---
id: "concept-ai-recall-share"
type: "concept"
source_timestamps: ["§ Three Practices to Build AI Recall Share"]
tags: ["marketing-metrics", "kpi", "ai-discovery"]
related: ["concept-share-of-model", "concept-interpretable-brand", "claim-inclusion-is-bottleneck", "framework-build-ai-recall-share"]
definition: "A metric measuring how reliably a brand is retrieved by an AI system as a candidate solution when its attributes match the user's specific problem."
sources: ["geo"]
sourceVaultSlug: "hbr-seg-geo"
originDay: 3
articleStem: "hbr-new-25-get-ai-to-surface-your-brand"
sourceUrl: "https://hbr.org/2026/06/how-to-get-ai-to-surface-your-brand"
sourceTitle: "How to Get AI to Surface Your Brand"
---
# AI Recall Share

**AI recall share** is proposed as a new, critical metric for marketing executives in the era of AI-mediated product discovery. Traditionally, marketers focused on *market share* (what consumers buy) and *mind share* (what consumers think about). AI recall share measures how often a brand is retrieved as a candidate by an AI system **when the brand's attributes actually fit the user's articulated problem**.

This metric emphasizes *fit* over mere exposure. When a user queries an AI assistant (e.g., "running shoes for knee pain"), the system identifies implied requirements and recalls brands whose attributes match. Brands are no longer competing just to be remembered by human consumers; they are competing to be retrieved by the artificial decision-makers that shape the initial consideration set. High AI recall share is the direct result of high brand interpretability — see [[concept-interpretable-brand|Interpretable Brand]].

It is deliberately contrasted with [[concept-share-of-model-d25|share of model]] (Dubois, Dawson & Jaiswal), which captures raw frequency of appearance without accounting for problem-solution fit. Because competition is decided upstream at the retrieval stage (see [[claim-inclusion-is-bottleneck|Inclusion, not sentiment, is the bottleneck]]), AI recall share is the outcome metric marketers should optimize. The operating playbook is [[framework-build-ai-recall-share|Three Practices to Build AI Recall Share]].

> Enrichment note: "AI recall share" is a novel but sensible extension of the broader movement from pure exposure metrics (share of voice) toward relevance-based metrics (search quality score, recommendation relevance rank). It parallels the rise of "share of search" as a leading indicator of brand health and tightens "share of model" by conditioning on attribute-problem fit.


## Related across articles
- [[concept-share-of-model-d10]]
- [[concept-share-of-model-d25]]
- [[concept-mention-rate]]