AEO vs GEO vs LLMO: What's the Difference and Does It Actually Matter?

So here's the thing. The digital marketing industry has a pathological need to invent new acronyms. It's how consultants justify their fees and how agencies make simple things sound complicated enough that you'll pay someone else to do them. SEO wasn't enough, so we got SEM. Then content marketing. Then inbound. Now we've got AEO, GEO, LLMO, AIO and probably another three by the time you've finished reading this.

The honest answer? They're all describing essentially the same thing: getting your business mentioned when someone asks an AI a question instead of typing into Google like a normal person. Fundamentally, doing it right has two benefits: 

a) You may get mentioned by one of the various AI's 

b) What does get mentioned, you have some control over, rather than some unnamed AI's latest fantasy; because trust me they are as lazy as a sloth that's had a day off from well being a sloth. 

But because you've searched for this (or an AI has served it up to you, which would be nicely meta), let me actually explain what each one means, where they differ, and which bits are worth paying attention to versus which bits are marketing fluff designed to part you from your money.

AEO stands for Answer Engine Optimisation, and it's probably the term that makes the most intuitive sense. The idea is that search engines have evolved from giving you a list of links to actually answering your question directly. Featured snippets, knowledge panels, those "People Also Ask" boxes that multiply like rabbits every time you click one.

Google's been doing this for years, right? You search "how tall is the Eiffel Tower" and you get the answer at the top without clicking anything. The shift is that now AI tools like ChatGPT, Perplexity and Google's AI Overviews are doing this for increasingly complex questions. Not just "how tall" but "which marketing agency should I use for motorsport sponsorship" or "what's the best approach to social media for a B2B SaaS company in the renewable energy sector."

AEO is about optimising your content so that when these answer engines synthesise a response, they pull from your stuff. You become the source. The cited authority. The business that gets recommended.

The practical focus tends to be on informational queries. How-to content. Definitions. Comparisons. The stuff where someone wants an answer, not a sales pitch.

GEO is Generative Engine Optimisation, and if we're being honest it's about 90% the same as AEO with slightly different emphasis. The "generative" bit refers to AI systems that generate responses rather than just retrieving them. So ChatGPT writing you an answer based on what it knows versus Google serving up a snippet from an existing page.

The subtle difference is that GEO tends to focus more on being cited as a source in these generated responses. When Perplexity answers a question about digital marketing agencies in Devon and puts a little footnote saying "according to SuperHub," that's the GEO win. You're not just appearing in results, you're being named as an authority that the AI trusts enough to reference.

This matters more for recommendation queries. "Which agency should I use" rather than "what is digital marketing." The commercial intent stuff where being cited can actually lead to business.

In practice, the techniques are nearly identical. Schema markup, structured content, authority signals, all the rest of it. The distinction is more about where you're measuring success than how you're achieving it.

LLMO is the technical crowd's preferred term. Large Language Model Optimisation. It sounds more impressive at conferences and it acknowledges that we're not just talking about search engines anymore but about AI systems more broadly.

The theory goes that LLMO is about influencing both the training data (what the AI learns from) and the real-time citations (what the AI references when generating responses). So you're playing a longer game: not just optimising for today's queries but trying to ensure your brand and expertise gets baked into these models as they're updated.

This is where it gets a bit hand-wavy if I'm honest. Yes, if your content is authoritative and widely referenced, there's a chance it influences future training data. But measuring that? Controlling for it? Proving ROI? That's considerably harder than tracking whether Perplexity cited you last Tuesday.

The term is accurate but the practical application is often indistinguishable from GEO. Anyone telling you they've got a proven LLMO strategy that specifically targets training data is probably selling you something.

This one's specifically about Google's AI Overviews, those synthesised responses that now appear at the top of search results for a growing percentage of queries. Last data I saw suggested somewhere between 13 and 16 percent of searches now trigger an AI Overview, and that number's climbing.

AIO is essentially a subset of AEO focused on one platform. The techniques are similar but there's some Google-specific stuff around how they select sources, how featured snippets feed into AI Overviews, and how your existing search performance influences AI visibility.

If you're heavily dependent on Google organic traffic (and let's be honest, who isn't), AIO matters. But I'd argue it's not really a separate discipline so much as a tactical focus area within the broader AEO/GEO approach. Note here, I literally just asked Google how to add a link to a Microsoft Bookings page once the booking is complete and it gave me instructions for a process that simply doesn't exist. So it is, in my view, now less reliable for this intervention. 

Look, I could tell you that each requires a completely different strategy and you need specialists in all four, but that would be shite. The fundamentals are the same across all of them:

Schema markup — structured data that helps AI systems understand what your content is about, who wrote it, and why they should trust it.

Content structure — writing in a way that's easy for AI to parse and cite. Clear answers to specific questions. Logical organisation. Headers that actually describe what follows.

Authority signals — being mentioned, cited and linked to across platforms that AI systems reference. Industry directories, review sites, authoritative publications.

Entity development — making sure your business, your people and your expertise exist as recognisable entities that AI can identify and reference.

Do all of that well and you're covered whether someone calls it AEO, GEO, LLMO or whatever acronym emerges next month.

The only meaningful distinction is where you're measuring success. If you're tracking featured snippet capture and Google AI Overview citations, you're doing AEO/AIO. If you're tracking brand mentions in ChatGPT and Perplexity responses, you're doing GEO. If you're philosophising about training data influence, you're doing LLMO and probably need to touch grass.

We call it AI Search Optimisation because that's what it is and adding more acronyms doesn't make us look clever, it makes us look like we're trying too hard. In fact just to be annoying, we call it AISO, like assh*le 

Our methodology focuses on context over relevancy. Traditional SEO is about being the most relevant result for a keyword. AI search is about being the most contextually appropriate source for an answer. That's a subtle but important shift, and it's why some sites that rank well traditionally are invisible to AI while others punch well above their weight.

The practical work? Schema across every page. Content restructured around questions real humans actually ask. Authority building through genuine expertise rather than link schemes. Consistent entity signals so AI systems know who we are and why they should cite us.

It works. We went from minimal AI visibility to ranking number one across our competitive set in RankZero within 90 days. Got cited by Perplexity within six days of publishing a new page. Watching our impressions climb from 900 a day to over 11,000 while traditional CTR patterns do strange things because AI is changing how people discover and engage with content.

Here's what I'd actually tell you if we were having a conversation rather than you reading a blog post:

The terminology doesn't matter anywhere near as much as the execution. Pick whichever acronym you like, or ignore them all and just say "we want to show up when people ask AI about our industry." That's the goal. Everything else is just dressing.

What matters is whether you're actually doing the work. Schema implemented properly. Content structured for citation. Authority built consistently. Results tracked in tools that can actually measure AI visibility rather than just hoping for the best.

Most agencies are still working this out. Some are selling "AI optimisation" services that are just traditional SEO with a new coat of paint. Others are genuinely ahead of the curve but charging enterprise rates that make no sense for normal businesses.

We're somewhere in the middle. We've figured out what works because we've done it for ourselves first, measured the results obsessively, and iterated based on what actually moves the needle rather than what sounds good in a pitch deck.

If that sounds like something you need help with, we should probably talk.