Useful AI training covers approved tools and data, clear instructions, output verification, privacy and security, role-specific practice, human review, and escalation. Training should help staff understand both what a tool can assist with and when not to use it.
NIST's AI Risk Management Framework and Generative AI Profile offer voluntary resources for understanding and managing AI risks. They can inform a training plan alongside an organization's own policies and applicable requirements.
Foundational topics for all staff
- What the approved AI tools do and what they are not authorized to do
- How to write clear instructions and provide appropriate context
- How to verify facts, calculations, citations, and omissions
- Which information may or may not be entered into each approved tool
- How to recognize limitations, bias concerns, and accessibility needs
- When to stop, escalate an issue, or ask a supervisor or subject-matter expert
Emphasize that a confident-sounding response is not, on its own, evidence that the response is correct. Staff should check generated content against reliable source material before relying on it.
Role-specific practice
Shared fundamentals can be paired with exercises that reflect the staff member's responsibilities, access to information, and required review. Administrative staff might practice permitted drafting or document organization; program staff might consider approved documentation support; managers might work through review and escalation responsibilities; and leaders might address tool approval and oversight.
Hypothetical example
A training exercise could ask a participant to draft a summary of a non-sensitive sample document using an approved tool, compare the draft with the original, identify any missing or unsupported statements, and revise or reject the draft. This is a hypothetical training exercise, not a report of a client engagement or measured result.
Training should produce practical work products
A useful program can leave participants with:
- A clearly scoped, approved use case for their work
- A tested prompt or workflow that follows organizational rules
- A checklist for reviewing outputs against source information
- A clear escalation route for errors, uncertainty, or sensitive situations
Follow-up practice gives teams an opportunity to surface questions and revisit guidance as approved tools, policies, and workflows change. Managers should make it straightforward for staff to report issues without treating use of AI as a substitute for professional judgment.
What not to do
Do not train staff on tools before explaining which systems and data are approved. Do not imply that fluent output is necessarily correct. Do not separate AI instruction from the policies, supervision, accessibility needs, and everyday systems that shape the work.
Frequently Asked Questions
Should every employee receive the same AI training?
Everyone should understand shared rules for approved tools, data, verification, and escalation. Practice and additional guidance should reflect each role's responsibilities and access.
How often should AI training be updated?
Review training when tools, policies, vendors, or workflows change, and schedule refreshers that fit the organization's use and oversight needs.
What is the manager's role after training?
Managers reinforce approved practices, support output review, identify training needs, and provide a clear route for staff to raise concerns.
Gallant Business Solutions lists practical AI staff training among its services, including role-specific practice and follow-up coaching. See the Gallant services page for the current description.