Search is changing. People are no longer relying solely on traditional search engines to find information, compare options or research suppliers. They are also asking AI tools such as ChatGPT, Perplexity and Gemini to answer questions and point them towards useful sources.
That creates a new consideration for businesses: how easily can your website and brand be understood, referenced and surfaced in AI-generated results?
AI search optimisation focuses on improving the content, structure and technical foundations that help AI-powered search systems understand your business and its expertise. It does not replace traditional SEO. Instead, it builds on the same fundamentals while accounting for how people now search and how AI systems retrieve and present information.
So, What Is AI Search Optimisation?
It is the process of making a website easier for AI-powered search systems to interpret and use when answering relevant queries.
Traditional SEO is largely concerned with helping pages appear prominently in search engine results. AI search adds another layer. Searchers may receive a direct answer generated from information gathered across multiple sources, rather than choosing from a list of blue links.
For businesses, that means visibility is not only about ranking for a keyword. Your content needs to clearly explain what you do, who you serve and how different pieces of information about your business connect.
This makes strong content, clear site structure, technical accessibility and consistent business information important foundations for AI visibility.
How Is AI Search Different From Traditional SEO?
The fundamentals still overlap. Search engines and AI systems both benefit from useful, accessible and well-structured information.
The difference is what happens after a search is made.
Traditional search generally directs users towards relevant webpages. AI-powered search can interpret a question, combine information from multiple sources and produce a conversational response. It may also cite or link to the sources it used.
That means businesses need to think beyond individual rankings. Content should answer genuine questions clearly, demonstrate expertise and provide enough context for systems to understand the subject being discussed.
This is why AI search optimisation should complement, rather than replace, a broader SEO strategy.
What Influences AI Search Visibility?
There is no single optimisation technique that guarantees inclusion in an AI-generated answer. Visibility depends on the quality, relevance and accessibility of the information available to the system.
Several practical foundations can help.
Give Useful, Direct Answers
Content should address the question it is designed to answer without burying the useful information under unnecessary introductions.
Clear definitions, explanations, comparisons and supporting detail give both readers and search systems a stronger understanding of the subject.
Make Content Easy to Navigate
Logical headings, descriptive sections and clear relationships between topics help establish what each page is about.
A well-structured article also makes it easier for readers to find the information they need, which should remain the priority.
Connect Related Information
Your website should make it clear how related topics, services and entities fit together.
For example, a page discussing AI search may naturally connect to relevant SEO, content and technical services. Internal links can reinforce these relationships while helping users move through the site.
Keep Technical Foundations Sound
Useful content still needs to be accessible to search systems.
Technical issues, poor site architecture, inaccessible content and inconsistent information can make it harder for search engines and AI systems to interpret your website. A strong technical SEO strategy provides the foundation for everything else.
LLM SEO: Getting Cited by ChatGPT, Perplexity and Gemini
This refers to optimising content and online information for visibility within systems that use large language models (LLMs).
The objective is not to write content specifically for a machine. It is to make your business information clear, useful and well-supported enough to be considered when an AI system answers a relevant question.
That matters because buyers are already using a wider range of digital channels during the research process. McKinsey’s 2026 Global B2B Pulse Survey found that B2B buyers use an average of ten channels across the purchasing journey, with generative AI increasingly part of supplier discovery and evaluation. McKinsey’s research provides useful context for why businesses need to consider how they appear beyond traditional search results.
For LLM SEO, this means focusing on the information your potential customers actually need. Strong topical coverage, clear explanations, credible sources and consistent business information give AI systems more useful material to work with.
Being cited is not something a business can guarantee through a checklist. The focus should instead be on becoming a useful and credible source for the questions your audience is asking.
Generative Engine Optimisation and Answer Engine Optimisation
You may also come across the terms generative engine optimisation and answer engine optimisation.
Generative engine optimisation (GEO) generally refers to improving a brand’s visibility within AI-generated responses. Answer engine optimisation (AEO) has traditionally focused on helping content provide direct answers to questions, particularly in search features that surface an answer rather than simply a list of results.
The terminology varies across the industry, and the concepts overlap. Both point towards the same broader shift: businesses need content that directly addresses user questions and provides enough context for search and AI systems to understand it.
Rather than treating GEO or AEO as completely separate disciplines, businesses can build them into an existing SEO and content strategy.
How to Build a Strategy for AI Search
A practical approach starts with the website and audience you already have rather than creating an entirely separate AI-focused content programme.
How Do You Measure AI Search Performance?
AI visibility can be harder to measure than a traditional ranking position. There is no single metric that captures every appearance in an AI-generated answer.
Instead, look at several signals together.
Organic Search Performance
Traditional organic performance remains important. Track rankings, impressions, clicks and conversions for the topics supporting your AI search strategy.
AI Mentions and Citations
Search relevant questions across major AI platforms and monitor whether your brand, content or website appears in their responses.
These checks should be repeated over time rather than treated as a one-off test.
AI-Driven Referral Traffic
Referral data can also show whether AI platforms are sending visitors to your website.
Adobe Analytics reported a 693.4% year-over-year increase in generative-AI referral traffic to US retail sites during the 2025 holiday season. That figure applies specifically to the retail sites and period analysed by Adobe, but it illustrates why referral traffic from AI platforms is becoming a useful measurement area.
Branded Search and Conversions
Ultimately, visibility needs to support business outcomes. Look at branded search behaviour, enquiries, leads and conversions alongside visibility metrics.
Common AI Search Optimisation Mistakes
Is AI Search Optimisation Right for Your Business?
AI search optimisation is particularly relevant if your customers research services, products or suppliers online before making a decision.
It can be useful for businesses with established websites, strong areas of expertise and a clear set of questions their audience needs answered.
However, it does not need to become a standalone project. In many cases, the most effective approach is to incorporate AI search considerations into existing SEO, content and technical work.
The starting point should be your audience, your existing visibility and the information your customers are looking for.
FAQs
Optimising for AI-powered search can include content improvements, technical SEO, internal linking, entity and topic relationships, structured information and measurement of visibility across AI-powered search platforms.
It can benefit businesses whose customers use online research to compare products, services or suppliers. The right approach depends on the business, audience and existing search visibility.
SMP looks at a combination of organic search performance, AI mentions and citations, referral traffic, branded searches and business conversions rather than relying on one visibility metric.
It overlaps significantly with traditional SEO but considers how AI-powered systems retrieve, interpret and present information. Strong SEO fundamentals remain an important part of the process.
LLM SEO focuses on making online information useful and understandable to systems powered by large language models, with the aim of improving the likelihood that relevant content or brands are surfaced or cited in AI-generated responses.