AI Readiness & Interoperability
An AI Readiness Scorecard You Can Run Monthly
A simple scorecard tracks progress across metadata, context, and explainability.
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Measure, score, review, and improve in a monthly loop.
TL;DR
- • Use a monthly scorecard to track readiness.
- • Focus on measurable improvements.
The problem (layman)
- • Teams don’t know if AI readiness is improving.
- • Efforts are reactive instead of planned.
Why it matters
- • A scorecard creates accountability and momentum.
- • Progress becomes measurable and repeatable.
Symptoms
- • No clear baseline for readiness.
- • Improvements are inconsistent.
Root causes
- • No defined readiness metrics.
- • Lack of ownership for improvement.
What good looks like
- • Monthly scores across layers.
- • Clear targets and trend tracking.
How to fix (steps)
- • Define readiness metrics for each layer.
- • Track monthly changes.
- • Tie initiatives to score improvements.
Pitfalls
- • Tracking too many metrics.
- • Not acting on score declines.
Checklist
- • Scorecard defined.
- • Monthly review cadence established.
- • Action plan tied to score.