Core concepts, capabilities, and limitations.

Common tools, use cases, and where human expertise matters.

Provide context and clear instructions to AI systems.

Check outputs for accuracy, relevance, and quality.

Protect information and keep human accountability.

Have frontline staff experience the course before recommending it to customers.

Use it as a follow-up resource after orientation, job search, or digital-skills sessions.

Map next-step training for occupations where AI tools are becoming part of the work.

Do not turn a general literacy course into an unvalidated screening requirement.

Completion demonstrates exposure to foundational concepts, not occupational competence.

Language, disability access, phone plans, and comfort with texting can still matter.

Customers benefit more when examples and next steps connect to real occupations and tasks.

The course itself teaches evaluation and responsible use; local practice should reinforce those principles.

Use it as an optional starting point for customers curious or anxious about AI.

Ask employers what baseline AI literacy looks like in their actual workflows before recommending deeper training.

Use the course as a bridge into higher-value occupation-specific instruction, not as an ITA-worthy training program by itself.

Decide where AI literacy belongs in staff development and customer service without creating unnecessary mandates.