AI search refers to search engines and tools that generate synthesized answers to questions instead of showing a ranked list of links. Rather than ten blue links from which you choose, AI search reads multiple sources and assembles a personalized answer in real-time — often with references to the sources used.
The major AI search platforms (2026)
Perplexity. A search engine that provides answers with source attribution. Perplexity cites sources in 97% of answers, making it the most transparent AI search platform for measuring content visibility (Otterly.AI, 2025). It combines real-time web search with synthesis by a language model.
ChatGPT with browsing. OpenAI’s ChatGPT can search the web in real-time when answering questions. It cites sources in approximately 16% of answers (Otterly.AI, 2025). The lower citation percentage is because ChatGPT also relies heavily on pre-trained knowledge.
Google AI Overviews. Google’s integration of AI-generated summaries at the top of search results pages. When AI Overviews are triggered, Google Gemini writes an answer based on multiple sources and displays it above traditional organic results. Because this integrates seamlessly with Google Search, which has held a near-monopoly on search for 20 years, the step to using Google AI Overviews is practically seamless in practice.
Gemini. Google’s standalone AI assistant, accessible independently of Google Search, which combines language model capabilities with access to Google Search data. Gemini can search the web and provide answers with source attribution, similar to Perplexity.
How AI search selects sources
AI search platforms use retrieval-augmented generation (RAG): they first search the web for relevant content, then use a language model to synthesize the found information into a coherent answer. The selection process favors content that:
- Is factually precise. Passages with specific data points, named sources, and verifiable claims are retrieved and cited more frequently.
- Is clearly structured. Content with descriptive headings, direct answers to questions, and logical flow is easier for AI systems to process.
- Comes from recognized entities. Websites and authors that consistently appear across multiple trusted sources — with schema markup, Wikidata entries, and cross-platform presence — are treated as more authoritative.
- Is current. Content with recent publication or update dates carries more weight, especially for topics where information changes rapidly.
Reddit is a notable source: large language models cite Reddit 3,212 times versus Wikipedia’s 961 citations (Otterly.AI, 2025). This suggests that authentic, experience-driven content from real users carries weight in AI search.
How AI search differs from traditional search
In traditional search, the goal is to rank as high as possible in a list of links. Users click through to your site, and success is measured by how many users do that and whether they convert. In AI search, the goal is to be selected as a source in the generated answer. Users may never visit your site directly — they read the AI-generated answer that references your content. Success is then measured by how often you’re cited, how that happens, and whether it leads to leads. This shift has implications for content structure. Traditional SEO rewards extensive, long-form content that keeps users on the page. GEO rewards concise, citable passages that AI models can find in your content in milliseconds.
The impact on B2B marketing
For B2B marketers, AI search transforms the discovery phase for customers early in their customer journey. Decision-makers increasingly use AI tools to research solutions, compare vendors, and understand industry concepts. Being cited in an AI-generated answer can influence purchasing decisions before a prospect ever visits your website. This makes entity optimization and LLMO strategic priorities, not just technical SEO tasks.
Related concepts
- GEO (Generative Engine Optimization) — optimizing content to be cited in AI search results
- LLMO (Large Language Model Optimization) — the broader discipline of optimizing for language models
- Entity Optimization — making brands and people recognizable to AI systems
- Knowledge Graph — structured data that AI uses to understand relationships between entities
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Last updated: March 2026 | Hands on GEO