It is practical judgment. A support lead needs to spot a false answer and handle customer data safely. A manager needs to judge whether a proposed workflow is worth funding. A developer needs to know where model output ends.
What does AI literacy look like at work?
People need enough context to use an approved tool without treating it as an authority. They should check material outputs, understand the task the system is allowed to perform, and know when a human in the loop must review a result. The goal is reliable judgment, not turning every employee into an AI engineer.
Training should use real workflows and failure cases. Good AI literacy gives people a clear next action when the system is wrong, rather than a warning to “be careful.”
Why does literacy reduce shadow AI?
Shadow AI often starts with an employee who does not know that a convenient feature sends sensitive content to another provider. Clear examples and a usable approved path make better behavior possible. AI governance sets the rules, while literacy makes them understandable in day-to-day work. AI adoption fails when people receive a tool without the judgment to use it well.