What Is a Generative Engine?
A generative engine is a search or information retrieval system that uses a large language model to synthesise answers from multiple sources rather than returning a ranked list of links. The term is used in contrast to traditional search engines, which match queries to indexed documents and display them in order of relevance. Generative engines go a step further by reading candidate documents and generating a novel, composed answer that may cite some of those sources inline.
The most widely used generative engines as of 2025 include Google's AI Overviews (integrated into Google Search), Perplexity AI, Microsoft Copilot (which uses Bing's index and OpenAI models), and ChatGPT with web search enabled. Each system applies slightly different retrieval and generation techniques, but they share the same fundamental behaviour: the user receives a composed answer, not a list of URLs.
For SEO and generative engine optimisation, the shift toward generative engines is significant because visibility is no longer guaranteed by ranking in the top ten organic positions. A page that ranks at position five may never be cited in a generative answer if its content does not directly address the query in a clear, extractable format. Conversely, a page ranked at position fifteen might be cited if it contains the most precise, fact-based answer to the user's specific question.
South African businesses operating in competitive verticals such as financial services, healthcare, legal, and property need to understand which of their target queries are now answered by generative engines rather than blue-link results. For those queries, appearing within the generated answer, rather than in the organic list below it, becomes the primary AI search visibility goal.
Generative Engine In Practice
A Johannesburg law firm notices that queries for "how to register a private company in South Africa" now trigger a Google AI Overview that fully answers the question without the user needing to click any result. The firm's website previously ranked at position three for this query and received steady traffic. After the AI Overview rolls out, clicks on that query drop by 41%.
The firm's digital marketing team audits their content against the AI Overview to understand what sources Google is citing. They find that the generated answer draws from three government sites and one legal resource site. Their own site is not cited despite ranking well, because their explainer article uses informal language and lacks the structured, step-by-step clarity that generative engines prefer.
The team rewrites the article with numbered steps, specific form names and fees from CIPC, direct definitions, and FAQ schema. After reindexing, their page begins appearing as a cited source in the AI Overview for the query, and their content is also cited by Perplexity for a related long-tail variant. This demonstrates how adapting content for generative engine requirements can restore and increase visibility even as traditional click-through rates decline.
FAQ
Which generative engines should South African businesses optimise for?
Google's AI Overviews is the highest priority because Google dominates South African search with over 93% market share. Perplexity AI is growing among research-oriented users globally. Microsoft Copilot uses Bing data and is relevant for B2B audiences. ChatGPT with web search enabled is used by a growing professional segment in South Africa.
How is a generative engine different from a traditional search engine?
A traditional search engine ranks and lists webpages for users to click. A generative engine uses a large language model to read multiple sources and synthesise a new, composed answer directly in the interface. The user receives the answer without necessarily visiting any source page, which means brands need to be cited within the generated answer to gain visibility.