Start from the work, not the tool
Describe the decisions, documents, and routines each role actually performs. Capability building is easier to judge when it is tied to specific tasks rather than a general ambition to become “AI literate.” Agree what should be different afterwards.
Assess the starting point
People arrive with different familiarity, confidence, and access. A short baseline—surveys, interviews, or a simple skills map—shows where the real gaps are, and reveals barriers that training alone cannot fix, such as unclear permissions or missing time.
Match learning to roles
Leaders, practitioners, and operational teams need different things: judgment and limits for one, depth and practice for another, and clear routines for the third. A single presentation to everyone tends to serve no one well.
Make practice the core
Understanding becomes capability when it is applied. Use real workflows in guided labs, provide safe spaces to experiment, and pair learning with examples of good and poor output so people can tell the difference.
Measure adoption, not attendance
Completion numbers show that a session happened. Look instead for evidence of use, changes in time or quality, and whether people continue without prompting. Communities of practice, refreshers, and visible ownership keep capability alive after the programme ends.
These are general planning considerations, not a substitute for an assessment of your specific business, regulatory, and technical requirements.
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