Eflot, a digital marketing and AI search visibility company, has published a research report examining how ChatGPT, Perplexity, Gemini and Google AI Overviews select and cite sources for commercial buyer queries.

The report synthesises findings from named studies published between 2024 and 2026, covering more than 680 million AI citations, together with patterns identified through
Eflot’s page-level AI visibility audits. It provides a practical framework for brands developing search engine optimisation, answer engine optimisation, and generative engine optimisation strategies.

AI Is Changing How Buyers Research Vendors

AI platforms are becoming an important part of commercial research. Forrester’s 2026 Buyers’ Journey Survey of 18,000 global business buyers found that 94% used AI during their most recent purchase process.

A G2 study of 1,076 buyers found that 51% begin vendor research in an AI chatbot more often than in Google, while 71% use an AI chatbot at some stage of the buying journey.

These findings indicate that brands may influence buyer shortlists through AI-generated answers before prospects visit a website or contact a sales team.

Search Rankings Do Not Determine AI Citations

Eflot’s report finds that traditional Google rankings and AI citations do not follow the same source-selection pattern.

A 2026 Moz analysis of 40,000 queries found that 88% of sources cited in Google AI Mode ranked outside the organic top 10. Ahrefs reported that AI-cited pages had an average age of 1,064 days, compared with 1,432 days for conventional organic search results, suggesting that content freshness can influence source selection.

The Princeton University and Georgia Tech study presented at KDD 2024 tested nine content changes across 10,000 queries. It found that adding statistics, credible quotations, and source citations increased generative engine visibility by 30% to 40%, with lower-ranking pages recording some of the greatest improvements.

The Retrieval, Evidence and Corroboration Model

Eflot organises the findings into a three-part framework for improving visibility across AI search engines:

- Retrieval: A page must directly address the main query and related subquestions to enter an AI engine’s candidate source set.

- Evidence: Clear definitions, statistics, comparisons, quotations, and step-based information make content easier to extract and cite.

- Corroboration: Independent media coverage, expert references and third-party sources help AI systems validate commercial claims.

Muck Rack’s analysis of more than 1 million prompts found that over 85% of non-paid citations produced by AI systems originated from earned media sources. This makes digital PR and independent brand mentions especially relevant to generative engine optimisation.

What the Findings Mean for Brands

Forrester found that 61% of the B2B buying journey is completed before buyers contact a provider. If a brand is absent from AI-generated recommendations, it may lose influence during the early research and comparison stages.

AI visibility may also be difficult to measure through website analytics because users can consume an answer without clicking the cited source. Eflot recommends that brands evaluate both traditional SEO performance and their presence across AI-generated answers.

“Traditional authority still matters, but it does not decide AI citations on its own,” said Ashok Veda, co-founder and director of Eflot. “AI engines need content they can retrieve, interpret, and verify. Specialist brands can compete for visibility by publishing clear facts, supporting claims with evidence and earning independent references.”

Businesses can visit eflot.com to learn more about AI search visibility audits, SEO, answer engine optimisation, and generative engine optimisation.