AI Overviews and AI Mode are dramatically changing organic search traffic.
While search engine optimization (SEO) focuses on matching a user’s query, generative search also considers information about the searcher themselves—from their Google Docs usage to their social media footprint. This information is used to inform, not only the current search, but future searches as well.
Likewise, the process of optimizing your website’s content to boost its visibility in AI-driven search engines (ChatGPT, Perplexity, Gemini, Copilot and Google AI) has a similar path. As SEO helps brands increase visibility on search engines (Google, Microsoft Bing), generative engine optimization (GEO) is all about how brands appear on AI-driven platforms. There is overlap between the goals of GEO and traditional SEO. Both SEO and GEO use keywords and prioritize engaging content as well as conversational queries and contextual phrasing. Both consider how fast a website loads, mobile friendliness, and prefer technically sound website. However, while SEO is concerned with metatags and links in response to user queries from individual pages, GEO is about quick, direct responses from synthesizes content out of multiple sources.
AI models are not trained solely to retrieve relevant documents based on exact-match phrasing. Generative search is about fitting into the reasoning process, starting with the user’s identity. That’s why your content is being judged, not just on whether it ends up in the final answer, but whether it helps the model reason its way toward that answer. Despite performing all the typical SEO common practices, your response may not make it to the other side of the AI reasoning pipeline. In fact, the same content could go through the pipeline a second time and yield a different result. It’s not enough to be generally relevant to the final answer. Your content is now in direct competition with other plausible answers, so it must be more useful, precise, and complete than the next-best option.
It appears now that Google AI Overviews favors content that:
contains the who, what, why
offers clarity and distinctiveness in the small sections
is written in natural, conversational terms (AI will attempt to deliver its answer in that same way)
uses strong introductory sentences that convey clear value
has H2 tags that align with user questions
is structured to match common question structures (open, closed, probing)
allows for restatement of quires and implied sub-questions, where a main question is broken down into smaller parts.
contains multi-faceted answers,
is rich in relationships,
has explicit logical structures and supports causal progression,
has clear headlines
cites sources
includes statistics & quotations
has multimedia integration
AI Overviews attempt to exclude content that is overly generalized, speculative, or optimized for clickbait over clarity. Vague and generic writing underperforms.
LLMs are being trained to favor content that helps them reason well. Writers should attempt to match those paths that the models take to arrive at high-confidence answers.
More information:
How AI Mode and AI Overviews work based on patents and why we need new strategic focus on SEO
What is generative engine optimization (GEO)?
