What do we mean by SEO?
Search Engine Optimisation (SEO) has traditionally meant the process of making websites more visible in search engines like Google. Back in the 2000s, that meant optimising web pages with the right keywords, tags and links so they could rank higher. There has been significant changes over the years. From desktop search to mobile search, the introduction of RankBrain, the rollout of numerous SERP (Search Engines Results Page) features and now of course, AI Search.
Why has the context of SEO changed?
The search experience itself has shifted. Ten years ago, visibility meant ranking in the “10 blue links” of Google. Then came answer boxes, knowledge panels and zero-click searches. Now we are in the middle of a larger transition: generative AI assistants and AI-powered search modes are answering questions directly.
This means the old formula-optimise content for humans scanning a SERP-no longer captures the whole picture. There is a real possibility that users may never see a traditional search results page. Instead, they see a generative AI answer, sometimes with links, sometimes not. That change alters both optimisation tactics and how we measure value.
What is GEO (Generative Engine Optimization)?
Generative Engine Optimisation (GEO) is the practice of optimising for AI-powered discovery environments: large language model assistants, AI search modes, and agent-driven interfaces. Where SEO focused on making content visible to human users via search engine ranking factors, GEO asks: how do we ensure that AI systems can find, understand and cite our brand or content?
Put simply, SEO was about being found by humans through search engines. GEO is about being surfaced by AI systems that may answer on behalf of humans.
Isn’t GEO just SEO with a new name?
It’s true that many GEO tactics look familiar. Structured data, semantic markup, strong content, brand authority—these are all classic SEO best practices. They matter just as much, if not more, when content is being evaluated for training data or citation in AI responses.
But dismissing GEO as “just SEO” misses the point. The environment has changed. The unit of competition is no longer just the search results page; it’s the AI answer. That changes the nature of visibility, how measurement works, and the balance between optimising for humans and optimising for machines. In that sense, GEO is clearly different.
How is optimisation evolving in an AI search world?
In SEO, optimising meant presenting information in ways that aligned with user intent and search engine ranking factors. The human remained central: content needed to attract clicks, keep attention, and satisfy a query.
In GEO, optimisation is hybrid. Content must still serve humans—because AI models are trained on human-facing data—but it must also be structured and distributed in ways that make it attractive to AI systems. That includes:
Creating content that is chunked and semantically clear, so models can process it.
Building brand authority through mentions, citations and trusted signals, so models treat your content as a reliable source.
Controlling how AI crawlers can access and use your data.
The audience is now both human and machine.
How important is measurement in GEO?
Measurement is arguably the biggest shift. In traditional SEO, measurement meant rankings, traffic, clicks and conversions. You could point to clear cause and effect: improve ranking, gain more traffic, generate more revenue.
In GEO, measurement is less straightforward. Visibility may not result in direct clicks. Your brand might be cited in an AI answer, shaping perception and authority, without driving immediate traffic. That forces us to rethink value. We are moving towards measuring brand presence within AI responses, co-mentions with competitors, and share of visibility across generative engines.
Just as SEO matured by embedding measurement alongside optimisation, GEO will only gain traction if professionals can measure and communicate its impact in boardrooms.
What still applies from SEO to GEO?
Many foundations carry across:
Technical excellence: Fast, crawlable, structured sites still matter.
Content clarity: Semantically structured, question-led content works for both humans and AI systems.
Authority building: Brand mentions, backlinks and visibility across the web increase trustworthiness for both search engines and AI models.
Entity optimisation: Covering topics comprehensively, filling gaps, and building topical authority all feed into how AI systems select sources.
These are not “new tricks” but reinforced priorities. GEO is not a replacement for SEO fundamentals—it builds on them.
What genuinely makes GEO different?
The key differences lie in:
Audience: Optimising not just for humans, but for AI intermediaries answering on behalf of humans.
Surface of competition: Moving from SERP rankings to inclusion in AI-generated responses.
Measurement: Shifting from clicks and sessions to visibility, citations and influence without guaranteed traffic.
Content granularity: Ensuring content can be ingested, chunked and reused by models, not just read by people.
These differences are not cosmetic—they reshape strategies, KPIs and investment decisions.
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