The search landscape has fundamentally changed
For two decades, Search Engine Optimisation meant one dominant goal: rank on Google Page 1. That's no longer the whole picture. A growing share of information queries — particularly research questions, comparisons, and "what is" queries — are now answered directly by AI systems, without the user ever visiting a traditional search results page at all.
This is a fundamental shift in digital visibility, not a passing trend.
What is GEO — Generative Engine Optimisation?
GEO is the practice of structuring content so it becomes genuinely useful raw material for AI to summarise, cite, and generate answers from. Unlike traditional SEO, which largely optimises for crawlers and ranking algorithms, GEO targets the way large language models actually select, weight, and cite source content when constructing an answer.
Key GEO principles:
- Authority signals — LLMs prefer sources that demonstrate genuine expertise, not generic marketing copy
- Structured clarity — clear headings, definitions, and direct answers extract more cleanly than dense, unstructured prose
- Topical completeness — covering a subject thoroughly, not a single narrow angle, increases the odds of being cited
- Named-entity consistency — using your organisation's name, location, and specific expertise consistently across content, so AI systems can reliably link them together
What is AEO — Answer Engine Optimisation?
AEO is closely related but specifically targets the "answer box" format — featured snippets, voice assistant responses, and AI-generated direct answers. AEO content is structured to be pulled out cleanly as a standalone answer, often through:
- Direct-answer formatting — a clear, quotable answer positioned at the very top of a page, before supporting detail
- Schema markup — FAQ schema and structured data give AI and search engines a machine-readable version of your content, not just human-readable prose
- Conversational structure — content phrased the way a person would actually ask the question, not just the way a marketer would title a page
How LLMs decide what to cite
Modern AI answer engines use a combination of signals when deciding what to cite:
- Pre-training data — what the model learned during training (harder for any single brand to influence directly)
- Retrieval-Augmented Generation (RAG) — real-time search that pulls current content into the answer, which is exactly where a well-structured page can genuinely compete
- Trust signals — domain authority, backlinks, freshness, and content quality
If your brand doesn't appear in reliable, up-to-date, well-structured content, you're structurally less likely to be included when an AI system constructs its answer.
What this means for Kenyan and African businesses
The gap is particularly significant for businesses in emerging markets — global AI systems are trained overwhelmingly on Western content and often underrepresent local expertise, local regulation, and local context. This is a genuine first-mover opportunity, not just a defensive move — a business that establishes clear, well-structured, locally specific authority now becomes the reference point AI systems return to later.
The RETIS approach: SEO and GEO/AEO together
At RETIS Systems, digital growth work treats SEO, GEO, and AEO as one integrated approach, not three separate disciplines competing for budget:
- SEO — technical optimisation, Core Web Vitals, keyword strategy, backlink building
- GEO — authority signals, topical coverage, content quality, authoritative depth
- AEO — direct-answer content, schema markup, multi-audience formatting for humans and AI alike
Start with an audit
Before optimising for AI visibility, you need to understand where you currently stand. Ask yourself: if a potential customer asked ChatGPT or Gemini about your industry or service, would your brand come up? Are your content's structural signals (schema, headings, direct answers) actually in place, or just assumed? Are your key pages genuinely citable, attributable, and current, or thin and generic?