How do we avoid age stereotypes in AI training?
Age is a poor stand-in for knowing what a person needs to practice. A short task based assessment or a conversation about their relevant experience tells you more, and you can group support around what you learn.
The continuous innovation and culture conversations challenged assumptions about who will embrace new tools. In a training plan, that means asking people. Someone might know the business process cold and still need help with the interface. Someone else can use the tool fluently and miss important context about the work.
Group support around those needs where you can, and let people move between learning routes as their needs change. Skip public labels that call one group behind or another naturally capable.
Basic questions and deep experience can share a room
A colleague who spots an unusual customer exception can add something essential to an exercise, even while they're still learning a new technical step. A fair learning environment lets someone ask a basic question and offer deep experience in the same conversation. Judge the training by whether people can perform the task.
What to try this week
Replace one group training sign-up with a short task based check, then sort the support by what turns up.