Narrative-Ready Models: Designing for Text Explanations

TL;DR

• Narratives need structure, not just numbers.
• Metadata and driver measures make narratives reliable.

The problem

• AI narratives are generic because the model lacks context.
• Metrics are not annotated with business meaning.

Why it matters

• Narratives are only useful when they reflect business reality.
• Clear explanations reduce manual analyst effort.

Symptoms

• AI outputs vague text like “revenue increased.”
• Narratives omit drivers and caveats.

Root causes

• Low metadata density.
• No driver measures or explanation template.

What good looks like

• Measures include definitions, units, and caveats.
• Driver measures are available for key KPIs.

How to fix

• Add descriptive metadata to measures.
• Define narrative templates for KPIs.
• Use driver measures in explanations.

Pitfalls

• Over‑reliance on AI to “figure it out.”
• Generic narratives without context.

Checklist

• Metadata density improved.
• Narrative templates defined.
• Drivers and caveats included.

Framework placement

Primary ARF layer: Analytical Explainability. Diagnostic bridge: semantic-reliability, execution-reliability, change-reliability.