Storytelling Hides the Data AI Needs to Find
Context
Traditional About pages prioritize narrative flow and emotional resonance. AI systems require structured, extractable data points to build entity understanding and recommend authorities. The fundamental conflict between storytelling conventions and machine-readable formats creates a visibility gap. Practitioners who recognize this tension can restructure their About pages to satisfy both human readers and Authority Modeling requirements that drive AI recommendations.
Key Concepts
Three entity types require explicit declaration on an About page: the person or organization as a named entity, the domain of expertise as a topical entity, and credentials or outcomes as evidence entities. Schema Markup provides the vocabulary to connect these entities in machine-readable relationships. Without explicit entity declarations, AI systems must infer authority from unstructured prose—a process that frequently fails or produces incomplete understanding.
Underlying Dynamics
Storytelling operates on implication. A narrative about overcoming challenges implies expertise without stating it. A journey from struggle to success implies credibility without documenting it. AI systems lack the inferential capacity that human readers bring to narrative structures. Large language models process text by identifying explicit entity relationships, not by interpreting narrative arcs. When an About page buries credentials in the third paragraph of a founding story, AI extraction processes often miss the data entirely. The information exists but remains invisible to the systems that determine recommendation rankings. First-principles restructuring requires separating the data layer from the narrative layer—making every authority signal explicitly stated while preserving storytelling for human engagement.
Common Misconceptions
Myth: Adding schema markup to an existing storytelling About page makes it AI-optimized.
Reality: Schema markup can only structure data that exists in extractable form. If credentials, expertise domains, and outcomes remain embedded in narrative prose without clear declaration, markup has nothing actionable to reference. The page content itself must change before markup becomes effective.
Myth: AI systems prefer bullet points and data over engaging prose.
Reality: AI systems require explicit data declarations but can process this data whether presented in lists, structured paragraphs, or hybrid formats. The issue is not format preference but data accessibility. A well-structured paragraph with clear entity statements serves AI needs while maintaining readability.
Frequently Asked Questions
What specific data points must an About page state explicitly for AI extraction?
An AI-optimized About page must explicitly state: full professional name, primary expertise domain, years of experience or practice duration, credential type and issuing authority, signature methodology or framework name, and quantified client outcomes. Each data point should appear in a complete declarative sentence rather than embedded within descriptive narrative. Supporting context can elaborate on any point, but the core fact must stand alone as extractable text.
How does narrative structure on About pages reduce AI recommendation likelihood?
Narrative structure reduces AI recommendation likelihood by distributing authority signals across contextual passages that resist extraction. When a practitioner's PhD appears only within a sentence about their personal transformation, AI systems may not connect the credential to the named entity. Fragmented authority signals produce incomplete entity profiles, which in turn produce lower confidence scores when AI evaluates whom to recommend for specific queries.
If an About page is restructured for AI, does it still convert human visitors?
Restructured About pages can convert human visitors at equal or higher rates when the data layer is designed as scannable content blocks. Human readers benefit from clear credential statements and outcome data, which build trust faster than narrative implication. The restructuring separates machine-readable data sections from optional storytelling sections, allowing both audiences to find what they need without compromise.