AEO, GEO, AIO: what the acronyms mean, and what actually changes for a brand

Every few months someone asks me whether they need an AEO strategy, a GEO strategy or an AIO strategy, usually because an agency has quoted for all three. My answer is that they are three names for overlapping parts of the same problem: how your brand gets found, understood and recommended when the answer is written by a machine rather than chosen by a person scanning ten blue links.

The short definitions

AEO, Answer Engine Optimisation, is about being the answer. Featured snippets, People Also Ask, voice assistants and the direct-answer boxes that sit above the results. It predates generative AI and is mostly about clear, well-structured content that answers a specific question better than anyone else.

GEO, Generative Engine Optimisation, is about being represented accurately in answers that are generated rather than retrieved. The term comes from a 2023 Princeton and Allen Institute paper, later published at KDD 2024. The engines it applies to are Google AI Overviews and AI Mode, ChatGPT, Perplexity, Gemini and Copilot. The model reads many sources, then writes its own answer and decides which brands and pages to mention or cite.

AIO is the loosest of the three. Some people use it to mean AI Overviews optimisation, others AI search optimisation in general. The broad meaning is the one most clients actually mean. It covers AEO and GEO, plus the practical work of measuring whether AI assistants mention you, link to you and send you customers who buy.

If someone sells you three separate programmes for these, ask them where one ends and the next begins. In practice they share most of the same foundations.

What has not changed

The fundamentals still do most of the work. AI assistants that browse the web lean heavily on the same indexes and the same signals of quality that classic search does. Google says this plainly in its guidance on AI features: to appear as a supporting link in AI Overviews or AI Mode, a page has to be indexed and eligible for a snippet in normal Search, and there are no extra requirements or special optimisations. If your site is hard to crawl, your pages do not answer the question in front of them, or nobody credible ever mentions you, no amount of prompt-era tactics will fix it.

That is the part of the AI search conversation that gets lost. The brands I see winning in AI answers are, almost without exception, brands that were already doing search properly.

What does change

There are three real differences, and they are worth planning for.

1. Being mentioned matters as much as being clicked. A generated answer can recommend you without sending a single visit. That changes what you measure. Rankings and clicks are no longer the whole picture; you also need to know how often you are named, in what context, and whether you are cited with a link.

2. Your brand is now described by someone else. In classic search you wrote the title and description that appeared under your name. In an AI answer, the model writes the description, drawing on everything it can find about you: your site, reviews, comparisons, forum threads, retailer listings. Consistency across all of those sources now shapes how you are introduced to a buyer.

3. The visits that do arrive are often better. AI assistants tend to send people later in their decision, after the comparison has already happened in the conversation. In the ecommerce programme I run, visitors from AI assistants have converted at around one and a half times the rate of visitors from Google over the past six months. They are still a small share of traffic. Fewer visits, better visits, which is why traffic alone is the wrong scoreboard.

What I would do first

If you are deciding where to start, I would put the effort in this order:

• Measure it. Build a fixed panel of the questions your buyers actually ask, run it across the major assistants on a schedule, and track presence, citation and sentiment over time. You cannot manage what you only check by hand once a quarter.
• Fix the foundations. Crawlability, clear page purpose, structured data and content that answers real questions directly. This helps classic search and AI answers at the same time.
• Make your brand easy to describe accurately. Clear entity information, consistent facts across your site and the places that talk about you, and pages that state plainly who you are for and what you do.
• Track revenue, not just visibility. Separate AI assistant traffic in your analytics and follow it through to sales. That is the number your leadership team will care about.

The acronyms will keep multiplying. The underlying question stays the same: when someone asks for what you sell, does the answer include you, describe you correctly and send the right people your way?