 32.7% of answers and Reddit in 16.2%.

• For each key use case, publish short demos and tutorial videos on YouTube, and explain the problem they solve in the title and description.
• Have a team member respond directly to questions from people considering adoption in communities such as Reddit and LinkedIn.
• Link from videos and community responses to more detailed documentation and comparison articles.

Include the Reference Year and Update Date on Pages

The year 2026 appeared in 19.2% of ChatGPT queries. Include the reference year and the date the page was last updated in the title or body of comparison articles, pricing pages, and recommendation lists.

Track Results Over a Week, Not Just a Day

Even for the same engine, the first company changed the next day in 34.0–37.4% of cases, and 1.5–1.7 times as many companies appeared for a question over a week as on a single day. Don’t judge rankings based on a single day’s results. Track for at least a week both the share of days a company was recommended first and the share of days it was mentioned.

Limitations

• This is an observation from one week, October 17–23, 2026. Results may change if engines change their models or search methods.
• The questions were general questions in US English that Anymorph measures for B2B software companies. They do not fully represent the distribution of questions across the entire market, and results may differ in other languages and countries.
• Recommended companies were based on the list of companies tracked by each measurement project. Companies not on the list were not counted.
• We counted sources according to how each engine presents them: inline citations for ChatGPT and displayed sources for Gemini and Google AI Overviews.
• Site types were manually classified for frequently cited domains, and 17.9–20.5% of citations per engine were unclassified. Page formats and query terms were automatically classified based on URLs, titles, and words.
• A citation means that a source was used in an answer. This study did not determine whether it led to a click or adoption.
• Differences between engines reflect observed distributions; we did not experimentally test what causes a particular type of source to be cited.

Conclusion

1. Recommendations differ by engine. All three engines named the same company first for the same question in 37.0% of cases.
2. The evidence differs even more. The average overlap among domains cited by two engines was 6.0–9.9%.
3. Each engine needs different pages. ChatGPT frequently cited documentation and guides, Gemini comparison and recommendation articles, and Google AI Overviews video and communities.

B2B software companies need public documentation, comparison articles, videos, and community responses to be recommended consistently across all three engines. After updating your pages, measure the same questions repeatedly for at least a week on each engine to confirm whether the number of days you are recommended first has actually increased.

Appendix: Research Data