Analytical Explainability
Narrative-Ready Models: Designing for Text Explanations
Narrative‑ready models provide the context and structure AI needs for clear explanations.
TL;DR
- • Narratives need structure, not just numbers.
- • Metadata and driver measures make narratives reliable.
The problem (layman)
- • 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 (steps)
- • 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.