An AI Readiness Scorecard You Can Run Monthly

TL;DR

• Use a monthly scorecard to track readiness.
• Focus on measurable improvements.

The problem

• 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

• 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.

Framework placement

Primary ARF layer: AI Readiness & Interoperability. Diagnostic bridge: data-movement-reliability, semantic-reliability, execution-reliability, change-reliability.