AI has changed the rules around digital brand visibility. Websites still need to be optimized for discovery in traditional search engines (SEO), like Google or Bing. But to be found in this era of AI, brands also need to focus on three separate but complementary strategies: AEO, GEO, and AIO.
You might have heard these terms used interchangeably, and they are very closely connected, but conflating them can lead to gaps in a brand’s visibility because they solve very different technical challenges and target different user behaviors.
We explore these three strategies – how they work together and the unique tactics underneath each – in a recent eBook. For this upcoming blog series, we’re going to discuss each pillar in more detail.
Let’s start by looking at AEO (answer engine optimization).
So, what is the purpose of AEO?
The goal is to get your content cited in a response from an AI system.
To “win” at this, you’ve probably heard that you need to focus on the structure of your content: add FAQs, use schema markup, or improve your heading hierarchy. All of which are valuable pieces of this strategy, but the bigger reason that brands are unsuccessful at getting cited is that their content doesn’t answer the questions people are asking AI.
How does AEO differ from SEO?
SEO is about getting someone to your website, while AEO is about getting an AI system to use your website as a source when it answers a question.
With SEO, you tend to think in keywords that represent topics. Someone searches ‘running shoes’ and you build a page around that concept.
With AI, people don’t search like that anymore. They ask complete questions, like:
What are the best running shoes for beginners?
Which running shoes are best for flat feet?
Are Brooks or HOKA better for long-distance running?
Those questions all sit within the same topic, but they require different answers.
AI systems need to identify a clear, direct answer to the specific question being asked. If your website doesn’t provide that information, it doesn’t matter how well the page is structured.
How do you know which answers to create for AEO?
This is where a lot of businesses get stuck. They can start by looking at customer support enquiries, sales calls, reviews, and community discussions, but the brands that are ahead are the ones with a digital assistant on their website.
Every question someone asks that assistant is direct evidence of what people are trying to do. You can see exactly the information they’re searching for, as well as where people get stuck, and where your content falls short or is missing entirely.
That creates a feedback loop. Every new question tells you what content you should create next. As you answer more of those questions, your digital assistant gets better, your website becomes more useful, and AI systems have more high-quality content they can cite from your brand.
At ai12z, this is one of the core principles behind our AI discoverability platform.
How do you start optimizing your content for answer engines?
Our AI discoverability platform can identify content gaps and prioritize opportunities. Rather than producing a list of disconnected recommendations, it creates a roadmap that helps teams understand:
Which questions people are asking
Which questions your website doesn’t answer well enough
What type of content should be created (FAQs, comparison pages, documentation, glossaries, or educational content.)
Which improvements are likely to have the biggest impact on AI visibility
Instead of treating AEO as a one-time audit, our platform helps organizations continuously discover new questions, identify content gaps, prioritize improvements, and measure progress over time in order so you can get cited in AI systems.
Answer Engine Optimization
If you’re wondering how well your brand is positioned for answer engine optimization today, run a free AI Discoverability Snapshot with ai12z. It provides a high-level view of your AI visibility and is a great starting point for identifying opportunities to improve. For organizations looking for deeper insights, our full AI Discoverability Reports include more than 100 pages of analysis, recommendations, an interactive dashboard, and prioritized next steps to improve AI discoverability.
