Build an enterprise AI academy around real role-based tasks. Develop four capability layers: foundations, human-AI collaboration, tools and agents, and evaluation and governance. Assess learners through reproducible work products, not attendance alone.
A four-layer capability model
| Layer | Capability | Evidence |
|---|---|---|
| Foundations | Limits, data risk, common failures | Scenario decisions and risk list |
| Collaboration | Task definition, context, verification | Reusable task templates and checks |
| Tools and agents | Knowledge, tools, permissions, workflows | Controlled prototype and logs |
| Evaluation and governance | Datasets, metrics, approval, review | Eval set, release record, improvement plan |
Design by role and task
Leaders need opportunity and accountability judgment; domain teams need clear task and quality definitions; technical teams need integration, permission, logging, and evaluation skills; risk roles should shape high-impact boundaries.
Use real but controlled exercises
Choose tasks that can be de-identified, evaluated, and rolled back. Record the current method and baseline, complete the task with AI, and compare quality, time, risk, and reuse. A one-off demo is not mastery.
Measure adoption after the course
Track work quality, verification errors, template reuse, steps saved, risk incidents, and real adoption. Thirty- and ninety-day reviews say more than an end-of-course quiz.
Frequently asked questions
Is an AI academy only for large companies?
No. Any team with repeatable tasks, data boundaries, and ongoing use can begin with a small role-based cohort.
Must we buy one standard AI tool first?
No. Define tasks, data, and permission requirements before selecting tools.
How do we prevent post-training drop-off?
Tie learning to real work, name owners and review cycles, and integrate effective templates and evaluations into operations.
Sources and further reading
- NIST AI Risk Management Framework
A public framework for AI governance and risk management.
- OpenAI Evals guide
A reference for continuous evaluation.