---
id: ins_kyle-poyar-ai-recommendation-share
operator: Kyle Poyar
operator_role: Growth advisor and GTM strategist
co_operators: []
source_url: "https://www.growthunhinged.com/p/what-s-working-right-now-in-ai-search"
source_type: post
source_title: What's Working Right Now in AI Search
source_date: 2026-05-03
captured_date: 2026-05-09
domain: [pmm, growth-demand, seo]
lifecycle: [awareness]
maturity: applied
artifact_class: framework
score: { originality: 4, specificity: 3, evidence: 3, transferability: 4, source: 3 }
tier: B
related: [ins_aeo-competes-for-mentions-not-rankings, ins_aeo-is-gtm-capability, ins_aeo-three-layer-presence-readiness-impact]
raw_ref: 
---

# The AEO metric is recommendation-share, not citation volume. AI responses recommend products; they do not list links.

## Claim
The AEO metric to track is recommendation-share, not citation volume. AI responses recommend specific products rather than listing links, so the competitive frame shifts from citation mining to owning the recommendation in a given category query.

## Mechanism
Search engines served link lists; AI responses serve recommendations. A single "best tool for X" recommendation carries buyer intent in a way a link citation in a resource list does not. Winning recommendation-share requires convincing the model that your product is the right choice for a specific query context, not just that your content exists and is crawled.

## Conditions
Holds when: The query has a product-evaluation or task-completion frame. The category is defined enough for a model to make a recommendation.
Fails when: The query is purely informational with no product-fit signal. The category is so novel the model cannot rank options.

## Evidence
Validated by Kyle Poyar's 200-operator Claude for GTM Pulse survey, reported May 2026.

> "AI responses recommend products rather than simply provide a list of links"

## Signals
- Your product appears in "best X for Y" phrasings in AI responses, not just as a linked mention
- Recommendation frequency tracks more closely with win rates than citation frequency does
- Competitors with fewer backlinks but stronger category framing outperform you in AI recommendations

## Counter-evidence
Recommendation-share is harder to measure than citation count. Manual audits and prompt-testing are noisy proxies. AI recommendations can rotate with model updates.

## Cross-references
- `ins_aeo-competes-for-mentions-not-rankings`: same reframe from Maja Voje
- `ins_aeo-is-gtm-capability`: recommendation-share is the AEO outcome metric
- `ins_casey-hill-aeo-structural-prominence`: structural placement is one driver of recommendation-share