AI literacy is often reduced to a training module. In a regulated bank or insurer it should mean that people understand the capabilities, limits and control responsibilities of the systems they use. A board needs a different level of literacy from a data scientist, but neither can delegate accountability to a generic policy.
Segment the training
Board members need to challenge purpose, risk appetite, customer impact and evidence. Business owners need to understand use-case boundaries, escalation and human oversight. Developers need data lineage, testing, security and change-control standards. Compliance and internal audit need traceability and challenge methods.
Make literacy measurable
Use role-based assessments, production simulations and incident exercises. A pass mark alone is weak evidence if staff cannot recognise data leakage, automation bias, prompt injection, drift or an unauthorised use case. Record training completion, test performance and remediation.
Connect literacy to governance
Training should link to the AI inventory, approval gates, incident process and performance monitoring. When a new tool is introduced, the owner should be able to identify the risk class, affected data, prohibited uses and required control evidence without waiting for a crisis.
The European Commission's AI framework is a useful reference for organisational readiness. Quantica Risk Modelling treats AI literacy as a control capability that supports actuarial, banking and insurance decision quality.