AILD AI Leadership and Development
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Updated 2026-02-25

AI Readiness Baseline Assessment for Teams

A practical assessment model to identify AI capability gaps before scaling leadership and execution workflows.

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

  1. Task Fit: Which tasks are repetitive, text-heavy, and reviewable?
  2. Data Risk: What data can be used safely in AI tools?
  3. Prompt Competence: Can staff generate usable outputs consistently?
  4. Review Discipline: Are there clear quality gates before outputs ship?
  5. 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.

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