Google AI Overviews vs AI Search Engines: Two Different GEO Games

When a marketing director asks me "how do we get into AI search results?" I always ask them to be more specific: which AI search? Because the technical mechanics, the content signals, and the optimisation strategies for Google AI Overviews and for standalone AI search engines like ChatGPT and Perplexity are more different than most people assume — and conflating them leads to strategy that is partially optimised for both and fully optimised for neither.

This is not a subtle distinction. Google AI Overviews sit inside the world's largest traditional search engine, inherit its ranking infrastructure, and are fundamentally constrained by Google's existing content evaluation systems. ChatGPT, Perplexity, and Claude operate entirely differently — with their own training data, retrieval architectures, and citation selection mechanisms. Winning one does not guarantee winning the other. The brands that are performing best across both have built differentiated strategies for each.

How Google AI Overviews Actually Work

Google AI Overviews (formerly SGE — Search Generative Experience) are AI-generated answer blocks that appear above traditional search results for many informational queries. Critically, they are generated by a large language model but draw heavily on content that Google's existing search infrastructure has already indexed, crawled, and evaluated.

This means that the signals that influence Google AI Overviews overlap substantially with traditional SEO signals. Domain authority matters. Page quality matters. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) — Google's framework for evaluating content credibility — carries significant weight. Backlinks from authoritative domains matter. Content freshness matters. Schema markup matters.

The key distinction is that AI Overviews are looking for content that is both highly ranked in Google's traditional index and structured in a way that allows the language model to extract and synthesise accurate, useful answers. A page that ranks well organically but is structured for human readers rather than AI extraction may not appear in AI Overviews even when it ranks on page one for the same query.

How Standalone AI Search Engines Work

ChatGPT (with Browse or SearchGPT), Perplexity, Claude, and similar standalone AI search engines operate with a fundamentally different architecture from Google AI Overviews. Understanding these differences is essential for building an effective separate strategy.

Training data vs. retrieval. Large language models like those underlying ChatGPT have a knowledge cutoff — they were trained on data up to a certain date, and their base knowledge reflects that training corpus. When they answer questions without using real-time retrieval, they are drawing from patterns in that training data, not from current web content. This means brand mentions in high-quality web content indexed before the training cutoff influence baseline LLM responses — a different mechanism from Google's ongoing crawl.

Retrieval-augmented generation (RAG). Perplexity, and increasingly ChatGPT with Browse enabled, use real-time web retrieval to supplement their training data. When a user asks a question, the system retrieves relevant current web pages, extracts relevant passages, and synthesises an answer with citations. This is more similar to a dynamic search than a static knowledge base — content freshness and crawlability matter significantly here.

Citation selection. In standalone AI search, citation selection is driven more by content quality signals at the source level — domain authority, content specificity, structural clarity — and less by traditional SEO signals like anchor text backlinks. A highly specific, structured page on a relatively lower-authority domain can outperform a high-authority domain's generic content in AI citation selection. This creates opportunities that traditional SEO does not.

The Diverging Optimisation Strategies

Given these architectural differences, the optimisation strategies diverge at several key points.

For Google AI Overviews: The foundation is traditional SEO health. Pages that do not rank in the top 10 for their target queries are very unlikely to appear in AI Overviews for those queries. So the first priority is ensuring that your citation-critical content already ranks well. On top of that foundation, the specific AI Overview optimisations are: adding FAQ sections with properly structured FAQPage schema, ensuring content answers questions directly and concisely within the first 200 words, structuring longer content with clear H2/H3 hierarchy, and using lists and tables where appropriate to make synthesis easy for the AI layer.

For standalone AI search: Traditional SEO ranking is less important. A page that never appears in Google's top results can still be cited by Perplexity or ChatGPT if it is a credible, specific, well-structured source. The focus should be on domain and author authority, content specificity and extractability, real-time indexability and crawl access, and consistency of entity signals across the web. External authority signals — press coverage, analyst mentions, third-party reviews — carry relatively more weight in standalone AI search than in Google AI Overviews.

Where the Strategies Converge

Despite the differences, there is a core set of practices that benefit both Google AI Overviews and standalone AI search. These are the foundational elements that should be in place before you build platform-specific strategies.

Clear entity definition — having a consistent, specific description of what your brand is, what it does, and who it serves — matters on both platforms. Both rely on entity recognition to connect your brand to relevant queries. Inconsistent or vague entity definition creates uncertainty that reduces citation likelihood across all AI platforms.

Structured data, particularly Organisation, FAQPage, and relevant industry-specific schema, provides clarity to both Google's AI layer and to standalone AI retrieval systems. Our six-times improvement in AI Overview appearance rate for FAQ schema pages reflects a pattern that shows up in standalone AI citation as well, though the exact magnitude differs.

Author authority — named experts with verifiable credentials, published thought leadership, and third-party mentions — is valued by both. Google weights it explicitly through E-E-A-T signals. Standalone AI systems weight it as an editorial credibility signal. Investing in your team members' public profiles and authored content pays dividends across both platforms.

For the practical implementation of the on-site content elements described here, the GEO Content Framework covers the layer-by-layer approach that underlies both strategies. And for the specific technical details of structured data implementation, the structured data and schema markup guide provides the implementation specifics.

Building a Dual-Platform GEO Strategy

In practice, most brands do not have the resources to run fully separated optimisation programmes for each AI platform. The practical approach is to build a strong shared foundation — the convergent elements described above — and then layer platform-specific tactics on top.

For brands with strong existing SEO, the Google AI Overview layer is relatively low-hanging fruit: add FAQ schema to your highest-ranking pages, restructure content for direct-answer extraction, and monitor AI Overview appearances in your rank tracking tools. The foundation is already there.

For standalone AI search, the investment is different and additive: focus on content specificity rewrites, external authority signal development (press, analysts, third-party reviews), and systematic citation monitoring across platforms. This work pays dividends in Google AI Overviews too, but its primary impact is in the standalone AI search ecosystem where most of the growth in query volume is currently happening.

The brands winning comprehensively across both platforms share one characteristic: they have stopped thinking about AI search as a single channel and started building differentiated strategies for each platform's distinct mechanics. That specificity of approach is increasingly what separates the leaders from the rest.

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