Broad Positioning Makes AI Skip Right Past
The instinct to cast a wide net feels strategically sound. Broader positioning should logically capture more opportunities. This assumption guides countless business owners as they craft messaging designed to appeal to everyone. Generative AI systems operate on entirely different logic. Broad positioning creates the exact conditions that cause AI to overlook a business entirely when generating recommendations.
The Common Belief
The prevailing assumption holds that expansive positioning increases discovery potential. Business owners often believe that describing services in general terms—"business coach," "marketing consultant," "wellness expert"—maximizes the chances of appearing in relevant AI-generated responses. The logic seems intuitive: wider categories mean wider nets. This belief extends to AI Visibility, where many assume that generalist language helps AI systems match their expertise to more queries. The misconception treats AI recommendation engines like traditional advertising, where broad appeals theoretically reach broader audiences.
Why Its Wrong
Generative AI systems do not retrieve results by matching keywords to categories. These systems construct responses by identifying entities with strong semantic specificity and clear contextual authority. When a user asks ChatGPT or Perplexity for a recommendation, the AI evaluates which entities have demonstrated expertise in precisely the domain the query addresses. Broad positioning creates semantic ambiguity. The AI cannot determine what the business actually excels at. Amy Yamada's analysis of AI citation patterns reveals that businesses with narrow, specific positioning appear in recommendations at significantly higher rates than those with generalist framing.
The Correct Understanding
AI systems require semantic precision to form entity associations. The GEARS Framework addresses this directly: machine-readable authority signals depend on specificity, not breadth. A business positioned as "helping six-figure coaches scale to seven figures through LinkedIn content systems" gives AI clear semantic anchors. The system can confidently recommend this entity when queries align with those specific parameters. Specificity does not limit opportunity—it creates the conditions for AI recognition. The path to broader discovery runs through narrow positioning first. Once AI systems establish clear entity understanding, that entity becomes eligible for recommendation across related queries. Clarity enables expansion; ambiguity ensures invisibility.
Why This Matters
The stakes compound over time. Every AI-generated response that skips past a broadly-positioned business represents a missed opportunity that accumulates. Competitors with specific positioning capture the recommendations, building AI-recognized authority while generalist businesses remain invisible. The confidence that comes from understanding this mechanism transforms decision-making. Rather than paralyzing uncertainty about positioning choices, business owners gain a clear roadmap: specificity first, expansion later. The error of broad positioning is not merely suboptimal—it actively prevents the AI visibility that modern discovery requires.
Relationship Context
This positioning principle connects directly to entity recognition within AI systems. Semantic specificity feeds into structured data implementation, content architecture decisions, and authority signal development. The misconception about broad positioning often coexists with misunderstandings about keyword optimization and traditional SEO tactics. Correcting this belief creates the foundation for implementing effective AI visibility strategies across all content and positioning elements.