AI Visibility Studies
Original research and analysis on how brands, products and sources appear across AI-led buyer journeys.
AI visibility is still an emerging category. These studies use Odyssiant tracker data to explore how AI engines answer buyer research questions, which sources they cite, when brands are mentioned, and where products disappear from the journey.
The Not Mentioned Problem
Across 3,903 product-level AI answers in the May 2026 tracker, 61% did not mention the product being tested. This study explains why product absence — not weak positioning — is the first AI visibility gap marketers need to solve.
More studies
What Sources Do AI Answers Cite?
Which sources AI engines cite during buyer research, and why AI visibility depends on more than website content.
Product Visibility vs Brand Visibility
Why brand-level AI visibility can hide commercial gaps at product, service-line and practice-area level.
The AI Buyer Journey Visibility Gap
How visibility changes across awareness, consideration, evaluation and decision-stage prompts.
What AI Search Means for Marketing Teams
How marketers should adapt content, PR, proof and third-party source strategy as buyers move into AI-led discovery.
Do AI Engines Recommend the Same Brands?
Why AI visibility changes across ChatGPT, Perplexity, Gemini and Claude.
The Proof Gap in AI Search
Why AI-generated answers increasingly depend on evidence, validation and third-party sources.
Coming next
Coming in the next quarterly research release
As the Odyssiant AI Search Tracker collects more data, our next studies will look more closely at source trust, cross-engine recommendation patterns and the role of proof in AI-led buyer discovery.
Which Sources Do AI Engines Trust Most?
A deeper analysis of the source types shaping AI answers, from owned websites and trade media to Reddit, Wikipedia, reviews and directories.
Frequently asked questions
Common questions about Odyssiant's AI Visibility Studies and how to read the findings.