Multi-audience AI learning in a large aerospace organization
A learning architecture for managers, HR, knowledge teams, project managers and professional units, from shared foundations to role-based practice.
The challenge
A large organization does not have one audience or one starting point. The program needed to create a shared language while respecting different roles, knowledge levels and work environments.
How the process was designed
- Map each audience, its starting level and target tasks
- Combine a shared AI foundation with role-based learning tracks
- Use lectures, webinars, online workshops and in-person practice
- Practice only with public, synthetic or approved materials
What participants practiced
- Research, synthesis and document comparison
- Meeting preparation, briefings and work plans
- Using leading language models for professional tasks
- Output verification, privacy and human judgment
Materials and deliverables
- An audience map and learning mix
- Syllabi adapted to each activity
- Exercises matched to roles and starting levels
- Recommendations for further learning and adoption
Program context and results
The 2024–2026 activity was designed as a continuing learning sequence. A July 2026 career and AI week included 24 planned activities across in-person sessions, online meetings and webinars. No unverified impact or ROI figure is presented.