Search engines and the way that information is discovered are always changing. Traditionally, SEO was dominated by keyword density, backlinks and technical optimisation. But it’s now being reshaped by AI-driven systems like conversational assistants, generative search engines and large language models.
Platforms such as Google Search, ChatGPT and even Microsoft Copilot are no longer simply indexing content. Instead, they’re starting to interpret, summarise and synthesise it. This shift means that content creators now need to think more carefully about what they produce and how to structure the information they’re putting out.
Optimising for AI search and discovery has become less about ranking in the search engine results pages (SERPs) and more about how that information is going to be understood, selected and cited by AI systems. In this article, we’re going to be discussing how AI search works and how to write for it.
Before we talk about optimising content for AI search and discovery, we must first understand what each one means. AI search and discovery refers to how artificial intelligence systems find, understand and present information to users. They will often do this by generating direct answers instead of just listing links. However, let’s take a closer look at both of them in more detail:
AI search is a modern approach to search powered by machine learning and natural language processing. Instead of matching keywords, it understands intent, context and meaning. Platforms like Google Search, ChatGPT and Microsoft Copilot will use AI technology to do the following things:
AI discovery is how content is surfaced to users, even when they don’t explicitly search for it. It’s driven by algorithms that predict what users might find useful. This is done by basing it on the following things:
Before we begin optimising content for AI search and discovery, we need to know how they both work. Here’s what they do and why they’re important when it comes to content optimisation:

Now that you know a little more about what AI search and discovery is, what it means, how it works and why it’s important, we can start putting that knowledge into motion. Here are just a few ways in which you can optimise for AI search and discovery in this modern, digital age of marketing:
When optimising for AI search and discovery, look to create clusters of related content around a central theme. For example, instead of one article on “email marketing,” produce supporting content on at least one of the following things:
In addition to this, make sure that you write the way people speak and ask questions. AI models are trained on conversational data, so content that mirrors this performs better. For example, instead of saying “best CRM tools UK”, write “what are the best CRM tools for small businesses in the UK?”.
AI systems prefer content that is easy to parse and summarise. This is where a solid content structure comes in handy. It includes things like using clear headings, providing direct answers to questions, and more. Here’s how to structure content for AI extraction:
AI tools often pull information from content similar to featured snippets. In the old days, this was where people would rank to appear in the Google Answer Box. Today, it’s all about appearing in the AI summary box.
If you want to optimise your content to achieve this, then you should use question-based subheadings, add FAQ sections to your websites and keep answers to questions short and concise.
We’ve recently written an article about “EEAT-ing” your content, which you can read here in more detail. But for now, we’re going to be summarising what strengthening E-E-A-T signals involves. Firstly, E-E-A-T stands for: Experience, Expertise, Authoritativeness and Trustworthiness. In order to do this effectively, you should
Structured data helps machines understand your content. This is especially important when it comes to optimising content for AI search and discovery. It includes things like implementing Schema types as a priority, including articles, FAQs, how-tos and products.
There are many benefits to this, including the improvement of machine readability, increasing the chances of rich results and helping AI systems to categorise content effectively and accurately.
AI search is heavily conversational. We’ve touched on it already, but AI search and discovery are reliant upon human tone of voice. When it comes to optimising for conversational queries, focus on things like targeting long-tail queries, including contextual variations and including prompts as a minimum. Many people use AI tools, like ChatGPT, to get started, especially if they’ve never optimised content for AI search and discovery before.
AI models favour content that is easy to interpret. This is why you need to focus on using simple language, writing short paragraphs and following a logical flow to improve content clarity and readability. In addition, avoid unnecessary jargon and ensure ideas progress clearly from one to the next, just to be sure.
AI search is increasingly multimodal (text, images, video). This means that AI search and discovery uses several categorised modes of activity or occurrence. For instance, to explain what images show in plain language, add descriptive Alt Text. To provide context for visuals, use captions, and to summarise video content in text form for AI parsing, including video summaries.
Traditional metrics alone aren’t enough when it comes to AI search and discovery. Instead, you need to think about monitoring visibility in AI tools and analysing engagement overall. To do this, check whether your content appears in AI-generated answers, track brand mentions and focus on things like:
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As an experienced Copywriter, Lorna enjoys creating varied content for an abundance of different industries and sectors. From detailed, informative articles to creative infographics, she's always looking to inject originality into the work she produces. When she isn't working, Lorna runs her own lifestyle blog, plays the guitar and loves to take part in charity runs.