Quality Principles
Standards for the training material and delivery experience.
Practicality
| Requirement | Standard |
|---|---|
| Real work | Exercises use familiar company workflows wherever possible |
| Plain language | Avoid unnecessary AI jargon unless the audience is technical |
| Role fit | Content changes for executives, managers, staff, champions, and builders |
| Reusable outputs | Every practical workshop produces a playbook asset |
Safety
| Requirement | Standard |
|---|---|
| Data handling | Participants know what not to paste into AI tools |
| Human review | High-impact outputs require accountable human review |
| Source checking | Research and claims are checked before reuse |
| Tool boundaries | Approved and prohibited tool uses are clear |
Accessibility and Inclusion
| Requirement | Standard |
|---|---|
| Learning modes | Blend explanation, demo, practice, and discussion |
| Technical depth | Do not assume coding knowledge in staff sessions |
| Psychological safety | Treat anxiety and scepticism as design inputs |
| Materials | Provide checklists and examples participants can revisit |
Measurement
Training should be evaluated against behaviour change, not attendance alone:
- Participants can complete target tasks with AI.
- Managers can explain where AI use is expected and where it is risky.
- Champions can coach peers without OzAI in the room.
- Teams can show at least one workflow improvement.
- Governance questions have named owners and due dates.