A marketer, a data analyst, and a support lead do not need the same AI skills. Most corporate AI training ignores this, teaches one generic curriculum to everyone, and lands on no one.
Start from the role, not the tool
The useful question is not "how do we use this AI tool." It is "what does this role do all day, and where does AI remove friction from it." The tool follows from the answer.
Different roles, different skills
Marketing gets the most from drafting, repurposing, and turning a brief into assets. Analysts gain from summarizing data, getting help with queries, and checking their own work. Support benefits from triage, drafting responses, and retrieving knowledge fast. Operations gains from automation and document processing. Leaders need something different again: knowing where AI pays back and where it does not, which is judgment, not tooling.
The common layer is judgment
Underneath the role-specific skills, everyone needs the same foundation: how AI fails, when to trust it and when to verify, and where data privacy and sensitive information demand caution. That judgment is what keeps the rest from backfiring.
- Marketing: drafting, repurposing, brief to assets
- Analysts: data summaries, query help, checking work
- Support: triage, response drafts, knowledge retrieval
- Leaders: where AI pays back, and where it does not
The question is not how to use the tool. It is what your role does all day, and where AI removes friction.
We map the skills each role actually needs and build the curriculum around them, so the training lands instead of washing over everyone equally.