The umbrella concept covering search experiences powered by AI, including Google AI Overviews, SearchGPT, Perplexity, and other systems that generate synthesized answers.
AI Search refers to the broad category of search experiences where artificial intelligence generates synthesized answers from multiple sources rather than simply returning a ranked list of links. This includes Google's AI Overviews, OpenAI's SearchGPT, Perplexity, Microsoft Copilot in Bing, and other systems that use large language models to understand queries and compose responses drawn from web content.
Why It Matters for AI SEO
AI search represents a fundamental shift in how users discover and consume information online. Traditional search optimization focused on earning a top-ten ranking for target keywords. AI search changes the game by synthesizing answers directly, often citing only a handful of sources. This means the competitive landscape narrows dramatically: instead of ten organic positions, there may be only three to five cited sources in an AI-generated response. The proliferation of AI search platforms also fragments the search space. Users no longer rely exclusively on Google. Perplexity handles millions of queries daily, ChatGPT's browsing mode serves as a search engine for its users, and specialized AI search tools are emerging across verticals. SEO professionals must now optimize for multiple AI-powered discovery channels, not just Google's traditional algorithm.
How It Works
AI search systems typically follow a retrieve-then-generate pattern. When a user submits a query, the system first retrieves relevant documents from the web or an indexed corpus using a combination of traditional search ranking signals and semantic similarity via vector databases. The retrieved documents are then passed to a large language model, which synthesizes a coherent answer, selecting information from multiple sources and attributing claims to specific URLs. Different platforms implement this pattern with varying approaches. Google AI Overviews use Google's existing search index and ranking signals, meaning traditional SEO factors still influence which content gets cited. Perplexity performs real-time web searches and tends to favor recent, well-structured content with clear factual claims. SearchGPT uses OpenAI's models combined with web browsing to generate responses with inline citations. The quality of citations varies across platforms. Some AI search engines provide prominent source links that drive meaningful referral traffic, while others bury citations in footnotes or expandable sections. Understanding each platform's citation behavior helps prioritize optimization efforts.
Practical Applications
Optimizing for AI search requires a multi-platform strategy. Start by monitoring your brand's presence across AI search platforms. Query your target topics on Perplexity, ChatGPT, and Google AI Overviews to see whether your content is being cited. Track which competitors appear and analyze what makes their content citable. Structure content for AI retrieval by using clear headings, concise definitions, and well-organized factual claims. AI systems favor content that provides direct, authoritative answers over content that buries answers in lengthy introductions or relies heavily on subjective opinions. Build authority signals that AI systems recognize. Original research, expert authorship, comprehensive topic coverage, and strong E-E-A-T indicators increase the likelihood of being selected as a cited source. Focus on creating content that serves as a definitive reference for your target topics, as AI systems prioritize sources they can confidently attribute factual claims to. Invest in answer engine optimization as a dedicated practice alongside traditional SEO. The skills and strategies overlap significantly, but AI search optimization requires specific attention to content structure, citability, and multi-platform visibility that goes beyond traditional ranking factors.