---
id: "framework-interpretability-elements"
type: "framework"
source_timestamps: ["§ Inclusion—Not Sentiment—Is the Real Competitive Bottleneck"]
tags: ["brand-architecture", "requirements"]
related: ["concept-entity-clarity", "concept-attribute-structure", "concept-evidence-base", "concept-interpretable-brand"]
steps: ["Establish Entity Clarity (consistent identification across sources)", "\\\"Build Attribute Structure (named", "comparable", "measurable features)\\\"", "\\\"Cultivate an Evidence Base (independent", "credible third-party support)\\\""]
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 Three Elements of Brand Interpretability

To become an [[concept-interpretable-brand|interpretable brand]] that AI systems can easily incorporate into recommendations, a brand must possess **three core elements**. Together they ensure that a brand's attributes and evidence can be clearly connected to a user's needs by an artificial reasoning system.

1. **[[concept-entity-clarity|Entity clarity]]** — The brand must be clearly and consistently identifiable across all third-party information sources.
2. **[[concept-attribute-structure|Attribute structure]]** — The product's features must be explicitly named, comparable, and measurable (rather than subjective).
3. **[[concept-evidence-base|Evidence base]]** — The brand's benefit claims must be supported by credible, independent, third-party sources (reviews, clinical data, expert commentary).

These three elements are the *supply side* of interpretability; the *demand side* is [[concept-problem-literacy|problem literacy]]. The execution counterpart is [[framework-build-ai-recall-share|Three Practices to Build AI Recall Share]], and the self-assessment is [[framework-ai-brand-diagnostic|the Simple Diagnostic]].

> Enrichment note: The triad is a synthetically packaged framework but strongly grounded in established information-retrieval and product-data practice — entity resolution (clarity), product taxonomy / attribute modeling (structure), and third-party signal weighting (evidence).