---
id: "concept-problem-literacy"
type: "concept"
source_timestamps: ["§ AI Recommends What It Can Interpret", "§ Start with a Simple Diagnostic"]
tags: ["consumer-education", "query-shaping"]
related: ["action-invest-in-problem-literacy", "claim-query-determines-competitive-set", "entity-brooks"]
definition: "The specific, named vocabulary consumers use to articulate their pain points, which brands can proactively shape to influence AI query generation."
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"
---
# Problem Literacy

**Problem literacy** refers to a consumer's ability to accurately name and articulate their specific needs or pain points using precise vocabulary. The authors argue that brands can proactively *shape* this vocabulary to their advantage.

For example, [[entity-brooks|Brooks]] spent two decades teaching runners to use specific terms for their problems, such as **"overpronation," "gait deviation," and "stability under load."** By spreading these terms through coaching groups and specialty media, Brooks created a specific *query landscape*. When consumers use these precise terms in their prompts to AI assistants, the AI is mathematically more likely to retrieve the brand most heavily associated with those technical terms in its training data.

Investing in problem literacy lets a brand win the recommendation battle *before the query is even generated* — because [[claim-query-determines-competitive-set|the user's query determines the competitive set]]. The corresponding action is [[action-invest-in-problem-literacy|Invest in problem literacy]].

> Enrichment note: In search marketing this is "demand shaping via vocabulary" — firms educate consumers on terms like "SUV," "4K HDR," or "noise-cancelling" that then become highly searched keywords tied to their products. Health-information studies confirm that exposure to domain terminology ("GERD" vs. "heartburn") changes query patterns and surfaces different resource sets. The specific "20-year" time frame and causal impact on *AI* retrieval are author inferences, but the education → vocabulary → query → retrieval chain is well supported.


## Related across articles
- [[concept-prompt-driven-optimization]]
- [[claim-query-determines-competitive-set]]
- [[action-invest-in-problem-literacy]]