- Structure your knowledge: Organise internal data, documents and customer insights so AI can easily find and use them.
- Create AI-readable content: Publish clear, authoritative content that directly answers customer questions.
- Use proprietary data: Turn call centre logs, customer feedback and internal expertise into unique AI-ready assets.
- Govern AI workflows: Combine AI-driven research and content creation with human oversight and clear guardrails.
- Prepare for Agentic AI: Build a flexible knowledge architecture that can power future AI agents and platforms.
Consumers are now asking long, nuanced questions of their favourite AI assistant and expecting a single, trusted answer. This shift has given rise to Generative Engine Optimisation (GEO) and Answer Engine Optimisation (AEO), two closely related but distinct disciplines.
And if you are a chief marketing officer hoping to compete in the answer economy, understanding how they differ and how to design for both is becoming increasingly urgent.
AEO or GEO? What is the difference, and is it important?
In marketing, we’re very good at inventing new acronyms to turn heads. But in this case, the distinction actually matters.
GEO is what happens inside the large language model (LLM). It’s how you design prompts, connect your own data and shape the interaction so the LLM gives you the strongest possible output.
AEO is the opposite side of the equation. It’s how you structure your website, content and data so that public LLMs recognise your brand as an authoritative source and use you as the answer when consumers ask questions.
In a nutshell, GEO is how you get better answers from AI, and AEO is how AI gives you or your brand as the answer. Right now, it’s the brands which master both that will have the early mover advantage.
Video provides the AEO advantage, and YouTube even more so
Smart marketers have already begun to invest more in YouTube. In fact, Kantar’s Media Reactions 2025 shows a net 55% of marketers globally plan to increase investment in TV streaming (which explicitly includes YouTube on TV).
This matters for AEO because it’s where Google’s AI goes to find rich, authoritative answers.
Most public LLMs were trained on huge swathes of internet data, but Google has a unique advantage as it owns the search ecosystem and YouTube, which is the world’s second‑largest search engine.
Long‑form YouTube content, such as explainers, demos, webinars and thought‑leadership, gives AI systems far more depth and context than a typical web page. When that video is well‑titled, well‑described and ranking organically for high‑intent queries on YouTube itself, it sends a strong signal of authority.
AEO doesn’t replace SEO, it sits on top of it
It’s useful to think of all the years we’ve spent doing SEO as building the infrastructure. We now have clean site architecture, sensible internal linking, fast pages and content that maps to search intent so Google can rank it.
You still need all of that. What’s changing is what happens when an AI agent, grounded in web search, comes to your site looking for an answer on behalf of a user. If your content is beautifully optimised for traditional SEO, but not structured in a way that a machine can easily parse, prioritise and reuse, you’ll lose at the critical moment, which is when the answer engine decides which single brand to surface.
We advise clients not to treat AEO as just another SEO trick on their website, but rather to see it as part of a bigger, always‑on growth system that connects data, content and distribution.
Agentic AI accelerates the shift
The next wave, agentic AI, will only accelerate the GEO/AEO divide. We’re moving from clever chatbots to orchestrated workflows, where specialised agents can research your private data, apply your brand and governance rules, and generate channel‑ready outputs in hours, not weeks.
In that world, your private knowledge base including your file systems, documentation, behavioural frameworks and your business ‘secret sauce’ IP, all become the real strategic asset.
Agentic AI makes AEO and GEO preparedness all the more urgent. If your knowledge, content and governance are well structured today, you can use GEO to let agents generate high‑quality outputs at speed, and AEO to ensure those outputs help position your brand as the trusted answer inside the very AI systems your customers are using.
So now what? A three-phased plan
In the short-term, the work required may be unglamorous but it is vital and applies to marketing leaders across the globe. CMOs need to get their house in order. They should clean, consolidate and structure their data and knowledge so the brand’s own best answers are actually findable.
For instance, brands can transform untapped internal resources, like call centre data and customer service logs to help them build content roadmaps.
In the medium-term, they should start orchestrating AI‑enabled workflows across research, content and customer experience but with clear guardrails and human oversight.
In the long-term, leaders should treat their knowledge architecture as a living, core asset and stay LLM‑agnostic. This will allow them to plug into whatever new agentic platforms emerge without starting again.
In short, marketing leaders hoping to compete in the answer economy should focus on embracing GEO and AEO together, investing in original content, and building a disciplined knowledge infrastructure, ready for agentic AI.
But the main thing to remember is not to panic. We’ve been adapting to technological shifts since the dawn of marketing. This one is no different in spirit, just faster in pace.
Jonathan Greene is executive VP Americas at RocketSource by Incubeta.
