Train for decisions, then make room to apply them
AI literacy means sound decisions in real work, and new skills only stick with time, an assignment and feedback.
AI skills count when people use them well on the job.
Someone can write an excellent prompt and still put confidential data into the wrong tool or accept an answer that doesn’t apply. That’s a training gap with operational consequences. And a course can be completed while the work stays exactly the same.
For HR, I define AI literacy through a few decisions: when to use an approved tool, what information to protect, how to check a result and when to bring in a specialist. Prompting sits inside that. It isn’t the measure.
Then connect learning to the team’s next real change in work, and come back to it after the first assignment. What can the person do now? Where do they still need support? Learning teams need to see those changes early, not get a late request to train everyone. CIPD’s 2026 guide recommends reviewing skills frameworks and technology assumptions at least every six months.
Where I’d start: run sessions on everyday situations: an ambiguous policy question, a document summary, an unsupported recommendation. Ask what people would verify, what they’d withhold and whether they’d use AI at all. Then agree one capability, protected practice time and a real assignment with a named reviewer.
The test: attendance is administration, not evidence. Check whether people can explain their decisions afterward. Without time, access and manager support, development is extra work the employee absorbs. That isn’t an investment plan.
Disagree? Tell me on LinkedIn. That’s where the discussion happens.