The phrase ‘Search Engine Optimisation (SEO)’ first came onto the scene in 1997 and has arguably become one of the most impactful and accessible ways for businesses to market themselves in the 21st Century.
As most marketers know, SEO is the act of optimising the structure of content on a website so it has better visibility on search engines – namely Google – to increase the number of internet users coming to a website.
For example, if a petshop owner optimises their website in-line with SEO practices – such as having highly relevant content, content headers and backlinks from other webpages – their website will likely rank high in search results if a user searches “puppies for sale”.
The emergence of Large Language Models (LLMs), however, is disrupting the way we search and optimise for search.
The advent of OpenAI’s ChatGPT in 2022 triggered a tsunami of AI investment and exposed the potential of consumer AI models to the public. Since, Google has had a competitor.
Clearly, times are changing and AI chatbots are revolutionising how we seek information. A byproduct of this is a shift from traditional SEO, to optimising for higher visibility in generative AI platform search results – a practice often referred to as AI Optimisation (AIO) or Generative Engine Optimisation (GEO).
What is Generative Engine Optimisation?
Generative Engine Optimisation is the practice of optimising a website so it appears in AI chatbot answers instead of ranking highly in search engine results.
Large Language Models (LLMs), that power AI chatbots like ChatGPT or Perplexity AI, show links to brand websites in query responses through citing, quoting or summarising what it’s found on the internet. Therefore, one of the major aspects of GEO is content that is highly factual and clear, so it can be easily quoted in AI answers.
Because of this, GEO has less of an emphasis on things like keywords, click-optimisation, and technical SEO elements, but still values high quality, structured content that delivers information efficiently.
Other GEO factors include writing conversational content that’s easy for chatbots to digest, and structuring content clearly with headers and bullet points – which also happen to be important for SEO too.
“GEO is the practice of optimising brand visibility within AI-generated responses,” says Ben Gibson, chief exec at GEO firm reegen.ai. He continues: “A great way to look at this is to compare it to SEO. Unlike traditional search engines, which rely on keywords and backlinks, AI models generate content by pulling from trusted media, expert insights, and authoritative sources, as well as low-to-mid value sources such as a company’s website, reviews, social posts and user-generated content. And importantly, these Gen AI tools generate answers, not just results.
“LLMs which power Gen AI tools generate answers through a complex process of contextualising and predicting words based on patterns learned during training. To influence AI-generated answers, the words associated with a brand, their frequency and consistency, and where they appear on the internet are all important factors.”
A main feature of GEO compared to SEO is the consideration of including the answers to search queries in content. This means factoring in answers to questions your current or desired consumer is likely to have.
“I think a lot of the principles of optimising for AI are very similar to optimising for search,” says Nick Miller, co-founder of performance marketing firm Seed. “AI will curate answers and give you more of a balanced view.
“So it’s more of a strategic mindset shift for marketers, from not just thinking about optimising for a specific set of keywords, but actually thinking about what questions your customers are going to be asking AI, and then how you create content that serves them and can be featured in that curation.”
Is GEO Really That Different?
Like with most AI buzzwords, it can be difficult to cut through the noise and understand if shifts as a result of AI are actually going to have notable effects on marketing as we know it.
What you could probably tell, is that SEO and GEO share similar rules. According to Miller, there are few differences: “GEO is specifically optimising answers for AI tools, and then SEO primarily is optimising content for search engines, which ironically, uses AI. They have the same objective, but for a different platform.”
“The actual principles of GEO and SEO are basically the same thing – with accuracy and factual correctness being at its core. I don’t see it being such a massive shift, and I don’t see that GEO is going to create a new industry. I think it’s just going to be adopted, and it will just evolve into what SEO already is, because the principles are the same.”
A major similarity between SEO and GEO, Miller explains, is the need for trusted, factual content. But with GEO being in its early stages, it’s unclear how AI chatbots will be able to differentiate between trustworthy and non-trustworthy information. He continues: “Two or three years ago, the way you created legitimacy was through trust and authority building, and primarily that would be through social signals or through links and domain authority. What nobody really knows is how an AI determines trust and authority.
“AI chatbots often get confused, answer logical questions incorrectly, or hallucinate.”
The Next Step: Optimising For Agents
Agentic AI has been the buzzword of 2025. When it comes to the way users will search in the future, Big Tech firms say people will use AI agents to book holidays, plan events, and search the web – so it will be AI agents that websites need to be optimised towards, not only AI chatbots.
This is already happening, with some brands optimising towards conversational agents, like Alexa and Bard, through including a conversational tone, supporting open-ended queries in content, and matching website FAQs to user needs. All of these factors ensure information can easily be found by agents.
The main difference between GEO and optimising for AI agents is that GEO content needs to be structured to be quoted in AI chatbot responses, whereas content for agents only needs to be understood ‘silently’ by an agent and doesn’t need to be written how it would appear to a user.
Because a lot of agent work is done behind the scenes, much of optimising for these models lies in having clear and enhanced HTML. This includes optimising your website’s semantic and schema markup – which are HTML elements that makes content more machine-readable for agents.
As you can see, GEO and optimising for agents follow similar tenets – however chatbots aim to display content exactly how you write it, while the agents want to understand your content.
What Impact Will Shifts in Search Have on Advertising?
AI is transforming search ads by shifting focus from keywords to user intent, and diminishing the number of people coming to websites and seeing adverts.
In a recent survey from Future, 27 percent of US people said they use AI chatbots over search engines, and a 2024 Gartner study suggests traditional search volume will drop 25 percent by next year.
This shift is going to dramatically decrease the number of users navigating to websites, pushing advertisers to evaluate how they connect with users.
To compete with AI chatbots, Google has even altered its interface to include a summarised AI-generated search result called ‘AI Overview’ – a comprehensive answer to queries, so that users don’t have to sift through websites.
“People are staying inside of the Google interface and not clicking out to websites because they’re finding the information they need in those summaries,” says Grant Gudgel, SVP of Marketing at ad software firm Verve. “If those people aren’t clicking out to that website, there’s an entire ad supported web business model that starts to break down.”
The impact of these shifts on marketers is significant, as they will need to adapt to AI-generated search results, optimise for visibility within them, and potentially embrace new ad formats in AI chatbot interfaces.
“I think that very quickly, we’re going to see native ads in chatbots,” continues Gudgel. “OpenAI and some of the other platforms have already started hiring teams to test search ads inside of chatbot results. It’s a very natural and obvious evolution for those business models.”
“The way we search in AI chatbots compared to search engines is a lot more specific. We might ask a chatbot to compare two cars, or tell it what kind of car engine we want. For this reason, we can prepare for chatbot ads to be far more granular and personalised than search ads.”
Search in the Future
While the core principles of creating high-quality, trustworthy content remain, the platforms we optimise for, and the way that content is surfaced, are evolving rapidly. Marketers must now think beyond ranking on Google and consider how their content is understood, summarised, and presented by AI chatbots and agents.
As search becomes more conversational and less reliant on clicks, those who adapt early – by aligning their strategies with the new rules of generative and agentic AI – will be best positioned to stay visible in the age of AI-driven search.
