Ranking Factors for AI Search Engines (2025 Study)

AI search engines are transforming the way content is searched, evaluated, and cited.

AI search changes how information is consumed, but it still relies on the foundations of traditional SEO. While core SEO signals remain important, generative models also look for clarity and structure to validate content for citation.

This guide breaks down the real ranking factors behind AI search visibility, based on hands-on analysis and citation testing.

TL;DR

• AI search engines often cite pages that rank well in traditional search, but top ranking alone does not guarantee citation.
• Clear structure with headings, bullet points, and tables improves AI readability.
• Direct answers and summaries help AI quickly extract relevant information.
• Structured data and schema make content machine-readable and citation-ready.
• Well-defined entities reduce ambiguity and enhance context for AI responses.
• SEO ensures discovery, while clarity, structure, and machine-readability drive citation suitability.

What You Will Learn in This Guide:

Let’s be clear: SEO is not dead. In fact, our recent study confirms that SEO is the price of admission for AI visibility.

Our analysis revealed that the vast majority of AI citations were pulled from pages already ranking on the first page of Google SERPs. If the AI cannot find you via traditional ranking signals (like authority and relevance), it generally won’t cite you.

However, ranking #1 does not guarantee a citation.

The difference exists because AI systems prioritize:

• Clarity of explanation over ranking position
• Structure that supports summarization and extraction
• Context that reduces ambiguity when generating answers

This is where SEO has evolved. Traditional optimization focuses on Discovery (getting into the top 10). AI optimization focuses on Suitability (getting chosen as the answer).

This is where AI Optimization (AIO) comes into play.

SEO helps content get discovered, Answer Engine Optimization (AEO) helps it become the answer, and Generative Engine Optimization (GEO) helps it remain usable across generative AI systems that summarize, compare, and cite information.

In short, Strong SEO gets you into the candidate pool, but strong AI optimization wins you the citation. The two are not competing strategies; they are two halves of the new standard.

To understand how AI-powered systems select sources, we conducted a structured analysis of AI-generated answers across multiple platforms.

Rather than focusing only on Google rankings, the experiment examined which pages were cited and how they differed from other relevant results. The analysis compared cited pages with non-cited pages appearing for the same queries.

This approach helped identify consistent signals influencing AI citation behavior.

What We Searched & Where

To examine how AI systems select sources, we tested a diverse set of queries designed to reflect real user intent. The queries spanned several types:

• Informational – providing definitions, explanations, or insights
• Commercial – comparing products, services, or solutions
• Transactional – focused on purchasing decisions or actionable steps

These queries were run across AI-driven platforms to capture differences in citation behavior:

• Google (AI Overviews)
• ChatGPT
• Perplexity AI

Each query was reviewed multiple times to account for variations in AI responses. The aim was to identify which pages were cited and to observe consistent patterns in AI content selection.

What We Measured (Analysis)

For every cited and non-cited page, we systematically evaluated content and structural attributes to identify factors influencing AI citations. By calculating the percentage of pages with each feature, we identified patterns that make a page more likely to be cited.

Key factors measured included:

• Content Structure – clarity of headings and subheadings
• Direct Answer / Summaries – presence of concise answers to user queries
• Bullet Points and Comparison Content – use of lists or structured sections
• Table Usage – presence of tables for comparison or clarity
• Freshness / Updated Content – whether the content was recently updated
• Author Bio – visibility of author or expertise information
• FAQs – inclusion of frequently asked questions
• Schema / Structured Data – machine-readable elements like JSON-LD
• Entity Clarity – especially for commercial queries, clear mention of key entities
• Price / Features Info – for transactional queries, presence of pricing and feature details

The Results: AI Cited vs. Non-AI Cited Pages

Based on findings from December 2025.

Summary

Cited pages consistently feature clear structure, direct answers, tables, bullet points, and schema, making them easier for AI systems to interpret and reference. Freshness, FAQs, and well-defined entities further increase citation likelihood. Non-cited pages often lack these elements, showing that AI citations prioritize clarity, structure, and suitability for machine reading over ranking alone.

1. Semantic Density & Information Gain

This factor aligns with AEO (Answer Engine Optimization), where content that delivers clear, complete answers with high information value is more likely to be selected. AI systems favor pages that explain the topic fully without unnecessary filler.

2. LLM Readability & Structural Clarity

Content that is well-organized, clearly structured, and easy for machines to parse improves AI readability. This includes logical headings, subheadings, and a flow that makes it simple for AI to extract and summarize information.

3. Entity Presence & Contextual Authority

Clearly identifying key entities such as products, brands, or concepts, and presenting them in context, signals authority to AI. Well-defined entities help AI understand the topic and reduce ambiguity.

4. Structured Data & Machine Interpretability

Machine-readable elements such as schema, JSON-LD, and tables make content easier for generative AI systems to extract and reuse. This aligns with GEO, where structured formats improve interpretation, verification, and citation suitability.

5. User Experience (UX)

Pages that are clear, easy to navigate, and accessible enhance both human and AI interaction. A good user experience ensures that key information is easy to find, making the page more suitable for AI citation.

Based on findings from December 2025.

AI search engines tend to favor pages that present information in clear, structured, and easily interpretable formats. Certain page layouts make it easier for AI systems to extract and reference content:

• Articles with Summaries or Direct Answers: Concise explanations help AI quickly understand the topic.
• Bullet Points and Lists: Structured lists improve readability and facilitate efficient information parsing.
• Tables and Comparison Charts: Tables organize information in a way that AI can easily interpret relationships or differences.
• FAQ Sections: Frequently asked questions highlight key subtopics and support content extraction.
• Schema & Structured Data: Machine-readable elements, such as schema markup, enhance content interpretability.
• Updated and Authoritative Content: Clear authorship and fresh information increase trust and usability.

In essence, AI prefers pages that combine clarity, structure, and extractability, making them easier to reference in responses.