Original Research

State of AI Search 2026

Akravo tracked AI citation patterns across 65+ brands and 7 platforms over 6 months. This report contains the data, the patterns, and the playbook. No theory. Just what we measured.

Based on data from 65+ client engagements
Covering 7 AI platforms
Q1 2026 data

What the data shows

Six findings from 6 months of tracking how AI platforms cite, recommend, and ignore brands.

01

78% of AI citations come from just 12% of a brand's content pages

02

Brands with complete schema markup get 3.2x more AI citations than those without

03

The average time from first optimization to first AI citation is 34 days

04

Perplexity cites sources 4x more frequently than ChatGPT

05

Brands cited in 3+ authoritative sources are 89% more likely to be recommended by AI

06

Only 14% of brands have an LLMs.txt file, but those that do see 2.1x more citations

What's inside the report

7 chapters, 42 pages, all built on first-party data from real client engagements.

01

The AI Search Landscape in 2026

Market size, platform adoption rates, and the shift from search to synthesis.

02

How Each Platform Selects Sources

Platform-by-platform breakdown: ChatGPT, Perplexity, Gemini, Google AIO, Bing Copilot, Claude, DeepSeek.

03

What Gets Cited: Content Patterns That Win

Word count, structure, formatting, and topical patterns that correlate with higher citation rates.

04

Entity Authority: The New PageRank

How cross-source mention density replaced backlinks as the primary authority signal for AI.

05

Schema Markup Impact on AI Citations

Measured impact of Organization, FAQPage, HowTo, and Product schema on citation likelihood.

06

Industry Benchmarks

Benchmarks across SaaS, eCommerce, Fintech, and Legal, broken out by company size.

07

The 90-Day Playbook: From Invisible to Cited

Step-by-step plan based on what worked for the 65+ brands in our dataset.

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