Analytical Readiness Framework (ARF)
A practical framework for reliable AI answers in BI
The Analytical Readiness Framework (ARF) is a product‑agnostic standard for making AI answers consistent, explainable, and safe across analytics systems. It focuses on the model and semantics that AI relies on—regardless of which LLM you use.
Why this matters
AI can generate language, but BI answers depend on data models, definitions, and context. When those foundations are weak, AI produces inconsistent numbers, unstable explanations, and answers that drift across tools. ARF helps teams remove ambiguity so AI can reason deterministically.
A stack from semantic integrity to AI readiness and interoperability.
User asks a question, model returns context, LLM answers with drivers and caveats.
The four layers
Semantic Integrity
Ensures every metric and term has one clear meaning, so AI and humans compute the same truth.
Context Stability
Keeps filter context predictable so the same question produces the same answer every time.
Analytical Explainability
Makes answers auditable and reasoned: not just numbers, but explanations and drivers.
AI Readiness & Interoperability
Ensures the model is AI‑readable, well‑governed, and compatible with different AI tools.
Common symptoms
- • The same question returns different numbers across tools.
- • AI answers change between runs even when data is stable.
- • Explanations lack drivers or cite the wrong dimensions.
- • Teams debate definitions instead of insights.
How to use this framework
- 1. Start with the layer that matches your most visible failure mode.
- 2. Use the layer checklist and metrics to establish a baseline.
- 3. Fix high‑impact gaps and re‑test with deterministic questions.
- 4. Repeat across layers to improve consistency and explainability.
Model‑agnostic by design
ARF applies regardless of which LLM or assistant you use (ChatGPT, Claude, Gemini, Copilot, or others). The framework focuses on the data model and semantics—factors that drive answer quality across all tools.
Start‑here knowledge base articles
Semantic Integrity
Canonical Metrics: One Definition, Many Views
Semantic Integrity
Why Multiple Measures for the Same Metric Break AI Answers
Semantic Integrity
Units, Currency, and Time: The Hidden Semantics That Cause Bad Answers
Context Stability
Filter Context in Plain English
Context Stability
Why AI Answers Change When Your Data Didn’t
Context Stability
Many-to-Many Relationships and AI: What Can Go Wrong
Analytical Explainability
Your Model Can Calculate, But Can It Explain?
Analytical Explainability
Lineage: Tracing a Number Back to Its Sources
Analytical Explainability
Contribution Analysis: Turning Totals Into Reasons
AI Readiness & Interoperability
Metadata Density: Why Descriptions Matter More Than You Think
AI Readiness & Interoperability
Semantic Contracts: Setting Expectations for Questions and Answers
AI Readiness & Interoperability
Grounding: Preventing Confidently Wrong Answers
Framework FAQ
Need clarification on scope, LLM differences, or how to start small?
Read the FAQ