SEO has been the backbone of online visibility for two decades. Keyword research, backlinks, meta tags, and content strategy have helped businesses climb Google's rankings and capture organic traffic. But the landscape is shifting, and a new discipline is emerging alongside traditional SEO: Generative Engine Optimisation, or GEO.
What Is Generative Engine Optimisation?
GEO is the practice of optimising your online presence so that AI-powered search engines and large language models can find, understand, and recommend your business. When someone asks ChatGPT, Claude, Perplexity, or Google's AI Overview a question, the answer is generated from a combination of training data and real-time web retrieval. GEO ensures your content is part of that process.
Traditional SEO focuses on ranking in a list of blue links. GEO focuses on being cited in a generated answer. They are related but distinct, and the businesses that understand the difference are going to dominate their markets.
How GEO Differs From Traditional SEO
Intent Mapping vs Keyword Matching
SEO targets keywords. GEO targets intent. AI models do not match keywords to pages. They understand what the user is trying to accomplish and generate a response that addresses it directly. Your content needs to answer specific questions clearly, not just contain the right phrases.
Structured Data vs Page Rank
In traditional SEO, your position depends heavily on domain authority and backlinks. In GEO, structured data (schema markup, JSON-LD, llms.txt) is the primary signal. AI models extract structured data more reliably than they extract meaning from unstructured copy. If your site lacks schema markup, you are essentially invisible to AI search.
Citability vs Clickability
SEO optimises for clicks. GEO optimises for citations. When an AI model recommends your business, it often provides a summary rather than a link. Your content needs to be specific and factual enough that the model can reference it with confidence. Vague marketing copy does not get cited. Concrete statements about what you do, where, and for whom do.
Practical Steps to Get Started
- Audit your structured data. Use Google's Rich Results Test to check what schema markup your site currently has. Add LocalBusiness, FAQPage, Service, and Article schema where relevant.
- Create an llms.txt file. This is a plain text file at your root domain that tells AI models what your site is about and how to reference it. Think of it as robots.txt for language models.
- Rewrite your service pages for specificity. Replace vague descriptions with concrete statements. "We offer bespoke digital solutions" becomes "We build custom websites for small businesses in Merseyside, typically delivered within 48 hours."
- Build FAQ sections that mirror real questions. Not the questions you wish people would ask, but the ones they actually do. Check your inbox, your Google Business Profile questions, and your sales call notes.
- Publish content that demonstrates expertise. AI models weight authoritative, original content. Blog posts, case studies, and guides that show genuine knowledge will be cited more often than generic filler.
Why This Matters Now
AI search adoption is accelerating. ChatGPT has over 200 million weekly active users. Perplexity is growing rapidly. Google's AI Overviews now appear for a significant percentage of search queries. The shift is not coming. It is here.
The businesses that invest in GEO now are building a moat that will be very difficult for competitors to cross later. Just as early adopters of SEO dominated search results for years, early adopters of GEO will dominate AI-generated recommendations. The window is open, but it will not stay open forever.
Read next: our pillar guide — Generative Engine Optimisation: The Complete Guide for Small Businesses — has the 12-point audit, schema markup patterns, llms.txt template, and the citable-content rewriting rules to put GEO into practice.
