AI Overviews (AIOs) and AI-powered search are reshaping which sources and formats surface in response to queries. Pew Research data shows that click-through rates drop from 15% on standard search pages to just ~8% when AI summaries appear. Only ~1% of users click links inside the AI box. Knowing where AI answers are pulling from in your category can guide investment towards the formats and entities most likely to be cited. Leaders should also run their own “who gets cited” analysis, since external reports give broad direction but not category-specific insights.
AI is not just summarising information; it is actively shaping which sources users see and trust. Studies show that AI Overviews disproportionately highlight user-generated content (UGC) platforms like Reddit, structured domains like Wikipedia, and even government (.gov) sites. This shift creates new winners and losers in visibility, with downstream effects on traffic, leads, and brand authority. For leaders, the critical question is: how do you ensure your business is among the cited sources? The answer lies in tracking citations, aligning with the right content formats, and structuring data for AI systems to recognise and reference.
AI-driven results no longer mirror traditional Search Engine Results Pages (SERPs). Pew Research reveals that AI summaries cite Wikipedia, Reddit, YouTube, and .gov links more frequently than standard results. For businesses, this means category-specific insights into which formats win, such as tutorials, technical documents, specifications, or government co-citations, can directly influence strategy. Without this visibility, brands risk losing ground to competitors and third-party platforms.
To measure visibility in AI Overviews, inputs include your target keyword set and daily snapshots of cited URLs across priority queries. The outputs should show:
These insights reveal who is shaping AI answers and where your brand is absent.
1. Collect and Classify: Use tools like SerpAPI or DataForSEO to extract citations. Auto-classify them by domain type (e.g., UGC, vendor, media) and content format. This builds a baseline picture of who is winning in your category.
2. Entity Alignment: Map each cited page to entities such as company, product, or schema type. Check whether your brand has a Knowledge Graph presence and structured data parity with competitors. This step ensures AI systems can “see” your product as a legitimate entity.
3. Overlap and White-Space Analysis: Compare your formats against those being cited. For example, if Reddit FAQs are surfacing but your brand has no official FAQ, that is a missed opportunity. Identifying these gaps highlights exactly where to act.
4. Action Plan Execution: Target formats and entities that AI prefers, such as how-to content, official specs, tools, or standards-compliant pages. Publish “entity-clean” pages, using structured data (Schema.org), canonical “entity home” pages, and clear product schemas to boost visibility.
Let’s take a hypothetical example. Suppose you are tracking 120 “data integration” queries over a 60-day period. In your own log analysis, you might find vendor documentation making up around 40% of citations, GitHub/Wikipedia references around 20%, YouTube explainers about 10%, and .gov standards close to 5%, with the remainder spread across media and community sources.
These figures are purely illustrative, but highlight Pew’s larger study: UGC and .gov content are consistently lifted. A practical solution is to create specification-style pages and guided tools, apply SoftwareApplication/WebApplication schema, and establish a canonical “entity home” page for your product.
AI Overviews are rewriting the rules of digital visibility. Evidence shows that AI Overview citations disproportionately reward structured formats, official specs, UGC, and .gov domains. Leaders should act now by building a rolling “who gets cited” report for their category, closing format gaps, and investing in structured, entity-aligned content. The winners in AI-driven search will be those who make themselves most legible to the algorithms shaping user journeys.
Entity alignment: Ensuring your brand or product exists as a distinct entity, through Knowledge Graph recognition, structured data, and a canonical “entity home,” so AI systems can confidently cite it.