Many AI projects fail through poor decisions made by capable people who lacked a few key concepts. A short, practical literacy programme closes that gap.
Topics that matter
- What models are good and bad at, and why they sometimes sound confident when wrong.
- How to ask for evidence: test sets, error rates, baselines.
- Data quality and ownership.
- Risk basics: privacy, security, bias and regulation.
- How to define a measurable outcome.
Teach with your own use cases and data. Examples from the manager's own team stick.