What Is AI Search and How Does It Work?

Overview

The principles behind AI Search

The technology behind AI Search is complex, but the underlying idea is straightforward.

Rather than relying primarily on keyword matching, AI Search attempts to understand what someone is asking, retrieve information that answers the question and present a clear, useful response.

In simple terms:

• Traditional search helps you find pages.
• AI Search helps you find and understand information.

AI Search at a glance:

What it does

Uses artificial intelligence to understand questions and generate useful answers.

Where it appears

Google AI Overviews, ChatGPT, Perplexity, Microsoft Copilot and other AI-powered search and assistant tools.

Key technologies

Semantic search, retrieval, generative AI, large language models and structured data.

Still relies on websites?

Yes. AI Search depends on reliable information published across the web.

Does it replace SEO?

No. It changes how search works, but many SEO fundamentals remain important.

How is AI Search different from traditional search?

Traditional search engines help people discover webpages. AI Search helps people find and understand information.

That doesn’t mean traditional search has disappeared. In reality, the two approaches now work alongside one another. Google, for example, may display an AI-generated summary at the top of the page, followed by traditional search results underneath.

Although the experience is changing, both approaches still rely on the web. The main difference is how information is presented and how people interact with it.

Where do you encounter AI Search?

AI Search isn’t a single tool or product. It’s a way of finding, understanding and presenting information that now appears across a growing range of platforms.

Each platform has its own approach, but they all aim to make finding information quicker and more natural.

How does AI Search work?

Although every AI Search platform uses different technology, most follow a similar process for answering questions.

In simple terms

The process usually follows these steps:

• A person asks a question.
• AI analyses the intent behind the question.
• Relevant information is retrieved.
• AI generates a response.
• Sources may be cited.

How does AI know which information to trust?

AI Search systems cannot independently verify whether every statement on the web is true. Instead, they evaluate a range of signals to identify the information that appears most reliable, authoritative and relevant to the question someone has asked.

These signals vary between platforms and are not always public, but they often relate to the quality, clarity and consistency of the information available.

Examples include:

• Clear authorship
• Recognised organisations or subject experts
• Consistent information across multiple sources
• High-quality, well-written content
• Well-organised website structure
• Technical accessibility
• Structured data
• References and citations from trusted websites

Whether AI references a page depends on many different signals rather than a single ranking factor. Together, those signals help AI Search identify sources that appear reliable, trustworthy and relevant to the question.

Why doesn’t AI Search always get it right?

AI Search isn’t perfect. Even when information is retrieved from reliable sources, it can occasionally produce inaccurate or incomplete responses.

• Different sources provide conflicting information
• Reliable sources are limited
• Context is misunderstood
• Older information is retrieved
• AI incorrectly combines information from different sources

For this reason, important decisions should still be verified using trusted sources.

Publishing clear, accurate and regularly maintained information helps reduce ambiguity and makes it easier for AI systems to retrieve reliable answers.

What is semantic search?

One of the biggest differences between traditional keyword-based search and AI Search is the move from matching words to understanding meaning.

Semantic search moves beyond simple keyword matching. It looks at the concepts, relationships and intent behind a question to better understand what someone is trying to find. For example, instead of asking, “Do these words match?”, semantic search asks, “What is this person really trying to find?”

For example, the following topics are closely related:

• Accessible web design
• WCAG compliance
• Inclusive websites
• Digital accessibility

Although each phrase uses different terminology, they all describe closely connected concepts. AI Search can recognise those relationships and understand that someone searching for one topic may also find the others relevant.

This means organisations benefit from writing naturally, covering a topic thoroughly and using clear, descriptive language rather than repeating the same keyword throughout a page.

This isn’t an entirely new idea. Google has been evolving beyond simple keyword matching for many years. The Hummingbird update in 2013 marked a significant move towards interpreting what people were really searching for, while later advances such as BERT (2019) and MUM (2021) further improved Google’s understanding of language, context and relationships between concepts.

AI Search builds on those foundations, placing even greater emphasis on understanding intent, context and meaning. Rather than looking only at the words someone uses, it aims to understand the question they’re really asking.

What is structured data and why does it matter?

Search engines and AI systems don’t just read the visible content on a page. Structured data provides additional information that helps them understand what that content represents.

It is usually implemented using the Schema.org vocabulary and added to webpages as machine-readable code that isn’t visible to visitors but can be interpreted by search engines and AI systems.

Structured data allows websites to identify and label information such as:

• Organisations
• People
• Services
• Events
• Articles
• Products
• FAQs

Rather than simply reading the words on a page, structured data helps machines understand what those words represent. For example, it can identify whether a name refers to a person or an organisation, whether a page describes a service or an article, or whether a date relates to an event.

This additional context helps search engines and AI systems understand webpages and the relationships between organisations, people, services and topics more accurately.

Structured data does not tell AI what to say, nor does it guarantee inclusion in AI-generated answers. Instead, it is one of many signals used alongside well-written content, technical quality and other indicators of trust to help machines interpret information more accurately.

Think of structured data as clear labelling rather than hidden instructions. It helps machines recognise what information represents and how different pieces of content relate to one another, making webpages easier for search engines and AI systems to understand.

Why do websites still matter?

Despite the growth of AI Search, websites remain the foundation of the web.

AI-generated answers depend on information published by businesses, public sector organisations, charities, publishers and subject experts. Without reliable websites, AI would have little trustworthy information to retrieve, reference or recommend.

AI Search isn’t replacing websites. Instead, it’s changing how people discover and use them. Some people will still click through to learn more, while others may first encounter an organisation because its expertise has contributed to an AI-generated answer.

In some cases, people may find the information they need without visiting the original website. This is often referred to as zero-click search. While it can reduce website visits for some searches, organisations can still benefit through increased visibility, brand awareness and citations in AI-generated responses.

For many organisations, success is no longer measured solely by website visits. It’s increasingly about being recognised as a trusted source of information when people ask relevant questions.

Does AI Search replace SEO?

No. Many of the principles that support traditional SEO also support AI Search.

Clear site structure, useful content, strong technical foundations, accessibility and well-organised information all help search engines and AI systems understand what a website offers.

AI Search doesn’t replace these fundamentals. Instead, it builds on them by placing greater emphasis on understanding context, answering questions clearly and recognising expertise.

For organisations investing in SEO, that means continuing to create accurate, trustworthy and genuinely useful content supported by strong technical foundations and a well-structured website.

The strongest websites are increasingly designed for both people and AI systems.

Does AI Search replace Google Search?

No. Traditional search and AI Search are increasingly working together rather than replacing one another. Most people continue to use a combination of both, depending on the task.

Common AI Search terms explained

Understanding AI Search

AI Search is changing how people find information, but the foundations of a good website remain largely the same.

Organisations that publish accurate, useful and well-structured information continue to provide the trusted content that search engines and AI systems rely on to answer questions and recommend sources.

Understanding how AI Search works helps organisations make better-informed decisions about their content, website and digital strategy. While the technology will continue to change, the principles of creating clear, trustworthy and useful information remain as important as ever.

AI Search will continue to evolve, and so will the way organisations approach their websites and digital content. We’ll continue adding practical guides, insights and explanations to our Knowledge Hub to help you understand the changes and make informed decisions.

Further reading

If you’d like to explore AI Search in more detail, the following resources provide authoritative guidance from Google and other trusted organisations.

Official Google resources

Understanding searches better than ever before (BERT)

Learn how Google’s BERT model improved its understanding of natural language and search intent.

https://blog.google/products/search/search-language-understanding-bert

Introducing MUM

Google’s introduction to the Multitask Unified Model (MUM) and how it helps Search understand more complex questions across multiple formats.

https://blog.google/products/search/introducing-mum

Google Search Central

Official documentation covering how Google Search works, including crawling, indexing, structured data and search features.

https://developers.google.com/search

Additional resources

Google Search Blog

Official updates from Google about Search, including AI features and new developments.

https://blog.google/products/search/ 

Schema.org

Documentation for the Schema.org vocabulary used to add structured data to webpages.

https://schema.org

W3C Web Accessibility Initiative (WAI)

The W3C’s accessibility guidance, standards and supporting resources for inclusive websites.

https://www.w3.org/WAI/

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