Answer Engine Optimisation (AEO) is the practice of structuring web content so that AI-powered answer engines, including Google AI Overviews, ChatGPT, Perplexity and voice assistants, extract and cite that content directly in their responses. AEO differs from traditional SEO in that it targets cited answers rather than ranked links, making it essential for visibility in a zero-click search landscape as of 2026.
AEO works because AI answer engines pull content from pages that provide direct, structured, factually clear answers. Pages that bury their definitions in long introductions are consistently less likely to be cited. The mechanism is straightforward: write a clean definition at the top, use structured data such as FAQ and HowTo schema, and answer questions in concise 40 to 60 word blocks that an AI can lift without editing.
Key principles of AEO in 2026:
- Write a direct, extractable definition within the first 100 words of every page
- Use FAQ, HowTo and Speakable schema to signal answer-format content to crawlers
- Build entity authority through consistent NAP data, author bios and a clear About page
- Name specific AI platforms (Google AI Overviews, ChatGPT, Perplexity) to improve entity clarity
- Earn citations from authoritative sources that large language models already trust
AEO is sometimes called Generative Engine Optimisation (GEO). Both terms describe optimising for AI-generated answers rather than traditional ranked results, and at Web SEM we treat them as overlapping disciplines rather than competing ones. The practical difference is narrow: GEO focuses on appearing in generative AI outputs broadly, while AEO focuses specifically on answer-format responses to direct questions.
For businesses in Cape Town and beyond, the commercial case for AEO is clear. Zero-click searches are rising, reducing organic click-through rates from traditional rankings. Being cited by an AI answer engine now replaces holding position one for many informational queries. Web SEM applies a structured AEO audit process, prioritising schema implementation, content restructuring and entity signal building, then measures success by tracking AI citation wins alongside conventional ranking data. It is results-driven work built on transparent, data-focused strategies, not guesswork.