AI readiness is an organization's proven ability to adopt AI safely, covering workforce understanding, governed access, working controls, and the capacity to perform when something goes wrong. It's distinct from AI policy, which defines what people should do rather than evidencing what they actually do.

An AI readiness assessment measures AI capability across a workforce before training and again afterwards, so change is measurable rather than assumed. Adaptive Assessments produce individual scores, skill levels, and movement over time, with team views showing the spread from beginner to expert.

AI governance is the set of policies, controls, and accountabilities that determine how an organization builds and uses AI. Written controls can look effective until something goes wrong, so proving governance means exercising it, putting teams inside realistic scenarios and capturing what they actually did and when.

Approving AI access through a policy or sign-off records permission, not capability. Tying access to demonstrated competency means employees prove the required capability for their role first, with access following through your existing identity workflow. Stricter controls can be applied to higher-risk roles.

Start with a baseline of what people already know, then reassess after learning to show what changed. AI Knowledge Pass delivers practical AI literacy aligned to DigComp 3.0, covering how AI tools work, what happens to employee data, and where human judgment must remain.

Coverage evidence usually spans both AI-specific and general security standards. The MITRE ATLAS Heatmap shows coverage across adversarial tactics by team and role down to sub-technique, alongside OWASP Top 10 metrics, MITRE ATT&CK benchmarking, OWASP Agentic Security, and AISVS.