Introduction to AI Search Platforms
New research from BrightEdge reveals that Google AI Overviews, AI Mode, and ChatGPT recommend different brands nearly 62% of the time. This suggests that each AI search platform interprets data in unique ways, indicating different approaches to thinking about each AI platform. The study used BrightEdge’s AI Catalyst tool to analyze tens of thousands of queries across the three platforms.
Methodology and Results
The analysis documented a 61.9% overall disagreement rate, with only 33.5% of queries showing the exact same brands in all three AI platforms. Google AI Overviews averaged 6.02 brand mentions per query, compared to ChatGPT’s 2.37. Commercial intent search queries containing phrases like "buy," "where," or "deals" generated brand mentions 65% of the time across all platforms. This implies that high-intent keyword phrases remain reliable for ecommerce, similar to traditional search engines. E-commerce and finance verticals achieved 40% or more brand-mention coverage across all three AI platforms.
Divergence Among AI Platforms
The three AI platforms often disagreed on brand recommendations for identical queries. BrightEdge’s research highlights the following key points:
- ChatGPT cites trusted brands even when it’s not grounding on search data, indicating reliance on LLM training data.
- Google AI Overviews cites brands 2.5 times more than ChatGPT.
- Google AI Mode cites brands less often than both ChatGPT and AIO.
The research indicates that ChatGPT favors trusted brands, Google AIO emphasizes breadth of coverage with more brand mentions per query, and Google AI Mode selectively recommends brands.
Understanding the Differences
The split across the three platforms is not random. BrightEdge asserts that this difference is due to "authority" signals within ChatGPT’s underlying LLM. However, an alternative explanation suggests that the LLM is simply reaching for the entity (brand) related to a topic, based on frequency, prominence, and contextual embedding strength in the training data. If a brand appears widely in appropriate contexts within the training data, it is more likely to be generated as a brand mention by the LLM.
Patterns and Triggers
The research data reveals unique patterns across all three platforms that can behave as brand citation triggers. One pattern shared by all three is that keyword phrases with high commercial intent generate brand mentions in nearly two-thirds of cases. Industries like e-commerce and finance achieve higher brand coverage, reflecting the ability of all three platforms to accurately understand strong commercial intents for keywords inherent to those verticals. Comparison queries for "best" products generate 43% brand citations across all three AI platforms, again reflecting the ability of those platforms to understand user query contexts.
Citation Network Effect
BrightEdge introduces the concept of a citation network effect, where earning citations in one platform could influence visibility in the others. A well-crafted piece of content could earn authority mentions on ChatGPT, generate competitive mentions on Google AI Overview, and secure selective placement on Google AI Mode. The citation network effect means that earning mentions on one platform often creates the validation needed for another.
Optimizing for Traditional Search
Traditional SEO remains the foundation for building visibility in AI search. BrightEdge’s data indicates that this is directly effective for AIO and has a more indirect effect for AI Mode and ChatGPT. ChatGPT can cite brand names directly from training data and from live data, suggesting that generating strong brand visibility tied to specific products and services may be helpful.
Conclusion
The emergence of AI-native brand discovery is creating new opportunities for businesses to build brand awareness in the topics they want to be surfaced in. The brands winning this transition are not necessarily those with the biggest SEO budgets or the most content, but rather those recognizing that AI disagreement creates more paths to visibility. Understanding platform-specific triggers is crucial for capturing comprehensive brand visibility, and smart brands are already learning to work with the 62% disagreement gap to their advantage.