A Practical Guide to Your First AI Literacy Assessment

You cannot target training you have not measured. Here is how to run a readiness assessment that actually changes what you do next.

Fellow Intelligence

Every organisation running AI training is guessing about who needs what. An assessment replaces the guess with a map.¹1The guess is expensive — it usually means everyone sits the same course regardless of need.

The goal is not a score. It is a decision: where to spend the next training baht.

Measure four dimensions, not one

A single "AI skill" number hides the gaps that matter.^◦ We assess four:By the numbers4dimensions we score, because one number hides the gaps that matter

Foundations — what the tools are, what they can and cannot do.
Applied use — can they get real work done with them today.
Judgment and risk — do they know when not to trust an output.
Workflow integration — is AI part of how the job is done, or a side experiment.

Report by cohort, not by name

The fastest way to kill honest answers is to make the assessment feel like a performance review. Report results by department and role, not by individual.^◦ You are diagnosing a system, not grading people.FigureResults map to cohorts, so training can be aimed where it actually lands.

Turn the result into a plan

A readiness snapshot is only useful if it forces a choice. A good output looks like:

Which cohorts are ready for advanced, workflow-specific training.
Which need foundations first.
Where confidence outruns capability — the riskiest gap of all.^◦DefinitionConfidence–capability gap When belief in one's AI skill outruns actual ability — the pattern most likely to produce unsafe use.

Re-assess every six to twelve months, or after any major rollout.^◦ Readiness is a moving target, and the point is to watch it move.RelatedWhy measurement keeps a rollout alive