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How to rank on AI search
How to Rank on AI Search
Ranking on AI search requires optimizing for both LLMs and Google's AI Overviews through structured data implementation, EEAT signals, and real-time visibility tracking. With 25.8% of U.S. queries triggering AI Overviews and 60% of searches ending without website visits, brands must adapt their strategies to maintain visibility in this AI-driven landscape.
TLDR
AI search dominance: 75% of AI Overview links come from top 12 organic rankings, proving traditional SEO remains foundational while requiring AI-specific optimization
Query structure matters: Long informational queries (7+ words) trigger AI Overviews at 65.9% rate versus 25.8% overall
Structured data is mandatory: Top pages achieved 97% schema adoption by 2025, with JSON-LD format preferred for AI parsing
EEAT signals drive trust: Experience, Expertise, Authoritativeness, and Trust form the foundation of AI credibility assessment
Real-time tracking essential: 60% of marketers report organic traffic drops from AI answers, requiring comprehensive visibility monitoring across all AI platforms
Brands that want to rank on AI search must optimize for LLMs and Google's AI Overviews alike—mastering structured data, EEAT, and real-time visibility tracking – all critical to rank on AI search today.
Why Does Ranking on AI Search Now Determine Brand Visibility?
The search landscape has fundamentally shifted. 25.8% of U.S. queries trigger an AI Overview, while 60% of searches end without users visiting websites, due to AI Overviews. This zero-click reality means brands must adapt or risk invisibility.
The impact extends beyond Google. Zero-click results hit 65% in 2023 and continue climbing, while AI overviews are reaching 1.5 billion users monthly and appearing in nearly half of all results. Traditional SEO is no longer enough as generative engines like ChatGPT, Perplexity, Gemini, and Bing Copilot will influence up to 70% of all queries by the end of 2025.
These statistics underscore a critical reality: brands that fail to optimize for AI search risk losing the majority of their potential visibility. The urgency is clear—mastering AI search optimization has become essential for maintaining digital presence.
Core Ranking Factors Across AI Engines
AI engines process content differently than traditional search. Google AI Overviews uses the Gemini AI model to consolidate answers from various online sources directly in search results, providing attribution links and occupying more than 75% of a mobile screen's real estate when combined with featured snippets.
The mechanics behind AI visibility are predictable. 75% of AI Overview links are from the top 12 organic rankings, proving that traditional SEO excellence remains foundational. However, three accelerators raise AI Overview likelihood: long queries, informational intent, and trust-heavy verticals. Conversely, two brakes sharply cut it: local/brand modifiers and visual-commerce terms.
Perplexity AI operates differently, with its free version using GPT-3.5, while its paid version leverages various models including GPT-4o, Claude 3.5 Sonnet and Haiku, Sonar Large, and Grok. This diversity in AI models means optimization strategies must be comprehensive rather than platform-specific.
Long-tail informational queries dominate
Query structure dramatically influences AI Overview appearance. The AI Overview rate for queries that combine seven or more words with informational intent is 65.9%, compared to just 25.8% overall. This pattern holds across query types:
Informational queries of all lengths: 39.4% trigger rate
Commercial queries: 22.2%
Transactional queries: 16.5%
Navigational queries: 12%
For informational queries that are 7+ words, that number jumps to 65.9%. This clear preference for longer, information-seeking queries reveals how AI engines prioritize comprehensive answers over simple lookups.
How Do You Engineer Pages for AI with Structured Data?
Structured data has become non-negotiable for AI visibility. Structured data is a way to label and organize the information on your webpages so machines (and AI) can understand it. While not a direct ranking factor, it fundamentally changes how AI systems interpret and surface content.
The evidence is compelling: structured data has a greater impact on the model knowledge of LLMs than previously assumed. Web Data Commons automatically extracts structured data from the Common Crawl, contributing billions of structured statements from several hundred million domains to LLM pre-training.
Top-ranking pages doubled schema usage when ChatGPT launched, improved performance scores by 20% despite pages becoming 43% heavier, and achieved 97% schema adoption by 2025. This dramatic shift proves that structured data has evolved from optional enhancement to mandatory foundation.
Why JSON-LD Wins
JSON-LD is the clear winner for AI optimization. Unlike microdata or RDFa, JSON-LD keeps your structured data separate from HTML, making it cleaner and easier for AI to parse. Using JSON-LD and including schema in your HTML tells crawlers exactly what each piece of content means.
Implementing structured data markup can increase your chances of being cited by AI assistants by over 200%. This dramatic improvement stems from how JSON-LD creates semantic relationships that AI assistants can follow and understand contextually.
Priority schema types for AI visibility include:
FAQ schema for direct answers
Article schema as baseline requirement
HowTo schema with detailed steps and tools
Product schema with nested reviews and offers
Organization schema for credibility signals
What EEAT Signals Do AI Models Trust?
75% of AI Overview links are from the top 12 organic rankings, revealing that AI systems heavily rely on established authority signals. Experience, Expertise, Authoritativeness, and Trust form the foundation of AI credibility assessment, with Trust sitting at the center of this evaluation framework.
Top-ranking pages doubled schema usage when ChatGPT launched, improved performance scores by 20% despite pages becoming 43% heavier, and achieved 97% schema adoption by 2025. These improvements directly correlate with stronger EEAT signals.
Critical EEAT implementation strategies include:
Use external links to credible sources like studies, industry reports, or government data
Implement comprehensive author schema with credentials
Maintain consistent NAP (Name, Address, Phone) across platforms
Build topical authority through comprehensive content clusters
Secure high-quality backlinks from authoritative domains
Google's 2025 Quality Rater Guidelines mark a turning point: AI-generated content is fine, but only if it adds real human value. The guidelines now reward accuracy, expertise, and user-first intent while cracking down on scaled content abuse.
Sites that focus on providing the best content and experience for people and uphold the spirit of these principles are more likely to do well in Google results. This alignment between traditional quality signals and AI trust factors creates a unified optimization strategy.
How Do You Track and Iterate on AI Visibility?
AI visibility tracking is the process of monitoring how your brand or website shows up in AI-generated results. This includes mentions, citations, summaries, and links across features and platforms like Google's AI Overviews, Bing's AI-powered summaries, ChatGPT, Gemini, Perplexity, and more.
The challenge is significant: 60% of marketers say they've seen organic traffic drops due to AI answers. However, there's no single dashboard (yet) that shows where your brand appears across AI-generated results. This fragmentation requires a multi-faceted tracking approach.
Effective AI visibility monitoring requires:
Simulating thousands of buyer questions to reveal AI perception
Tracking mentions across all major AI platforms simultaneously
Monitoring competitor positioning changes in real-time
Analyzing citation patterns and sentiment scores
Measuring share of voice across AI responses
Relixir's analytics engine provides real-time insights into how your brand appears across multiple AI platforms, revealing exactly how AI sees your company. This comprehensive visibility enables rapid optimization based on actual AI behavior rather than assumptions.
Which GEO Platform Features Matter Most in 2025?
Generative Engine Optimization represents a fundamental shift from traditional keyword-based SEO to answer-focused optimization. As traditional SEO effectiveness declines, with zero-click results hitting 65% in 2023, GEO platforms have become essential for maintaining visibility.
When an AI tool mentions a brand in its answer, that brand sees a 38% boost in organic clicks and a 39% increase in paid ad clicks. This multiplier effect makes GEO investment crucial for overall marketing performance.
Essential GEO platform capabilities include:
Autonomous content generation optimized for AI engines
Competitive gap detection across all major AI platforms
Real-time monitoring with proactive alerts
Enterprise guardrails for brand consistency
Integration with existing SEO and content workflows
Relixir stands out as the only platform purpose-built for Generative Engine Optimization, backed by Y Combinator with proven results flipping AI rankings in under 30 days. The platform's ability to transform content strategy through autonomous technical SEO and content generation specifically designed for AI engines sets it apart from traditional SEO tools.
Key Takeaways for Dominating AI Search
The path to AI search dominance requires immediate action on multiple fronts. Relixir is an AI-powered Generative Engine Optimization platform that helps brands rank higher and sell more on AI engines like ChatGPT, Perplexity, and Gemini by revealing how AI sees them, diagnosing competitive gaps, and automatically publishing authoritative, on-brand content.
Businesses implementing GEO strategies have reported a 17% increase in inbound leads within just six weeks. The urgency is clear—with 60% of marketers seeing organic traffic drops due to AI answers, waiting means losing ground to competitors who act now.
Your immediate action plan:
Implement comprehensive JSON-LD schema across all pages
Strengthen EEAT signals through author pages and citations
Create longer, informational content targeting 7+ word queries
Deploy real-time AI visibility tracking across all platforms
Invest in GEO platform capabilities for autonomous optimization
The AI search revolution isn't coming—it's here. Brands that master these fundamentals while leveraging platforms like Relixir will capture the growing share of AI-driven discovery, while those that hesitate will watch their visibility evaporate in the zero-click future.
Frequently Asked Questions
What is AI search and why is it important?
AI search refers to search engines that use artificial intelligence to generate results, such as Google's AI Overviews and ChatGPT. It's important because AI search influences a significant portion of online queries, making it crucial for brands to optimize for these platforms to maintain visibility.
How does structured data impact AI search rankings?
Structured data helps AI systems understand and surface content more effectively. It is not a direct ranking factor but significantly influences how AI interprets and displays information, making it essential for improving AI search visibility.
What are EEAT signals and why do they matter for AI search?
EEAT stands for Experience, Expertise, Authoritativeness, and Trust. These signals are crucial for AI search as they help establish credibility and authority, which AI models rely on to rank content. Strong EEAT signals can improve your chances of appearing in AI-generated results.
How can brands track their visibility in AI search results?
Brands can track AI visibility by monitoring mentions, citations, and links across AI platforms like Google's AI Overviews and ChatGPT. Tools like Relixir provide real-time insights into AI visibility, helping brands optimize their presence based on actual AI behavior.
What role does Relixir play in AI search optimization?
Relixir is a platform designed for Generative Engine Optimization, helping brands improve their AI search rankings by providing insights into AI visibility, diagnosing competitive gaps, and automating content generation tailored for AI engines.
Sources
https://www.webfx.com/blog/seo/where-and-why-google-ai-overviews-appear-2-3m-keywords-study/
https://www.brightedge.com/blog/structured-data-ai-search-era
https://www.brightedge.com/resources/weekly-ai-search-insights/how-seo-changed-in-the-ai-era
https://showupinai.com/blog/advanced-schema-structured-data-for-ai-assistants
https://relixir.ai/blog/choosing-ai-geo-platform-2025-feature-pricing-comparison-enterprises
