When you ask ChatGPT or Gemini a question, the answers come with links to its sources, known as citations. Those citations go to news stories, research reports, and brand content.
We recently published the 2026 AI Trust Report, which analyzed more than 3.9 million AI citations across six AI platforms, using data from our partner Scrunch, to understand how often brand content is surfaced in AI search.
The answer was that it gets cited quite a lot. AI platforms appear to treat high-quality brand content as at least as authoritative as journalism. Brand sites were cited in 29% of the responses we tracked, while editorial sites were cited in 21%.
But which content types get cited most often? We know that authority signals play a large role, and we can't say for certain how much of a role framing and angles play on their own. Still, we noticed a few patterns when looking at the pieces that tended to get cited most often.
Here are four content types that performed well in our research. (All four of the examples we examine below were published on the brands' own domains, so that we could look at angles and structure separately from distribution.)
This story type doesn't rely on a specific news event to be relevant. It provides a clear, structured answer to questions like "What is X, and how does it work?" It helps consumers understand complicated topics or make more educated decisions. This was the most common format in our analysis of the most-cited stories — nearly two-thirds of the top 100 stories could be classified as explainers.
This Experian story that explains the pros and cons of debt consolidation is a strong example of that structure.
Looking at both the benefits and drawbacks gives the piece a balanced perspective, while the pros-and-cons table is easy for readers to scan and for LLMs to extract. The "In this article" box on the right side anticipates possible follow-up questions and provides a clear roadmap.
These stories answer questions like "What are the best X?" or "Where is the cheapest Y?" AI tends to cite them because they use proprietary or compiled data to create a finding that doesn't exist anywhere else. When AI needs to answer a question like these, it turns to a source that uses a sound methodology and shows its work.
Take this report on the cheapest places to live in the U.S. in 2026, published by AmeriSave, as an example.
AmeriSave relied on a number of authoritative sources to assemble its report, including the U.S. Census Bureau, U.S. News, Realtor.com, and SmartAsset. Its "Key Takeaways" section at the top quickly answers which cities are cheapest, and an FAQ at the bottom answers the most likely follow-up questions. Its frequent use of "in 2026" also signals to AI engines that this content is fresh and up to date.
Primary research in the form of a survey or original analysis gives LLMs a fresh dataset to cite. If the brand is the data owner, its content immediately becomes more authoritative than if the data came from a third party.
Intuit QuickBooks' 2026 Small Business Late Payments Report is an example.
This content type helps readers understand the difference between several options, directly answering the prompts that users enter when making important decisions.
This BriteCo piece that compares homeowners insurance for jewelry against dedicated jewelry insurance does just that.
The page provides a complete response to a very narrow question. It does that in several ways: with a pros-and-cons table, a side-by-side comparison, and more in-depth responses to common questions. It includes specific, quotable numbers and definitions for the jargon someone might encounter.
Across all of the stories we analyzed, the ones cited most often were educational and specific. They explained complicated topics in depth, introduced new datasets or took a fresh angle on existing data, and helped readers make decisions.
Whether the subject was insurance, debt, or the housing market, they established authority by providing a wealth of information in an easy-to-read structure.
For content teams, this means that optimizing for AI includes thinking about how a story will be found as well as how it will be read. Headlines should mirror how people might phrase a question, and numbers should be well-sourced.
And because authority signals matter in addition to structure, where a story appears is important, too. A well-structured story that is balanced and genuinely helpful to readers is more likely to be picked up by the high-authority publishers that AI tends to trust. Stacker's research has found that distribution creates citations that wouldn't otherwise exist, on top of the ones a brand site earns on its own.
Stacker's analysis looked at citations from across six AI platforms: ChatGPT, Claude, Google AI Mode, Google AI Overviews, Google Gemini, and Perplexity. The analysis was based on prompts that Stacker tracked via the AI visibility platform Scrunch between May and August 2026. Because these prompts were tracked on behalf of Stacker's clients, they tended to skew towards personal finance and health topics.
Download the 2026 AI Trust Report here.
Madeline Stone is the Content Manager for Editorial & Insights at Stacker. She was previously a longtime business and tech journalist at Business Insider and a content and communications consultant for tech startups.