The rise of generative search is set to be one of the biggest shifts in years in how people find information online – and this raises new challenges for marketers. To adapt to this, all brands must make generative engine optimisation, or GEO, a key part of their activities.
For many businesses, this will be a new concept. So what does it involve and why should you be doing it?
GEO refers to efforts to tailor your website’s content to increase its chances of appearing in generative search results.
You could think of it as a younger sibling of traditional search engine optimisation (SEO) – it has much the same goals and follows similar principles, but it focuses on adapting your website for an era where AI search visibility is vital.
End-users are becoming increasingly familiar with and reliant on generative search – ie, search queries that use generative AI to create new results rather than simply presenting a list of links, such as Google’s AI Overviews.
Consider these stats from McKinsey:
AI is already transforming search, so brands that fail to account for this in their marketing will quickly fall behind and lose visibility to more forward-looking competitors.
AI Overviews now appear in 21 per cent of Google search results, according to Ahrefs – with this rising significantly for searches using longer keywords and question-based phrases. However, the appearance of AI-generated results directly in SERPs is only one part of the challenge.
The other side of generative search bypasses the likes of Google Search altogether. Many consumers now type or speak their questions directly into large language models like ChatGPT, Gemini or Microsoft Copilot. This means GEO also has to focus on visibility on these platforms.
Book a GEO strategy call today to learn more about what this means in practice
GEO is much more than an extension of SEO. It requires a whole new way of approaching content creation and website structure. Essential strategies to remember about GEO include:
We don’t just talk about change we help businesses succeed through it. Our work has delivered
increase in organic sessions for bOnline.
high-quality leads generated for Town Square Invest.
reduction in lead generation costs for Blackfog with blended content strategies
Generative engine optimisation (GEO) is the practice of shaping online content so AI-powered search tools and chatbots can understand, trust and surface it. The aim is to make content easy for generative systems to interpret and cite in their instant, conversational answers, increasing the brand’s visibility where users now search.
SEO focuses on helping search engines crawl, index and rank pages, aiming to win a position on results pages and drive users to click through. It relies on keywords, backlinks, technical hygiene and metadata to boost visibility and organic traffic. GEO, by contrast, is about helping generative engines understand, trust and use content inside their written answers. Instead of chasing rankings, it aims to secure a place within the generated response itself, prioritising semantic clarity, factual consistency and authoritative signals to increase brand visibility even when users don’t click.
Generative search works through a process known as retrieval-augmented generation (RAG). First, it retrieves relevant material by breaking content into smaller passages and identifying those that closely match the query’s meaning. Then, it uses a large language model (LLM) to produce a concise, summary-style response based on those passages, often weaving in extra context from the user’s history or the web.
Brands can strengthen their presence in AI-generated answers by publishing clear, authoritative content and supporting it with strong structure, clean metadata and natural, easy-to-parse language. Building credibility through a positive online reputation, third-party endorsements and consistent brand behaviour also helps AI systems view the source as trustworthy. Alongside this, maintaining solid technical foundations ensures AI tools can locate, interpret and confidently draw on the brand’s information when generating responses.
Google’s Generative Search works much like a librarian: it scans its vast index and points you towards relevant sources, then adds an AI-generated summary above the traditional list of links. AI Search tools like ChatGPT behave more like a research assistant, gathering information on demand and producing a full, conversational answer as the main output. While Generative Search enhances the familiar results-page experience, AI Search replaces it with an answer-first approach that prioritises clarity, context and coherence over classic ranking signals.
A search technology that combines traditional results with AI-generated summaries. The system retrieves information from its index, then uses a language model to produce a short overview above the usual list of links. Google’s SGE is the most common example.
Generative Search enhances the familiar Google results page with an AI summary, still relying on indexing and rankings. AI Search replaces the results page entirely, producing a direct, assistant-style answer drawn from live retrieval and model reasoning.
Structured databases that map relationships between entities, such as people, places, products or concepts, to help search systems understand context. They allow engines to recognise what a query refers to and how different ideas connect.
An optimisation approach focused on meaning rather than keywords. It involves writing clear, structured content that helps search systems understand topics, intent and relationships between concepts, rather than relying on exact-match phrasing.
Brief overviews generated by language models to give users a quick answer without reading full pages. They synthesise key points from multiple sources and appear in products like Google’s SGE, Perplexity and AI-powered browsers.
Search systems that use RAG. They first fetch relevant information from the web or an index, then use a language model to synthesise an answer based on those retrieved passages. This improves accuracy and reduces fabrication.
The practice of making content easier for AI search tools to find, interpret and use in their answers. It focuses on clarity, structure, trustworthy signals and semantic consistency so generative engines can confidently surface a brand’s information.
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