Ranking on Google is no longer the only way someone can discover your business online.
Potential customers can now ask ChatGPT which companies they should consider, use Google AI Mode to research a service, ask Gemini to compare suppliers or turn to Perplexity for recommendations.
Instead of receiving a traditional page of search results, they may receive a generated answer containing a handful of brands, sources, links and recommendations.
That creates a new challenge for businesses: are you discoverable when AI becomes part of the search journey?
This is where AI discoverability comes in.
AI discoverability is the ability of an AI-powered search platform or assistant to find, understand and surface information about your business when responding to a relevant question.
Being discoverable does not simply mean allowing an AI crawler to access your website.
A platform also needs to understand what your business does, connect it with the topic being researched, retrieve relevant information and decide whether that information is useful enough to include in its response.
For a business, successful AI discoverability could result in your company being mentioned in an answer, your website being cited as a source, a specific page being linked to or your brand being included among a set of recommendations.
Terms such as AI visibility, LLM visibility, Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO) are increasingly used around the same area. The terminology is still evolving, but the underlying challenge is straightforward: making sure your business can be found and understood as search becomes increasingly AI-assisted.
Traditional search often starts with a relatively short query.
Someone looking for a supplier might search:
commercial roofing Manchester
An AI-assisted search can be much more detailed:
Which commercial roofing contractors around Manchester have experience working on large industrial buildings and offer ongoing maintenance?
Rather than simply returning ten webpages that contain similar keywords, an AI system can research different parts of that question before constructing an answer.
That changes the type of visibility businesses need to think about.
If an AI platform regularly includes three competitors when answering questions about your services but never mentions your business, being number five on Google for one traditional keyword may not tell the whole story.
This does not mean traditional search is disappearing. Google itself describes its generative search experiences as being built on top of its existing Search ranking and quality systems. AI is adding another discovery layer rather than making conventional SEO irrelevant.
For businesses, this means visibility increasingly needs to be considered across both traditional results and AI-generated experiences.
AI discoverability and traditional SEO are closely connected, but they are not exactly the same thing.
| Traditional SEO | AI Discoverability |
|---|---|
| Visibility in ranked search results | Visibility within AI-generated answers |
| Often begins with search queries | Often begins with detailed questions and follow-ups |
| Measured heavily through rankings, impressions and clicks | Can include mentions, citations, recommendations and referrals |
| Primarily webpage focused | Can involve pages, brands, people, products and other entities |
| Usually analysed around Google and Bing | Can include Google AI, ChatGPT, Gemini, Perplexity and Copilot |
| A position can often be tracked for a query | AI responses can vary between prompts and repeated searches |
The important point is that these two areas overlap considerably.
For Google specifically, strong SEO remains one of the foundations of AI discoverability.
Google says its generative search features use its existing Search index and ranking systems. It also uses techniques including retrieval-augmented generation, or RAG, to retrieve relevant webpages and query fan-out, where a model creates several related searches to gather the information needed to answer a broader question.
A good AI SEO strategy therefore should not replace established SEO principles. It should build on them.
There is no single universal process shared by every AI platform.
Google AI Mode does not operate exactly like ChatGPT, and ChatGPT does not necessarily retrieve information in the same way as Perplexity or Gemini.
However, it is useful to think about AI discoverability as a chain.
At Big Fat Digital, we can break this into six stages.
The first question is simple:
Can the platform access your information?
For Google, technical SEO remains fundamental. Google states that a page must be indexed and eligible to appear in normal Google Search with a snippet before it can be eligible for Google’s generative AI search features.
Problems such as noindex directives, incorrect robots.txt rules, broken pages, poor canonicalisation or important content that search engines cannot properly render may prevent the process from getting any further.
This is why website crawling and indexing remain relevant even when the objective is improving AI visibility rather than simply achieving a traditional ranking.
Different platforms also have their own crawlers and discovery mechanisms, which we will look at later.
Being accessible is not the same as being understandable.
An AI system needs enough clear information to determine who your business is and how it relates to the topic being discussed.
Consider whether your website clearly communicates your services, products, locations, expertise and the people or organisation behind the business.
A company that repeatedly describes itself using vague marketing language may be harder to interpret than one with clear service pages, descriptive headings, well-structured content and consistent business information.
Structured data can also form part of a wider SEO strategy by helping search engines interpret eligible information, although it should not be treated as a special AI ranking mechanism.
The objective is to make the relationships between your brand and the topics you want to be known for as unambiguous as possible.
Your own website is only one source of information about your business.
There may also be references to your company on industry publications, news sites, directories, review platforms, partner websites, social profiles and other relevant third-party sources.
This wider digital footprint can help establish the context around a business.
For example, imagine two companies both claiming to be specialists in a particular area.
One has a detailed website but almost no presence elsewhere.
The second also has strong onsite content, but its expertise is supported by relevant coverage, authoritative backlinks, reviews, industry references and consistent information across other websites.
That wider evidence may create a much clearer picture of what the second business represents.
This does not mean businesses should manufacture brand mentions for AI.
Google specifically warns against pursuing artificial mentions simply to manipulate visibility in generative search. Its advice remains focused on high-quality content and avoiding spam rather than trying to manufacture signals.
Digital PR, link building and wider brand authority should therefore be about building genuine third-party recognition rather than creating artificial footprints purely for AI systems.
The next stage is relevance.
Being recognised as a company in a particular industry does not automatically mean your business should appear for every question related to that industry.
The AI system needs information relevant to the specific question being asked.
This is where comprehensive content becomes important.
Imagine someone asks:
Which accountancy software is best for a small construction company with five employees?
Being recognised as an accountancy software provider is only one part of the problem.
The platform may also need information about small businesses, construction, pricing, employee numbers, integrations and the features that differentiate each option.
Google’s description of query fan-out illustrates why this matters. One user prompt can result in multiple related searches being carried out behind the scenes before an answer is assembled.
Websites that provide genuinely useful coverage of the questions surrounding their products and services can therefore provide more opportunities for relevant information to be retrieved.
If the previous stages are successful, your business or content may be surfaced within an AI response.
That can happen in several ways.
An AI system might name your business, cite an article on your website, link to one of your pages or include your company as one option in a broader recommendation.
These outcomes are related, but they are not identical.
A business can be mentioned without receiving a link. A webpage can be cited without the wider brand being explicitly recommended. A company may even appear in a recommendation while the supporting sources come from third-party websites rather than its own domain.
That is why AI visibility should not be measured using one metric alone.
The final stage is understanding what happened.
Did your brand appear?
Which pages were cited?
Which competitors appeared alongside you?
How frequently did it happen?
Did anyone subsequently visit your website?
More importantly, did that visibility contribute to enquiries, sales or other meaningful business outcomes?
The purpose of AI discoverability should not be accumulating mentions for the sake of reporting a larger number.
It should ultimately contribute to getting the business in front of potential customers at relevant points in their research journey.
AI-powered discovery is already spread across multiple platforms.
Google has incorporated AI Overviews and AI Mode into the Search experience. ChatGPT can search the web and include sources within responses. Gemini, Perplexity and Microsoft Copilot offer their own combinations of generated answers, web retrieval and source attribution.
Businesses therefore need to avoid thinking of AI visibility as another single ranking.
There is no universal position one, two or three that carries across every AI system.
A brand might be highly visible in Google AI Mode but rarely appear in ChatGPT. Another might be cited frequently in Perplexity because of its informational content while receiving relatively few direct brand recommendations elsewhere.
Even repeated versions of similar prompts can produce different answers.
Research published by SparkToro in January 2026 found substantial inconsistency when AI systems were repeatedly asked to recommend brands and products. This is one reason visibility monitoring needs to use multiple relevant prompts and repeated observations rather than treating one test as a permanent ranking.
There is no universally published list of “AI ranking factors” that applies across ChatGPT, Google, Gemini, Perplexity and every other platform.
Claims that a particular number of backlinks, schema properties or keyword mentions will make a company rank higher in AI answers should therefore be treated cautiously.
What businesses can control are the underlying conditions that make their information easier to discover and use.
Technical accessibility is one of those conditions. Important content should be crawlable, indexable where appropriate and connected through a logical site structure.
Content quality and relevance are another. Generic summaries offer little additional value when thousands of websites can publish the same information. Google explicitly recommends creating useful, expert-led, non-commodity content for generative Search rather than publishing large quantities of pages aimed at every possible wording of a query.
Entity clarity also matters strategically. Your website should make it obvious who you are, what you offer, where you operate and what subjects your expertise relates to.
Finally, businesses should consider the information that exists about them beyond their own domain. Relevant PR coverage, authoritative references, reviews, backlinks and consistent external profiles can all contribute to a stronger overall digital presence.
There is no button that makes a business appear in ChatGPT or guarantees a recommendation from Google AI Mode.
A sensible approach is to improve the foundations that allow your information to be found, understood and retrieved in the first place.
Check that important pages are accessible and indexable. Resolve crawling problems, incorrect noindex directives, canonicalisation issues and other technical barriers before looking for more advanced AI SEO tactics.
Review AI crawler access. Make sure platforms you want visibility from are not being unintentionally blocked by robots.txt or other site controls.
Make your business proposition clear. Your core pages should clearly explain what you offer, who it is for and, where relevant, where you provide it.
Create genuinely useful supporting content. Answer the questions customers ask while researching, comparing and choosing products or providers, rather than simply creating pages for keyword variations.
Strengthen entity consistency. Keep important information about your organisation, services, people and locations consistent across your site and relevant external profiles.
Improve internal linking. Help search systems and users understand how supporting articles, commercial pages, service categories and related topics connect.
Use appropriate structured data. Continue following normal structured data best practices where relevant, but do not expect special AI schema to produce AI visibility.
Build credible third-party authority. Seek meaningful links, mentions, PR coverage, reviews and industry references rather than artificial signals created purely to influence AI systems.
Monitor meaningful prompts. Test questions actual customers might ask, not simply variations of your business name.
Connect visibility with business performance. Combine AI monitoring with Search Console, analytics and lead data so that visibility is assessed in terms of useful outcomes.
The important principle is that SEO for AI search should improve the quality and clarity of your digital presence, rather than make your website less useful to humans in an attempt to satisfy a machine.
ChatGPT deserves separate consideration because its discovery controls are not identical to Google’s.
OpenAI states that any public website can potentially appear in ChatGPT Search.
For content to be available for summaries, snippets, citations and links, OpenAI recommends ensuring that OAI-SearchBot is not blocked from accessing the relevant content.
It is also important to distinguish OAI-SearchBot from GPTBot.
OAI-SearchBot relates to search discovery, while GPTBot is a separate user agent associated with whether site content may be used for potential model training. OpenAI provides separate controls for the two.
This distinction matters because a business may be comfortable allowing its public content to appear in ChatGPT Search while taking a different position on model training.
Crawler access alone does not guarantee that ChatGPT will cite or recommend a website. It simply ensures that an unnecessary technical restriction is not preventing content from being accessed for search purposes.
There has been considerable discussion around creating special files, markup or content formats specifically for AI search.
For Google, its current guidance is clear.
Google says websites do not need an llms.txt file, special AI text files, special Markdown versions of pages or AI-specific markup to appear in Google Search or its generative features.
It also says there is no special schema.org markup required for generative AI Search.
Google currently ignores llms.txt for Search visibility, meaning adding one neither improves nor harms Google rankings or visibility in its generative search features.
That does not mean llms.txt could never be useful to another service that chooses to support it.
It simply means businesses should not treat it as a universal AI SEO requirement or assume that implementing it will improve visibility across AI platforms.
The same applies to claims that content must be aggressively broken into tiny sections so AI can understand it.
Google explicitly says there is no requirement to “chunk” content into small pieces or rewrite everything in a special format for generative AI.
Good structure still matters, but primarily because it creates clearer and more useful content for people and search systems.
These terms are often grouped together when discussing AI visibility, but they measure different outcomes.
| Outcome | What it means |
|---|---|
| AI mention | The AI names your company, product or brand within its response |
| AI citation | The AI references or links to a particular source used to support its response |
| AI recommendation | The AI actively includes your brand or product among suggested options |
| AI referral | A user clicks from the AI experience through to your website |
A mention is not necessarily a recommendation.
A citation does not necessarily mean the platform prefers your business over competitors.
And a recommendation does not always result in traffic, particularly where the user receives enough information directly within the generated answer.
This is why tracking AI citations alone can provide an incomplete picture.
Businesses should consider the combination of brand presence, source visibility, competitor representation, referral traffic and ultimately commercial outcomes.
The terminology around AI search is not yet standardised, and different tools, agencies and publishers use these terms in different ways.
A useful distinction is to think of AI discoverability as the underlying ability to be found, understood and considered by AI systems.
AI visibility is then the observable result.
For example, a technically accessible and well-understood brand may have strong foundations for discoverability. If that brand then starts appearing frequently across relevant ChatGPT, Gemini and Google AI responses, that produces measurable visibility.
LLM visibility is commonly used more specifically to describe how a brand appears within large language model-based experiences.
In practice, these areas overlap considerably.
What matters more than the terminology is understanding whether your business is appearing when potential customers use AI to research the topics, products and services relevant to you.
Measuring AI search has historically been difficult because businesses could see referrals from some platforms but had little visibility into when their website appeared inside generated answers.
That is beginning to improve.
Google launched dedicated Generative AI performance reports in Search Console on 3 June 2026 and completed the worldwide rollout on 31 August 2026.
The reports allow site owners to see impressions within Google’s generative AI Search and Discover experiences, including AI Overviews and AI Mode. They also provide information about the pages shown, countries, devices and changes over time.
That provides website owners with far better first-party visibility into how their content is appearing across Google’s AI search experiences.
Google is only part of the picture.
Businesses can also monitor relevant prompts across platforms such as ChatGPT, Gemini and Perplexity.
The aim should not be to ask:
Does ChatGPT know who we are?
A more useful test is whether you appear during the types of searches a potential customer could realistically make.
Those prompts might involve provider comparisons, product recommendations, industry questions, problems customers need solving or questions about particular locations and use cases.
Monitoring these over time can help identify where competitors repeatedly appear and where your own brand is missing.
AI share of voice can provide another useful indicator.
Instead of looking at one answer in isolation, it considers how frequently your business appears across a defined collection of commercially relevant prompts compared with competitors.
Because AI responses can vary, the methodology matters. A larger set of meaningful prompts and repeated monitoring is much more useful than treating one response as a fixed ranking.
Some AI platforms can also send identifiable referral traffic.
OpenAI states that ChatGPT referral links automatically include a utm_source=chatgpt.com parameter, allowing publishers to identify traffic arriving from ChatGPT in analytics platforms.
Referral traffic should still be interpreted carefully.
A user may discover a brand through AI and subsequently search for it directly in Google rather than clicking immediately. Others may receive an AI answer without ever visiting a source website.
AI visibility therefore cannot always be measured purely through sessions.
Ultimately, the most valuable measurement is whether increased visibility contributes to business performance.
That may include direct enquiries, calls, sales, branded searches or assisted conversions.
Increasing AI visibility without improving the quality of traffic or enquiries is not a meaningful business objective in itself.
There is rarely one simple explanation.
Sometimes the issue is technical. Important information may be blocked, poorly indexed or difficult for a platform to access.
In other cases, the problem is understanding. Your website may not clearly establish what the business does, who it serves or how its products and services differ.
Content gaps can also be responsible.
A competitor with detailed information addressing the specific questions surrounding a purchase may provide better material for retrieval than a business with a single generic service page.
The wider digital footprint can matter as well. If very little reliable information about a company exists outside its own website, platforms may have less context available when researching that business.
And sometimes your website may be perfectly accessible and relevant but simply not appear in one particular response.
AI outputs are variable. One missing mention should not automatically be treated as evidence that a website is invisible to AI.
The correct approach is to look for patterns across multiple relevant prompts, platforms and dates.
Not exactly, but SEO provides much of the foundation.
Technical SEO helps search systems access and process your content.
Content strategy helps establish relevance across the questions your audience is asking.
On-page SEO and site architecture help clarify relationships between topics and pages.
Link building, digital PR and wider brand activity can strengthen the authority and external evidence surrounding a business.
AI discoverability adds another layer by asking whether those signals translate into visibility within generated search experiences.
For Google specifically, this relationship is particularly close. Google describes optimisation for its generative search features as part of SEO rather than requiring an entirely separate set of tactics.
The wider AI ecosystem makes the picture more complex because businesses also need to consider platforms such as ChatGPT, Gemini and Perplexity.
A comprehensive AI SEO services strategy therefore needs to consider technical accessibility, traditional search visibility, content, entity clarity, authority and ongoing AI visibility monitoring together rather than treating ChatGPT optimisation as an isolated exercise.
AI discoverability is not about building a second website for robots or rewriting every page in a special format for ChatGPT.
It is about ensuring that your business is accessible, understandable, relevant and supported by enough useful information for modern search and AI systems to use confidently.
Traditional SEO remains fundamental, but the way people discover businesses is expanding.
A potential customer might still click a conventional Google result.
They might also ask an AI assistant to compare providers, summarise their options or recommend where to start.
Businesses should therefore begin asking a broader question than simply:
Where do we rank?
They should also be asking:
Are we being discovered wherever our customers are now searching?
At Big Fat Digital, our AI SEO services help businesses assess how they appear across traditional and AI-powered search, identify the technical, content and authority gaps limiting their visibility, and build a strategy designed around how search is evolving.
AI discoverability describes how easily AI-powered search tools and assistants can find, understand and surface information about your business. This could result in your brand being mentioned, your website being cited or your company being recommended when users ask relevant questions.
Start by making sure your public content is accessible and useful. OpenAI recommends allowing OAI-SearchBot if you want site content to be available for summaries, citations and links in ChatGPT Search. Beyond crawler access, your website should clearly explain what your business does and provide relevant information that can answer the types of questions users are asking.
AI visibility can be assessed using a combination of Google Search Console’s generative AI reports, prompt monitoring across AI platforms, citation and mention tracking, competitor comparisons and analytics referral data. No single metric provides a complete picture.
They overlap, but they describe slightly different ideas. Generative Engine Optimisation, or GEO, refers to practices intended to improve visibility within generative search experiences. AI discoverability describes the broader ability of a business or its content to be found, understood and surfaced by AI systems.
Not for Google. Google currently states that llms.txt is not required for its generative search features and does not improve or harm visibility in Google Search. Other platforms may choose to support different standards, so requirements should be reviewed individually rather than treating llms.txt as a universal AI SEO requirement.
No. AI responses are generated dynamically and can vary according to the platform, prompt, context and information available. Businesses can improve the conditions that support discoverability and monitor how visibility changes, but no legitimate agency can guarantee that a particular AI system will consistently recommend a specific company.
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