AI Search
Knowledge Base

Answer Engine Optimisation (AEO) is the term commonly used for the practice of structuring a website's content, data and technical signals so that search engines and AI-powered systems can more accurately understand and surface its information in response to relevant queries. Where traditional SEO focuses on ranking a page in a list of links, AEO focuses on being correctly interpreted, summarised, and cited within a generated answer. It draws on many of the same foundations as SEO (structure, clarity, authority) but places more weight on how information is organised, disambiguated, and machine-readable.

An answer engine is a system that responds to a user's question with a direct, synthesised answer rather than simply presenting a list of links. Tools and features such as ChatGPT Search, Google's AI Overviews, Perplexity and Microsoft Copilot can retrieve and synthesise information from web sources to produce an answer, often with links or citations to supporting material. This shifts the goal for businesses from "rank on page one" to "be among the sources the system uses or cites."

Traditional SEO optimises for ranking algorithms that sort web pages by relevance and authority, with the end goal of a click. AEO optimises for language models and retrieval systems that read, interpret, and often summarise or paraphrase content, with the end goal of being cited, quoted, or recommended, sometimes without a click at all. In practice, AEO relies on many of the same fundamentals as good SEO, clear structure, useful content and technical accessibility, while placing additional emphasis on making information about a business and its relationships clear and unambiguous. The exact signals and weighting used by different AI platforms aren't universally published.

AI search is increasingly becoming part of the buying journey rather than simply an alternative way to find information. In Semrush's 2026 survey of 622 US B2B professionals, with findings based on the 519 respondents who use AI for work, 66% said they regularly use AI to research products, vendors, or solutions, with a further 29% doing so occasionally. 92% said AI had shaped their vendor shortlist, 83% said it influenced their final vendor decision, and 89% expect to rely on AI more for work decisions going forward. This means businesses increasingly need to consider not only whether they rank in conventional search, but whether AI systems can accurately discover, understand, and describe them.

The main platforms worth considering are ChatGPT, Google's AI Overviews and AI Mode, Perplexity, Microsoft Copilot, and Google's Gemini. In the same Semrush 2026 survey, the three most-used platforms for B2B product research were ChatGPT (71%), Gemini (61%), and Microsoft Copilot (45%). Rather than optimising for any one platform in isolation, the most effective approach is strengthening the same underlying fundamentals, clear site structure, crawlable and indexable pages, accurate entity information, and genuinely useful content, that Google confirms its AI-powered search features continue to rely on, without requiring special AI-specific markup or files.

Unlikely in the near term, but the two are converging. Google itself has integrated AI-generated answers directly into its search results through AI Overviews and AI Mode, rather than treating AI search as a separate competing product. This is borne out in buyer behaviour too: in Semrush's 2026 survey, 75% of respondents still use a search engine as part of their vendor research, and when asked how they combine the two, 41% said they start with AI and then validate through search, 35% start with search and use AI for synthesis or comparison, and 20% move between both. For most businesses, the realistic picture is that traditional search and AI-generated answers will coexist and increasingly blend together, meaning visibility strategies need to account for both rather than choosing one over the other.

When an AI platform generates a web-grounded answer, it typically involves some form of search or retrieval step, querying an index of web content relevant to the question and drawing on a shortlist of sources to inform the response. Not every AI-generated answer works this way; some responses draw more on the underlying model's training rather than live retrieval. Clear, well-organised content that directly addresses a specific topic tends to be easier for these systems to navigate and interpret, but Google has stated there's no need to restructure content in a special way, or break it into small fragments, specifically to be selected by generative AI features.

OpenAI confirms that ChatGPT Search can retrieve current information from the web, rewrite a request into targeted search queries and return linked sources. OpenAI also states that ranking in ChatGPT Search is based on multiple factors intended to surface reliable, relevant information, but it doesn't publish the complete factors or their weighting. Outside documented Search behaviour, the process determining whether a particular business is mentioned is less transparent, so there is no published formula businesses can follow to guarantee inclusion.

Entity SEO is the term commonly used for work that helps search engines clearly identify a business, person, place, product or other distinct concept and understand its relationships. The aim is identity clarity rather than mechanical uniformity: legitimate legal, trading or abbreviated names can differ, provided those relationships are clear and information isn't genuinely contradictory or outdated.

Structured data, commonly implemented using the Schema.org vocabulary, is a standardised way of labelling the content on a webpage so that machines — not just human readers — can more reliably interpret what it represents. For example, structured data can explicitly mark up a business's name, address, opening hours, or services, rather than leaving search engines to infer this from unstructured text. It doesn't guarantee visibility or change how a page looks to a visitor, but it gives search engines explicit, machine-readable information to work from where relevant features support it.

Schema markup gives search engines explicit, machine-readable information about the entities and content on a page, which can reduce ambiguity and support eligible search features. However, schema is not a direct route into AI-generated answers and does not guarantee that a page will be cited or recommended. Google states that its AI search experiences continue to rely on its core Search systems and do not require special AI-specific structured data. For businesses, schema is best treated as one part of a wider technical and entity-clarity strategy rather than an AEO shortcut.

Yes. A page can perform well in conventional Google Search without being selected or cited within an AI-generated answer. Google states that its generative AI Search features use its core Search ranking systems, but a conventional ranking position does not guarantee that a page will be used within a particular generated response.

Being indexed simply means a search engine has crawled a page and stored it in its database, making it technically eligible to appear in results. Being understood means the search engine or AI system has correctly interpreted what the page is about, who it belongs to, and how it relates to other information — accurately enough to confidently use it in an answer or recommendation. A page can be fully indexed and still poorly understood if its content is vague, disorganised, or lacks clear signals about what it actually represents.

Improving AI visibility generally involves a combination of: ensuring the site is fully crawlable and technically accessible, structuring content clearly around specific topics and questions, adding accurate structured data, ensuring consistent and accurate information about the business across the web, and producing genuinely useful content that directly answers the questions your audience is asking. There's no single fix, it's a combination of technical accessibility, content clarity, accurate business information and genuinely useful content working together, rather than one isolated change.

Clear, well-organised content — with sensible headings and concise, unambiguous explanations — tends to be easier for AI systems to interpret and reuse accurately than vague or meandering marketing copy. That said, Google is explicit that there's no special format or structure that guarantees a page will be selected for a generative AI feature; the same fundamentals that make content genuinely useful to a human reader are the ones most likely to serve it well with AI systems too.

There's no universal timeline. For AI features that generate answers using live web retrieval, changes to a website can potentially begin influencing results once the page or index is recrawled, similar to how search engines pick up content changes over time. Improvements that depend on a model's underlying training data are a separate matter, and refresh cycles for that are generally not made public by AI providers, so timescales there are largely opaque.

AI visibility is best assessed through patterns rather than a single response. Google and Bing now provide partial first-party reporting for their AI experiences, while ChatGPT Search shows sources used within individual search-grounded answers. Referral traffic from identifiable AI sources can provide one downstream signal; changes in branded search, direct traffic or conversions can also add context, but shouldn't automatically be attributed to AI visibility because many other factors can influence them.

There are several common factors that can affect this: the site may not be well-indexed or easily crawlable, business information may be inconsistent or outdated across different platforms and directories, or on-page content may not clearly and specifically address the kinds of questions people are asking. Being technically eligible to appear does not guarantee selection. The exact weighting AI systems place on these factors isn't publicly disclosed.

Common mistakes include: technical barriers that prevent crawling or indexing, vague or generic content that doesn't clearly answer specific questions, inconsistent or outdated business information across different platforms and directories, and treating AI visibility as a one-off technical fix rather than an ongoing practice.

Yes. Microsoft confirms that Microsoft 365 Copilot Chat and agents can generate a web query and send it to Bing, meaning Bing-indexed content can be used to help ground certain Copilot responses. A site that's poorly indexed in Bing, even if it performs well in Google, may be missing from part of the AI-driven search landscape as a result. Ensuring a site is properly indexed and accessible in Bing is a part of AI search visibility that's easy to overlook.

Google's Knowledge Graph is a large structured database of entities — people, places, organisations, and concepts — and the facts and relationships between them, which Google uses to power features like knowledge panels and to better understand queries and content across the web. Being clearly and accurately represented within Google's entity ecosystem can help Google understand who or what a business is and how it relates to relevant people, places, services and concepts, supporting entity clarity across Search.

Backlinks remain relevant to conventional Google Search. Google says its generative AI Search features use its core Search ranking systems, so link-related signals may matter indirectly where those systems influence retrieval. However, Google doesn't publish a separate rule stating that backlinks themselves increase the likelihood of being cited or recommended in an AI-generated answer, and equivalent backlink weighting isn't documented across AI platforms generally.

There is no published universal "trust algorithm" across AI search. Google documents the use of its core Search ranking and quality systems for generative AI Search, while OpenAI says ChatGPT Search ranking uses multiple factors designed to surface reliable, relevant information. The complete weighting and source-selection logic remains proprietary, so claims about specific universal trust factors such as reputation or corroboration should be treated as inference unless the relevant platform documents them.

Yes, particularly where geography affects which businesses are relevant. OpenAI explicitly documents using location information when ChatGPT Search reformulates local queries, and can use optional precise device location for local recommendations. Accurate information about where a business operates therefore matters, although no major provider publishes a complete universal formula for how local businesses are ultimately selected or recommended.