How AI Search Engines Work: A Practical Guide for B2B Marketers

AI search engines can combine model knowledge, information supplied in the prompt, and current information retrieved from the web. For complex questions, systems may use query fan-out to run several related searches before synthesizing an answer. The sources they cite are therefore probabilistic and can vary by prompt, platform, context, and available information.

AI search is changing how people discover businesses, evaluate solutions and make purchasing decisions.

Instead of presenting users with a list of links, AI search engines interpret the question, collect information from multiple sources and synthesize it into a direct answer. Sometimes those answers rely primarily on knowledge learned during model training. In other cases, the system searches the live web to find current information.

For brands, this creates an important question:

How do AI search engines decide which sources, companies and perspectives to include in their answers?

The answer lies in three mechanisms: training data, real-time retrieval and query fan-out. Together, they determine whether your brand becomes part of an AI-generated answer—or remains invisible.

From AEO theory to execution

In our previous article, Beyond the Blue Link: Why Answer Engine Optimization Is the Next Frontier for High-Value Conversions, we explored why visibility in AI-generated answers is becoming increasingly valuable for B2B companies.

But understanding the opportunity is only the beginning.

To develop a reliable Answer Engine Optimization strategy, you need to understand how AI systems find, evaluate and retrieve information. Without that knowledge, AEO can quickly become a random collection of tactics.

Three mechanisms are particularly important:

1. Training data and model knowledge

2. Real-time retrieval

3. Query fan-out

1. Training data versus real-time retrieval

AI systems do not always find information in the same way.

Depending on the platform, query and configuration, an answer can be based on information learned during model training, information supplied by the user or information retrieved from external sources.

Training data

Large language models are trained on extensive collections of text and other data. During this process, they learn patterns, relationships and associations between concepts.

The model does not store this information as a searchable library of complete articles. Instead, training shapes the statistical relationships the model uses to generate an answer.

This distinction matters for brands.

If reputable websites, industry publications, customer reviews and other online sources repeatedly associate your company with a particular topic, solution or area of expertise, those associations may contribute to how AI systems understand your brand.

However, training data has a fundamental limitation: it does not provide a continuously updated view of the internet. A model’s built-in knowledge may therefore be incomplete or outdated when a question concerns recent developments, changing product information or current events.

Real-time retrieval

To answer questions that require current or more specific information, AI systems can retrieve information from external sources.

One common approach is Retrieval-Augmented Generation, or RAG. A retrieval system finds relevant documents or web pages and provides their contents to the language model. The model then uses this information to construct its response.

In practical terms, the process can look like this:

1. The user submits a question.

2. The system determines that additional information is required.

3. Relevant sources are retrieved.

4. The model evaluates and synthesizes the retrieved information.

5. Supporting sources may be presented as citations or links.

This is the part of AI search that brands can influence most directly.

Google confirms that pages must be indexed and eligible to appear in conventional Google Search before they can be included as supporting links in AI Overviews or AI Mode. Google also recommends making content crawlable, accessible through internal links and available in clear textual form.²

The strategic takeaway

Brands should account for both forms of discovery.

To strengthen how AI systems understand your company over time, you need consistent mentions and descriptions across your wider digital footprint. To increase your chances of being retrieved today, you need a technically accessible website with clear, useful and authoritative content.

This means AEO is not limited to publishing articles on your own domain. It also involves:

• Building credible third-party mentions

• Maintaining consistent brand and product information

• Creating content around commercially relevant questions

• Making important information easy to crawl and understand

• Keeping time-sensitive information accurate and up to date

2. How query fan-out changes search

Traditional keyword research usually starts with a search query and the pages that rank for it.

AI search can operate differently.

A user might enter one detailed prompt, but the AI system can decompose it into multiple related searches. This process is called query fan-out.

Google confirms that AI Overviews and AI Mode may use query fan-out to issue multiple searches across related subtopics and data sources. This allows the system to find a wider and more diverse set of supporting pages than a conventional search might return.²

Consider this prompt:

What is the best cybersecurity partner for a Dutch manufacturing company with 500 employees that needs 24/7 monitoring?

An AI search engine might investigate questions such as:

• Which cybersecurity providers operate in the Netherlands?

• Which providers specialise in manufacturing?

• What does 24/7 security monitoring include?

• What is the difference between an SOC and MDR?

• Which providers have relevant certifications?

• What do customers say about these providers?

• Which solutions are suitable for a company with 500 employees?

• Does the provider offer Dutch-speaking analysts?

The exact subqueries are not necessarily visible to the user. Yet they influence which sources and brands the system encounters while constructing its answer.

How extensive can query fan-out become?

The number of searches depends on the platform, model and complexity of the prompt.

In a 2025 study of Gemini 3, Seer Interactive observed an average of 10.7 fan-out queries per prompt. The smallest number observed was three, while the largest was 28.³

These results should not be treated as a universal benchmark for every AI platform. However, they demonstrate how one user prompt can create a much larger network of searches behind the scenes.

Why isolated keyword optimization falls short

If your website only answers the primary question, it may be visible for one part of the retrieval process but absent from all the adjacent searches.

A stronger approach is to build a connected body of content around:

• The buyer’s main problem

• Related problems and symptoms

• Selection criteria

• Alternatives and comparisons

• Implementation questions

• Risks and objections

• Costs and commercial considerations

• Industry-specific applications

• Evidence, cases and measurable outcomes

This does not mean placing every possible answer on one enormous page. It means developing a logically connected topic ecosystem, supported by intentional internal linking.

The strategic takeaway

Stop treating every keyword as an isolated content assignment.

Instead, map the complete network of questions a buyer might ask before making a decision. Then determine which questions deserve a dedicated page, which can be addressed within a broader guide and how those pages should connect.

The objective is to become a useful source across the entire fan-out—not merely an exact match for the original prompt.

3. Why AI citations are random

Traditional rankings can fluctuate, but they are generally presented as an ordered list. AI-generated answers are more variable.

The sources included in an AI answer can change based on factors such as:

• The precise wording of the prompt

• The context provided earlier in the conversation

• The platform and model being used

• Whether live web retrieval is activated

• Which subqueries the system generates

• Which pages are available at retrieval time

• How the system synthesizes the evidence

This means AI visibility should not be measured as a single fixed position.

A company might appear in six out of ten responses for one prompt, disappear when the wording changes and be mentioned consistently for a closely related question.

The more useful metric is therefore share of visibility across a representative set of prompts.

For example, instead of asking whether your company “ranks number three” for one AI prompt, measure:

• How often the brand is mentioned

• How often the website is cited

• Which pages receive citations

• Which topics produce visibility

• Which competitors appear more frequently

• How visibility changes across platforms and prompt variations

• Whether mentions are positive, neutral or negative

The signals that can increase your probability of being cited

There is no guaranteed formula for earning AI citations. However, current research points to several signals that brands can influence.

1. Relevance

Your content must directly answer the question being investigated.

Clear definitions, focused sections, descriptive headings and specific answers make it easier for retrieval systems to identify relevant passages.

2. Topical depth

Because query fan-out explores related subtopics, a connected body of content creates more opportunities to be discovered.

Topical depth is not the same as publishing large quantities of generic content. Every page should serve a clear purpose within the buyer’s research journey.

3. Authority

Traditional search visibility remains relevant, particularly within Google’s own AI search experiences.

Ahrefs’ 2025 research originally found that 76.1% of pages cited in Google AI Overviews ranked in the traditional top 10 for the same query. However, an updated analysis published in March 2026 found that this overlap had fallen to approximately 38%.¹

This change is significant.

It suggests that strong traditional rankings can still contribute to AI Overview visibility, but ranking in the top 10 is no longer the near-universal gateway earlier research implied. AI search can surface sources from a wider set of results, including pages that do not rank prominently for the user’s original query.

That is logical when viewed through query fan-out: a page may not rank for the original question but may rank highly for one of the related subqueries generated behind the scenes.

4. Freshness

Freshness matters most when the subject changes over time.

In an analysis of 17 million citations across seven AI search platforms, Ahrefs found that URLs cited by AI assistants were, on average, 25.7% more recently updated than traditional organic search results.⁴

This does not mean every article needs constant superficial updates. It means dates, statistics, recommendations, product information and other time-sensitive details should be reviewed regularly.

A page that is old but accurate can still be valuable. A page that looks current but contains outdated information is not.

5. Independent corroboration

AI systems can encounter information about your company on your own website as well as on third-party sources.

Consistent descriptions across industry publications, customer reviews, comparison pages, partner websites and expert content can strengthen the association between your brand and a particular category.

The objective is not to repeat an identical marketing message everywhere. It is to build a credible pattern of independent evidence.

6. Technical accessibility

Even outstanding content cannot be retrieved if search systems cannot access it.

At a minimum:

• Do not unintentionally block relevant crawlers

• Ensure important pages are indexable

• Use clear internal links

• Make essential information available as text

• Keep structured data consistent with visible content

• Avoid hiding core answers inside inaccessible interfaces

• Maintain accurate canonical tags and status codes

Google explicitly states that the existing foundations of SEO continue to apply to its AI features and that no special AI-specific markup is required.²

A practical AEO framework for B2B brands

Understanding the mechanics is useful only if it changes how you execute.

A practical AEO programme can be organised into five steps.

Step 1: Map the decision journey

Identify the questions buyers ask from initial problem awareness through final vendor selection.

Include broad informational questions as well as commercial, technical and risk-related questions.

Step 2: Build a prompt and fan-out map

Turn the decision journey into realistic prompts.

For every primary prompt, identify the subquestions an AI engine may need to answer. These subquestions form your potential query-fan-out network.

Step 3: Audit your current visibility

Test representative prompts across relevant AI platforms.

Record:

• Whether your brand is mentioned

• Whether your website is cited

• Which competitors appear

• Which third-party sources influence the answer

• Which topics consistently exclude your brand

Run prompts multiple times and use variations. One response is not enough to measure a probabilistic system.

Step 4: Close content and authority gaps

Create or improve the pages needed to answer important subquestions.

At the same time, strengthen your wider digital footprint through customer evidence, expert contributions, digital PR, partnerships and relevant third-party coverage.

Step 5: Measure visibility over time

Track visibility across a stable set of commercially relevant prompts.

A useful AEO dashboard should combine:

• Brand mentions

• Website citations

• Citation frequency

• Competitor visibility

• Sentiment

• Cited pages

• AI referral traffic

• Conversions from AI-referred visitors

The goal is not to win one citation once. It is to increase the probability that your company appears whenever a qualified buyer investigates the problem you solve.

Winning the probability game with Overflow Agency

AI search does not eliminate the foundations of good SEO. It increases the importance of authority, technical accessibility, clear positioning and genuinely useful content.

The major shift is in how those elements work together.

Brands are no longer optimizing only for one keyword and one results page. They are competing across a changing network of prompts, subqueries, retrieved sources and generated answers.

At Overflow Agency, we help B2B companies:

• Map the questions and synthetic searches surrounding their market

• Audit how their brand appears across AI search experiences

• Identify gaps in content, authority and online consensus

• Build technically accessible, topic-rich marketing websites

• Strengthen visibility in both traditional and AI-powered search

The brands most likely to win will not be those that publish the most content. They will be the ones that provide the clearest, most credible and most accessible answers across the buyer’s complete decision journey.

1. Google Search Central; Optimizing your website for generative AI features on Google Search; Read the original source.
2. Nick Haigler; Initial Research: Gemini 3 Query Fan-Outs; Seer Interactive; 21 November 2025; Read the original source.
3. Louise Linehan and Xibeijia Guan; Update: 38% of AI Overview Citations Pull From The Top 10; Ahrefs; 2 March 2026; Read the original source.
4. Ryan Law and Xibeijia Guan; New Study: AI Assistants Prefer to Cite “Fresher” Content (17 Million Citations Analyzed); Ahrefs; 28 July 2025; Read the original source.