Teaching a non-technical team to use AI every day

The team was spending hours each week on inbox triage, weekly planning, follow-ups, and deck polishing. Output quality varied by person. There were no shared rules for what could and couldn't go into an AI tool.

Five Career Specialists across six regions. None of them technologists. All of them busy, and some of them skeptical.

• A 90-minute live workshop with pre-work, a 15-minute live prompt demo, and homework on a real task
• A team handbook of six copy-paste prompt workflows: inbox triage, weekly task pull, daily check-in, post-call follow-up, end-of-week summary, and a quick LinkedIn post
• 1:1 working sessions on request, including a deck-rebuilding session that ended with the specialist running the workflow live
• A living training log with one entry format per session (need, objective, content, live outcome, materials, follow-ups) so any session can be repeated by someone who wasn't there
• A responsible-use standard: redact before you prompt, a PII-removal preamble, review before send, model-to-task matching, work and personal accounts kept separate

• Homework on a real task instead of a demo exercise, so the first use happened on something that mattered. The tradeoff: harder to standardize, easier to adopt.
• Gemini named as the fallback because it was already in the organization's workspace, even though Claude was the primary tool taught. Meet people where the tools already are.
• A training log over a slide archive. Slides show what you said. A log shows what someone can do next time.

Specialists asked for 1:1 time. One ran the deck workflow live during a session. Communications and class decks visibly improved. Leadership agreed to advocate for paid AI seats for national leads and asked for the full-team case for the next fiscal year.

Collect time-saved data from the homework systematically and run a 30-day measured pilot on one workflow.