AI Search Optimisation: a practical guide for businesses in 2026

The term ‘AI search optimisation’ now covers a wide range of services: traditional SEO, content production and regular monitoring of AI answers to specific questions. What usually connects them is a promise of greater visibility. Visibility alone, however, is not yet a business outcome. The growing number of overlapping terms, including AEO, GEO, LLMO and AI SEO, makes it even harder to understand which activities and outcomes a particular service actually covers.

A page citation, a brand mention, a company recommendation, a website visit, an enquiry and a sale are different outcomes. There is also no single, fixed ‘position in AI’, because answers change depending on the platform, model, question and time of measurement.

Before any work begins, you therefore need to define what success means for the project, which questions and platforms you will use to measure it, and how you will assess its effect on the business. Without this foundation, an increase in almost any chosen metric can be presented as a success, even when the company is not receiving more valuable enquiries or generating more sales.

This guide explains the whole process using platform documentation, research and lessons from our own projects.

AI search optimisation in brief

AI search optimisation increases the likelihood that AI models will find credible information about a company, its expertise, offer and representatives, and then use that information when answering potential customers’ questions.

It is not simply a matter of writing content for AI models. It is the broader process of building digital brand authority: presenting the company, its expertise, offer and experts online in a consistent, credible and verifiable way.

In practice, it covers the technical foundations of a website, the quality and structure of its content, the consistency of information about the company, its credibility beyond its own domain and the regular analysis of results, including website traffic and mentions in AI models.

The most important conclusions for a business are:

1. SEO is the foundation, but it is not the whole of AI search optimisation. A website must be accessible to search engines and systems that use the web, and its content must be well organised. Citations, mentions and recommendations still need to be measured separately.
2. There is no single ‘position in AI’. Results vary between platforms, models, questions and successive measurements.
3. Content should address genuine audience needs. What matters is not length or volume, but a clear, useful and credible answer to questions and concerns that matter to the customer.
4. Information about the company must be unambiguous. An AI model should be able to establish what the company does, whom it helps, who represents it and how all these elements relate to one another.
5. A one-off visibility test tells you very little. Maintain a stable set of questions, expand it periodically and observe results over time.
6. A citation is not the final goal. What matters is whether the company’s presence in AI answers supports business goals such as reaching the right customers, generating enquiries and increasing sales.

Why does AI visibility matter to a business?

AI models can influence a customer’s decision-making process before the customer ever visits a website. People do not always begin by browsing a list of search results. They may turn to their preferred AI assistant, describe their situation, ask for an explanation of the available options, compare possible solutions, narrow down the choices and only then investigate particular companies.

A typical journey might look like this:

problem → question to an AI system → understanding the options → comparing solutions → assessing a company → enquiry or purchase

A brand can influence this decision in several ways: by providing a useful source, appearing as an example of a solution or service, sharing the knowledge of its experts or being recommended directly. Some of this influence happens before the user visits the website and is not visible in standard analytics tools.

This does not mean the end of Google Search, nor does it mean that businesses should move their entire marketing budget into a new visibility channel. Google remains an important source of information, and its generative features operate within the existing search ecosystem. At the same time, ChatGPT, Gemini, Claude, Grok and Perplexity differ in how they access the web, present sources and are used by their audiences.

In a May 2026 Gartner study of 645 B2B buyers, 45% said they had used generative AI during a recent buying process, primarily to gather information about suppliers and products. Buyers consulted seven information sources on average, while 69% preferred to validate AI-generated information later with a sales representative.

This shows that AI systems are becoming one part of the research that precedes the choice of a supplier, but they do not replace the whole buying process. For a business, the important question is what a potential customer learns about it during a conversation with AI before visiting the website or speaking to the sales team.

For which businesses is AI visibility particularly important?

AI search optimisation deserves serious attention when:

• customers research information and compare solutions or suppliers before buying;
• the company’s offer is complex or expensive;
• the sales process is long;
• reputation and trust influence the choice of supplier;
• the company already has a functioning website, content and basic analytics;
• the brand wants to understand how generative systems present it alongside competitors so that it can plan marketing activity accordingly.

AI search optimisation should not be the highest priority when the website does not work properly, the offer is unclear, basic information about the company is missing, nobody measures customer acquisition performance, or the organisation lacks the resources to create its own content and develop its digital presence.

In that situation, AI search optimisation may be a later step rather than the starting point.

The difference between AI search optimisation and traditional SEO

AI search optimisation does not replace SEO, nor does it sit beside it as a completely separate discipline. It relies on many of the same foundations but extends the range of possible outcomes and places where a brand can appear, while requiring a different approach to measurement.

Area	SEO	AI search optimisation
Scope	Technology, content, information architecture, authority and search visibility	Uses the same foundation while extending it to how the brand is presented and how it appears in AI answers
Main outcome	Website visibility in search results	Information used in an answer: a citation, mention, description or recommendation
Unit of visibility	Primarily a page or search result	A page, piece of information, brand, product, person or another source
Role of external sources	They influence authority, reputation and organic visibility, among other factors	They can also directly provide information that a system uses to describe or recommend a brand
Measurement	Indexing, rankings, impressions, clicks, traffic and conversions	Citations, mentions, recommendations, brand portrayal, AI traffic and conversions
Stability and measurement	Variability is measured within a mature ecosystem of tools and metrics	Significant variability between answers, models and platforms for the same question

Google explains in its official documentation that AI Overviews and AI Mode use Google Search’s core ranking systems and information available in the search index. Depending on the question, they may also use other Google data sources.

Traditional SEO therefore remains the foundation of visibility in generative search features. This includes:

• making it possible for the appropriate crawlers to access the website;
• ensuring that pages are indexable and use the correct canonical versions;
• making the main content accessible, particularly on websites built with JavaScript frameworks;
• creating a logical information architecture and internal linking structure;
• maintaining good website performance;
• providing unambiguous information about the organisation, its offer and representatives, and showing when the material was last updated;
• creating content that addresses genuine user intent.

Organic search data shows whether pages are being found, which queries they appear for and whether users continue to the website. It does not, however, prove that citations, mentions or recommendations have increased in AI systems. That area requires separate measurement.

How do AI systems find and use information?

The mechanisms differ between platforms and are not fully public. Based on the available documentation, however, we can identify five stages that a company can influence:

1. access to information and discovery of a source;
2. understanding the company, content and relationships;
3. matching information to the user’s question;
4. assessing credibility and corroborating information;
5. using the information in an answer.

1. Access to information and discovery of a source

Before an AI model can use a piece of information, it must find an appropriate source and be able to read it. Search methods differ between platforms: a system may use its own search mechanism, a search engine index, an external provider or several sources at once.

Google explains that AI Overviews and AI Mode may use a query fan-out mechanism, which means running several related searches across different parts of a question and different data sources.

In its documentation on how ChatGPT Search works, OpenAI explains that ChatGPT may rewrite a user’s question as one or more precise queries before it begins searching.

Finding a potential source is not enough if the system cannot access it. It is therefore essential to check whether search engine and AI crawlers are blocked. Crawlers that collect data for model training should be distinguished from those that retrieve information in response to a user’s request.

2. Understanding the company, content and relationships

Reaching a page is not enough. Information should be presented in a way that makes it possible to establish clearly whom or what it concerns and how the different elements relate to one another.

Clear naming, a logical content structure and consistent information across the website all help.

According to Google’s structured data documentation, structured data can also provide a search engine with explicit information about the meaning and type of content, for example by identifying an author as a person or organisation. Google notes, however, that AI Overviews and AI Mode do not require any additional structured data or special schema.org markup.

3. Matching the question and the user’s intent

Finding and correctly interpreting information does not mean it will be useful in a particular answer. The model must find content that addresses the problem, situation or decision described by the user.

Alongside standard informational pages describing the company and its offer, it is therefore worth publishing company knowledge that helps audiences in different situations and answers their questions. Content should provide useful, credible information based on sources such as the company’s experience, proprietary data, processes, examples and the knowledge of its experts and representatives.

4. Credibility and corroboration

A company’s own website is only one source of information about it. Search systems may also use trade publications, expert profiles, partner websites, directories, reviews and other independent sources.

We do not know how every platform verifies information about a brand. We do know, however, that search systems use signals of quality, credibility and authority. Google describes, among other things, mechanisms that analyse relationships between pages and systems designed to identify reliable and authoritative information.

For a business, this means its own claim to expertise should not be the only evidence of its position. If a brand specialises in a particular field, it is worth ensuring that its activity, experience and representatives are also presented consistently in credible external sources.

This is not about manufacturing mentions. It is more important for independent sources to corroborate basic information about the company, its specialism, experience and the people who represent it.

5. Using information in an answer

After finding and processing the information, a system may:

• cite the page as a source;
• name the company or an expert;
• use the company as an example;
• include the brand in a comparison;
• recommend it as one possible solution;
• use the information without visibly identifying the source;
• leave it out of the answer.

In an Ahrefs study of AI Overviews, an average of 45.5% of cited URLs changed between successive observations, even though the overall meaning of the generated answers remained relatively similar.

In this environment, it is difficult to secure a single, stable position. Work should instead increase the likelihood that a system will find the right information, understand the brand correctly and use that information when answering questions that matter to potential customers.

The dotGrow BTK model: brand, technology and knowledge

At dotGrow, we run AI search optimisation projects across three parallel areas: brand, technology and knowledge.

The BTK model provides a framework for our work. It combines activity related to the brand’s presence, the technical accessibility of information and the knowledge that the company publishes and shares with potential customers. Together, these three areas build digital brand authority: they create a credible picture of the brand, substantiate its expertise and make it easier for the right information to be found and used.

B — Brand

We organise the way the brand, offer and experts are presented. We then work to make this information consistent not only on the company’s own website but also in external sources. This area includes reputation, expert presence, publications, partnerships and PR.

T — Technology

We review and improve the technical conditions in which information is discovered and processed. This includes the SEO foundation, website structure, access for the appropriate crawlers, linking, semantics and structured data where justified.

K — Knowledge

We uncover the knowledge that already exists within the company and turn it into information that is useful to potential customers. We work with experts’ experience, data, processes, customer cases and questions raised during the buying process rather than simply producing more generic content.

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A brand does not become an authority by repeating information that is already widely available. Its most valuable knowledge often comes from the experience of the people behind the company: customer conversations, decisions, processes, mistakes and the lessons learned from them. That knowledge needs to be uncovered, organised and shared in a form that helps potential customers. This is what builds lasting, distinctive authority.

The AI search optimisation process in eight steps

AI search optimisation should not be a collection of disconnected activities. It is better managed as an organised process: from defining the objective and starting point, through implementation, to regular measurement and adjustment.

1. Define business goals and customer questions

The process should begin with the situations in which a potential customer seeks information before making a decision. The questions that arise along this journey should determine where the company wants to improve its visibility and which outcome that visibility should support.

How: collect questions from sales conversations, forms, customer service interactions, search data and the knowledge of people responsible for the offer. Select the 10–20 most important questions and determine which customer decision or next step each one could support.

2. Record a baseline

Without a point of reference, it is difficult to assess later whether the work has genuinely changed the company’s visibility. The baseline should show the situation in traditional search and AI system answers separately.

How: prepare a repeatable set of tests and record the date, platform, mode, question, answer, cited URLs, named brands and type of exposure. At the same time, record the most important organic visibility data.

3. Review the technical SEO foundation

Information can be used only when the most important pages are accessible, work correctly and can be found by search engines and systems that use the web.

How: carry out an audit covering indexing, crawler rules, the sitemap, canonical versions, redirects, website architecture, internal linking, performance and structured data. Where necessary, we recommend rebuilding the website or ecommerce website so that its architecture, performance and technical foundation support visibility in Google and AI systems.

4. Organise information about the brand, offer and representatives

Core information about the company should be clear and consistent wherever a potential customer or AI system finds it. This includes the company’s specialisms, offer, representatives and their experience.

How: prepare a reference document that describes the company, offer, specialisms and representatives. Use it as a point of reference for the website, expert profiles, social media and materials published outside the company’s own domain.

5. Map intent across the decision journey

A potential customer needs different information while identifying a problem, exploring available solutions, comparing options and choosing a supplier. The company’s body of knowledge should meet these different needs.

How: assign the most important customer questions to the following stages:

identifying the problem → exploring solutions → comparing options → choosing a supplier

Use this map to identify which needs are already well served and where useful information is still missing.

6. Publish and develop knowledge that meets customer needs

Publishing information alone does not create visibility. A company should develop a body of knowledge that genuinely helps potential customers and offers more than a repetition of generally available information.

How: for each important subject, define the audience’s question, the answer they need and the evidence that can support it. Use your own experience, data, processes, examples, case studies and expert knowledge.

7. Build credibility beyond your own domain

A company’s authority should not depend solely on what it says about itself. Credible external sources that contain information about the brand, its experts, experience and specialism also matter.

How: identify channels and sources relevant to your industry, such as media outlets, trade publications, partner websites, organisations, expert profiles and appropriate directories. Make sure the brand’s presence there is credible and consistent with its positioning.

8. Measure outcomes and adjust your approach

AI search optimisation is an ongoing process. Successive measurements should show not only changes in visibility, but also whether that visibility leads to valuable user behaviour and business outcomes.

How: establish a fixed measurement schedule, compare results with the baseline and monitor citations, mentions, recommendations, traffic, conversions and the quality of enquiries. Use this evidence to decide which activities to expand, change or stop.

How should you measure AI visibility and optimisation outcomes?

Measurement should cover four distinct layers: organic visibility, presence in AI answers, user behaviour and business outcomes.

The most important requirement is to maintain the same methodology across successive periods. Without it, it is difficult to establish whether a genuine change has occurred.

1. Organic visibility

In Google Search Console or Bing Webmaster Tools, record:

• the indexing status of the most important pages;
• the queries for which the website appears;
• the number of impressions and clicks;
• average positions for the most important query groups;
• the pages with the greatest organic visibility;
• impressions in the AI generative performance report, covering AI Overviews and AI Mode, if this report is available in Google Search Console;
• the AI Performance section in Bing Webmaster Tools.

This data shows whether the website is being discovered and how its visibility is changing across Google’s ecosystem.

2. Visibility in AI answers

Prepare a set of questions that reflect genuine customer situations, including identifying a problem, looking for a solution, comparing options and choosing a supplier.

Test the same set on platforms that matter to your market and its customers, such as ChatGPT, Gemini, Perplexity, Claude and Grok. Where possible, use new sessions without conversation history, memory or personalisation.

Ask each question more than once because sources, named brands and recommendations may differ between answers.

For each test, record:

• the platform and mode;
• the exact question;
• the date;
• cited URLs and domains;
• named brands;
• how the company is presented;
• the type of appearance;
• any errors concerning the brand.

Citations, mentions and recommendations: what is the difference?

A company appearing in an AI answer can mean different things. Measurement should therefore distinguish between three main outcomes: a citation, a mention and a recommendation.

Outcome	What it means	What it does not mean
Citation	The answer contains a link or identifies a specific source	The brand is named in the answer itself
Mention	The model names the company, product or expert	The model includes a link, evaluates the company positively or recommends it
Recommendation	The company appears among the suggested solutions	The user will visit the website or choose the offer

A model may also use information without showing its source. Because this type of use is difficult to observe reliably, we do not treat it as a standalone metric.

The distinction between these three outcomes has practical importance. In a June 2026 Semrush study covering 3,981 appearances of domains in answers to 115 questions across 14 countries, 61.7% were citations that did not name the brand in the answer itself.

Citations and mentions should therefore be measured separately.

3. Traffic and user behaviour

In Google Analytics or a similar analytics tool, monitor sessions, key events and conversions from both organic traffic and recognised AI assistants.

According to the GA4 ‘AI Assistant’ channel documentation, since May 2026 Google Analytics has automatically classified recognised visits from services including ChatGPT, Gemini and Claude under this separate channel.

This channel does not include AI Overviews or AI Mode in Google Search, so Analytics and Search Console data should not be treated as interchangeable.

Do not analyse the number of sessions alone. Also consider:

• landing pages;
• engagement;
• completed conversions;
• the quality of acquired leads.

4. Business outcomes

The final question is whether increasing visibility leads to valuable enquiries, qualified leads, sales and revenue.

Not every layer has to grow at the same time. Greater visibility in AI answers may emerge before traffic or sales increase.

Low traffic from AI assistants does not necessarily mean low business value. In a June 2025 analysis of its own website, Ahrefs found that visitors acquired from AI systems represented 0.5% of all visitors but accounted for 12.1% of sign-ups during the 30-day period studied.

This is an example from one company, not a universal conversion rate. It demonstrates, however, why the number of visits alone is not an adequate measure of performance.

A company should therefore measure form submissions, telephone calls and other key conversions correctly, and be able to connect the resulting data with sales.

What is not an effective AI search optimisation strategy?

A single technical or editorial tactic cannot replace a coherent system for building visibility and authority in the eyes of artificial intelligence.

Take particular care with the following ideas:

• An llms.txt file as a guarantee of AI visibility. Although this file can be useful for human-directed AI agents, Google’s documentation on llms.txt states that Google does not use it for its generative search features. Other services may read it, but there is no basis for attributing greater visibility to the mere existence of the file.
• Structured data as a magic factor. It helps describe information and can support traditional rich results, but it does not guarantee a citation.
• Mass-produced question-and-answer sections. Repeating similar content without adding value does not solve the audience’s problem and may reduce the website’s overall quality.
• Content length and volume as goals in themselves. A system may use either a short, precise answer or an extensive resource. The scope of the material should reflect the user’s intent.
• Manufactured mentions. Inconsistent and unnatural publications on third-party platforms do not build genuine credibility.
• Guarantees of citations or recommendations. The variability of answers and the lack of access to the platforms’ complete logic make an honest guarantee impossible.
• Repackaging standard SEO under a new AI label. A project limited to keywords and title changes does not address the consistency of brand information, the measurement of AI answers or their influence on customer decisions.

What have our projects taught us?

The following observations come from dotGrow’s work on clients’ visibility in AI systems. They show what worked in particular projects, where our initial assumptions needed to change and what we now pay the closest attention to.

We label each conclusion according to the type of evidence available.

Content volume alone does not secure citations

Original hypothesis: very extensive and comprehensive resources would increase website authority and citation frequency.

Measurement finding: in the projects analysed, visitors from AI systems landed on both very long and shorter pieces of content.

Interpretation: we do not treat length as an independent criterion for citation. Our priorities are a specific intent, the quality of the answer, the accessibility of information and the credibility of the source. We have no basis for claiming that thousands of additional words produce a proportionately greater effect.

Organising existing content can be a better first step than writing something new

Observation: in one project, the number of visits from AI systems increased during the same period in which we reorganised existing articles. We added a table of contents, improved the section structure, expanded the substance of the material and revised the headings.

Hypothesis: the timing does not prove that the structural changes caused the increase in traffic. It does show, however, that before commissioning more content, it is worth checking whether existing knowledge primarily needs to be organised more effectively.

Unambiguous information can reduce errors in how a brand is described

Observation: in another project, a brand and the owner-experts associated with it were initially presented inconsistently and ambiguously by AI systems. After separating information about the brand and the individuals into dedicated pages, clarifying their roles and specifying the relationships between them, AI answers began to distinguish the organisation from each person more clearly and present each individual in line with the intended positioning of their personal brand.

Hypothesis: systems may find it easier to build a coherent picture of a company when every important person and organisation has its own unambiguous source of information and the relationships between them are confirmed consistently.

AI models speak in the voice of third parties when a brand has no voice of its own

Observation: before we began working together, information about the brand in AI models came mainly from publicly available business directories, even though the brand had its own website. This information was very general and did not allow the brand to present its specialism and credibility properly.

Measurement finding: after we collected and organised information about the company, its profile, representatives and services, and rebuilt the website content, AI models began describing the brand using its own materials and citing the relevant pages.

Interpretation: simply having a website is not enough if it does not contain clear, well-organised information about the brand. When that information is missing, systems may rely on more accessible external sources to describe it.

dotGrow’s most important operational conclusion

AI search optimisation requires simultaneous work on the technical accessibility of a website, unambiguous information about the brand and its representatives, content that addresses important customer intent, and accurate measurement of outcomes.

This increases the likelihood that the company will appear in AI answers, gives it greater control over the information about it available online and makes it possible to assess whether rising visibility translates into business value.

AEO, GEO, LLMO and AI SEO: what do these terms mean?

The terminology in this field is not yet used consistently. From a business perspective, defining the scope of work, method of measurement and expected outcome matters more than the abbreviation itself.

Term	Common meaning	Practical significance
AEO	answer engine optimisation	preparing information so that it can answer users’ questions effectively
GEO	generative engine optimisation	increasing the visibility of sources and information in answers produced by generative models
LLMO	large language model optimisation	a broad term for work related to how language models understand and use information
AI SEO	SEO extended to AI answer surfaces	combining traditional discoverability with content preparation and measurement of visibility in AI systems
AI search optimisation	an accessible business term	the complete range of activities related to finding, interpreting and presenting a company in AI answers

At dotGrow, we use the term ‘AI search optimisation’ because it is clear to people working in business. We are preparing a detailed comparison of these terms as a separate article.

Frequently asked questions

Will AI search optimisation replace SEO?

No. Systems that use search still need technically accessible, discoverable and relevant sources. SEO creates that foundation, while AI search optimisation extends it to include information consistency, the way content is used and separate measurement of AI answers.

Can AI citations be guaranteed?

No. Sources change between platforms, models and successive answers. AI search optimisation aims to increase the likelihood that a particular model will identify a company or its content, but no one can honestly guarantee a particular citation or recommendation.

How long does it take to see results?

AI search optimisation should be treated as a long-distance effort rather than a sprint, much like SEO. Building brand authority in both AI systems and Google takes time and consistent work.

Does every company need AI search optimisation?

Not to the same extent. It becomes a greater priority when customers look for explanations, comparisons and recommendations before buying. If a company does not have a clear offer, consistent information about itself or basic analytics on its website, it is usually better to solve those problems first.

Does a website need to be rebuilt for AI search optimisation?

Not always. Improving the website structure, content and the way information about the company is presented is often enough. Building a new website only makes sense when the existing technology or architecture clearly makes that information difficult to find, understand or update.

Does AI-generated content help with AI search optimisation?

Not by itself. AI search optimisation also involves building authority, and articles produced solely with AI tools rarely add distinctive value because they rely on information that is already available online.

AI tools can support source analysis and editing, but responsibility for facts, the brand’s distinctive perspective and the quality of the material remains with people. Publishing derivative content at scale does not build authority or a competitive advantage.

How can I check whether a model recommends my company?

Do not rely on a single question such as ‘What do you know about company X?’

Prepare a set of questions that reflects genuine customer situations, run tests on several platforms, record sources, mentions, recommendations and errors, and then repeat the study.

The result describes the behaviour of a particular system at a particular time, not a permanent state of its knowledge.

Does AI search optimisation work in the same way across ChatGPT, Gemini, Perplexity, Claude and Grok?

No. Platforms differ in how they access information, which sources they use and how they produce answers.

The same company may be cited or recommended by one model and completely omitted by another. Visibility should therefore be measured separately for the platforms that matter in a particular country and industry.

Does greater visibility in Google mean greater visibility in AI?

Not necessarily. Impressions, rankings and clicks help assess the SEO foundation, but they do not prove that a company has been cited, mentioned or recommended in ChatGPT, Gemini, Perplexity or Google’s generative search features.

Conclusion: AI visibility is built systematically

AI search optimisation is not a single website change or a matter of publishing more articles. It requires simultaneous work across three areas: the technical accessibility of information, an unambiguous picture of the brand and knowledge that answers genuine customer questions.

A baseline should be the starting point. Only by comparing answers to a stable set of questions, search data and business outcomes can a company assess whether its visibility results not only in citations and mentions, but also in valuable leads and greater sales.

The main goal is not a ‘number one position in AI’, because no such stable position exists. The goal is to increase the likelihood that systems will find credible information about the company, interpret it correctly and use it when it matters to a customer’s decision.

If you want to understand how AI systems present your brand today and which actions to prioritise, explore dotGrow’s AI Search Optimisation support.