GEO vs SEO: How to Rank in AI in 2026 - Ighenatt Blog

Search engines are no longer just lists of links. Google AI Overviews, ChatGPT Search and Perplexity generate complete responses that cite sources, and those sources do not always coincide with the top organic results. A new field of optimisation has emerged to respond to this reality: GEO.

This article explains what GEO is, how it relates to traditional SEO, what factors determine whether an AI engine cites your content, and how to prepare your strategy to capture traffic from both channels.

What GEO is: definition and origin of the term

GEO (Generative Engine Optimization) is the discipline of optimising web content so that generative AI engines select it as a source in their responses. The term was coined by researchers at Princeton University, Georgia Tech and IIT Delhi in a paper published on arXiv in November 2023.

The original paper analysed 10,000 queries and demonstrated that applying specific GEO techniques (citing sources, including statistics, using authoritative language) increased content visibility in generative engines by up to 40% compared to non-optimised content.

Unlike SEO, where the objective is to position a URL in a list of results, the objective of GEO is for your content to be the source that the AI extracts, paraphrases and cites. The user may not click on your link, but your brand appears as an authoritative reference in the response.

The key conceptual difference

Traditional SEO operates in a “10 blue links” model where the user chooses where to click. GEO operates in a model where the AI chooses what content to synthesise. The user sees a generated response, with sources cited at the bottom or as inline links. The decision of what content to cite is made by the language model, not the user.

GEO vs SEO: similarities, differences and why you need both

GEO and traditional SEO share a common base: both require relevant, well-structured and technically accessible content. But they diverge in objectives, metrics and tactics.

Shared foundations:

• Both need content to be crawlable and indexable.
• Quality and depth of content matters in both.
• Domain authority influences the probability of being selected.
• Semantic structure (headings, lists, tables) facilitates information extraction.

Key differences:

The winning strategy is not to choose one or the other. 89% of searches still generate traditional organic results. AI Overviews appear in approximately 11% of searches in Spain, concentrated in informational queries. A hybrid strategy optimising for both channels captures traffic from both.

How generative AI engines work

To optimise for these engines, you need to understand how they select their sources.

Google AI Overviews

Google generates its response by combining information from multiple indexed sources. It does not use an isolated language model: it combines its model (Gemini) with its search index. The sources it cites tend to be pages that already rank well in organic results, but they do not always coincide with the top positions.

Google prioritises sources with specific and verifiable data. Pages that include statistics with cited sources, clear definitions and direct-answer structure (question-answer, ordered lists) have a higher probability of being cited.

ChatGPT Search

ChatGPT Search browses the web in real time when the user activates the search function. It uses Bing as the underlying search engine and applies its language model to synthesise information. It cites sources with direct links and shows snippets of the original content.

The key difference from Google AI Overviews is that ChatGPT Search shows sources more prominently, with preview cards that include title, domain and extract. This generates higher CTR towards the cited sources.

Perplexity

Perplexity operates as a conversational search engine that cites each statement with a specific source. Its citation model is the most granular: each paragraph of the response includes numbered references. Perplexity particularly values content with quantitative data and primary sources.

The 6 factors that make your content cited by AI engines

The Princeton paper and subsequent analyses by BrightEdge and Search Engine Land identify six factors with the greatest impact on citability:

1. Verifiable quantitative data

AI engines prioritise content with specific numbers: percentages, absolute figures, time ranges. “Web speed improves SEO” is less citable than “a 0.1s improvement in load time increases conversions by 8.4% according to Google/Deloitte”.

2. Citation of primary sources

Including references to studies, papers and official sources increases the credibility perceived by the model. Content that cites its sources gets cited more.

3. Direct-answer structure

Sections that answer a question directly in the first 1–2 sentences, followed by context and nuance, align with the extraction pattern of language models. It is the same principle as a featured snippet, but applied to generative responses.

4. Authoritative language

Language models detect language patterns that they associate with expert content. Phrases such as “according to our tests”, “the data shows that” or “based on analysis of X cases” generate an authority signal that the model weighs.

5. In-depth topic coverage

Content that covers multiple angles of a topic has a higher probability of being cited for different variations of the same query. An article about structured data and AI that covers definition, implementation, schema types and measurement is more citable than one that only defines the concept.

6. Freshness and updates

AI models have a preference for recent content, especially for topics that evolve rapidly. Publication and update dates are signals that engines use to weigh temporal relevance.

Structured data and schema markup to optimise for AI

Schema markup does not guarantee appearance in AI Overviews, but it makes it easier for AI models to extract structured information from your content. The schema types with the greatest correlation with citations in generative engines are:

Article schema — communicates to the engine that the page is an article with author, publication date and topic. The author, datePublished and dateModified fields are especially relevant for evaluating freshness and authority.

FAQPage schema — structured questions and answers align with the most common extraction pattern of generative engines. Google AI Overviews extracts FAQs marked with schema more frequently than unmarked FAQs.

HowTo schema — for instructional content, the HowTo schema allows models to extract specific steps with precision.

Organization and Person schema — entity information (who writes, for which organisation) feeds the Knowledge Graphs that AI models consult to evaluate the authority of a source.

The technical implementation requires JSON-LD embedded in the <head> of the page. Google recommends JSON-LD over microdata or RDFa for structured data.

How to measure your visibility in generative AI results

Measuring presence in AI Overviews and other generative engines is more complex than measuring organic rankings. Tools are evolving, but functional options already exist:

Google Search Console — since mid-2025, it shows impressions and clicks from AI Overviews in the performance report. It is the most reliable source for measuring your presence in Google’s generative responses.

BrightEdge — its GEO tracking module monitors which keywords trigger AI Overviews and whether your domain appears cited. It provides historical data on evolution.

Semrush — has incorporated AI Overview indicators into its position tracking, showing which SERPs include generative responses and which domains are cited.

Manual verification — for qualitative analysis, search your target keywords in Google, ChatGPT and Perplexity and document whether your domain appears cited. It is labour-intensive but provides insights that automated tools do not yet capture.

• Percentage of target keywords that trigger AI Overviews.
• Citation frequency of your domain compared to competitors.
• CTR from AI Overviews vs traditional organic results.
• Referral traffic from ChatGPT Search and Perplexity (visible in Google Analytics as traffic sources).

Real cases: content that appears in AI Overviews and why

Analysing what type of content generative engines cite reveals clear patterns.

Case 1: Core Web Vitals article from a technical blog. An article from a development blog with 3,500 words, 12 cited sources and comparative metrics tables appears cited in AI Overviews for 14 query variations related to Core Web Vitals. The article includes updated CrUX data, tool comparisons and a FAQ section with 8 questions. The structure answers each question directly before expanding into context.

Case 2: ecommerce SEO guide from a SaaS. A 5,000-word guide about SEO for online stores appears cited in ChatGPT Search and Perplexity. The factors: each section opens with a verifiable statement with source, uses numbered lists for recommendations and has a platform comparison table with market share data. ChatGPT Search cites it with a preview card showing the table.

Case 3: the anti-pattern effect. An article with good organic ranking (position 3) for “what is TTFB” does not appear in AI Overviews despite its position. The reason: the article uses generic language without specific data, does not cite sources and its definition is in the third paragraph, not the first. An article in position 7 with a direct definition in the first sentence and three cited sources does appear.

These cases confirm that organic position is a factor, but not the only one. The structure and citability of the content determine the final selection.

How to prepare a hybrid SEO + GEO strategy

The practical implementation of a strategy covering both channels follows these steps:

1. Audit your existing content: identify which pages already appear in AI Overviews and for which queries. Use Google Search Console as the primary source.

2. Prioritise informational keywords: the queries that trigger AI Overviews are predominantly informational (“what is”, “how does it work”, “difference between”). Map your target keywords and classify them by probability of triggering generative responses.

3. Optimise response structure: for each target page, ensure that the direct answer to the query appears in the first 1–2 sentences after the relevant heading.

4. Include data and sources: add verifiable statistics with cited sources in each section. Own data (studies, analyses, experiments) are the most valuable.

5. Implement schema markup: at minimum, Article, FAQPage and Organization. For instructional content, HowTo.

6. Monitor and adjust: review AI Overview presence monthly and adjust content that is not being cited. An article about how to appear in AI Overviews details specific adjustment tactics.

Frequently asked questions about GEO

What is GEO and what is it used for?

GEO (Generative Engine Optimization) is the optimisation of content to appear as a cited source in responses from AI engines such as Google AI Overviews, ChatGPT Search and Perplexity. It is used to maintain the visibility of your brand in an environment where some searches are resolved without the user clicking on a traditional web result.

Does GEO replace traditional SEO?

No. GEO complements SEO, it does not replace it. Transactional searches (buy, hire, compare prices) continue to depend on traditional rankings. GEO is more relevant for informational searches that AI engines can answer directly.

How do I know if my content appears in AI Overviews?

You can verify this manually by searching for your target keywords in Google and observing whether your domain appears cited in the AI Overview response. At tool level, BrightEdge and Semrush are incorporating AI Overview presence tracking into their platforms. Google Search Console also shows impressions and clicks data from AI Overviews.

Does schema markup help to appear in AI Overviews?

Schema markup does not guarantee appearance, but it makes it easier for AI models to understand and extract structured information from your content. The Article, FAQPage and HowTo types show the greatest correlation with citations in AI Overviews.

Search is changing faster than many SEO teams can adapt. We have seen sites lose traffic by failing to adjust their strategy in time, and sites gain visibility simply by publishing content structured for AI. If you want to know where your site stands, let’s talk.