Updated 2026-02-25

AI Readiness Assessment Scoring Guide

A practical AI readiness assessment scoring guide to identify governance, rollout, and capability gaps before leaders scale adoption.

Core pillar

AI Readiness Checklist for Leaders

Use this assessment within AILD's AI readiness checklist pillar.

AssessmentLeadership

Why run a baseline first

Most AI programs fail because teams skip capability diagnosis. A baseline assessment gives leaders a real starting point for leadership and execution plans.

Five assessment dimensions

Task Fit: Which tasks are repetitive, text-heavy, and reviewable?
Data Risk: What data can be used safely in AI tools?
Prompt Competence: Can staff generate usable outputs consistently?
Review Discipline: Are there clear quality gates before outputs ship?
Measurement Readiness: Are baseline time and quality metrics tracked?

Scoring model

Use a 1-5 scale per dimension.

1-2: high risk, low readiness
3: partial readiness
4-5: strong readiness for scaled rollout

What to do with low scores

Low task fit: start with workflow discovery and process mapping.
Low data risk maturity: deploy policy controls before any expansion.
Low review discipline: enforce 5-minute QA checks.
Low measurement readiness: set up the ROI dashboard.

Decision rule

Do not scale across departments until at least three dimensions score 4+ for the pilot function.

Executive implementation plan (next 30 days)

Run one leadership literacy baseline survey across strategy, operations, and risk owners.
Convert literacy gaps into role-based learning priorities tied to active workflows.
Add a monthly leadership session focused on real decisions, not generic AI news.
Track whether literacy improvements translate into better governance and decision speed.

Failure modes to avoid

Treating literacy as one-off training instead of an operating capability.
Measuring completion rates but not decision quality improvements.
Leaving executives out of the learning loop while expecting cross-functional alignment.