---
id: "claim-query-determines-competitive-set"
type: "claim"
source_timestamps: ["§ AI Recommends What It Can Interpret"]
tags: ["research-finding", "query-dynamics"]
related: ["concept-problem-literacy"]
confidence: "high"
testable: true
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"
---
# The user's query determines the competitive set

The researchers found that **exploratory queries generated 95% more brand mentions than goal-oriented queries**, and there was only an **11% overlap** of brands appearing in both types.

This demonstrates that AI assistants build **entirely different competitive sets** based on how consumers articulate their problems. A generic query for "running shoes" produces one set of candidates, while a specific query for "stability shoes for overpronation" produces a completely different set. Therefore, a brand's competitive landscape in AI is fluid and entirely dependent on prompt phrasing — which is precisely why [[concept-problem-literacy|problem literacy]] (shaping the vocabulary consumers use) is a strategic lever.

**Confidence:** high · **Testable:** yes.

> Enrichment note: The 95% and 11% figures rest on the authors' dataset, but the underlying logic is strongly consistent with search and LLM literature: query intent has long reshaped competitive sets (broad informational vs. narrow transactional queries), and LLMs intensify this by pre-filtering options. Research on consumer search costs confirms better-specified preferences yield smaller, differently-grouped consideration sets.


## Related across articles
- [[concept-prompt-driven-optimization]]
- [[claim-search-queries-are-need-based]]
- [[concept-problem-literacy]]