AI should accelerate thinking and execution without replacing your responsibility to understand, verify and explain the result.
Key points
- Define the problem and acceptance checks before prompting
- Verify important outputs against data, docs or a second method
- Record what you accepted, rejected and changed—and why
Illustrative example
Illustrative AI decision log
Teaching example — not a SkillsNCR outcome claim.
- Task
Write SQL for average order value by region for March.
- Suggestion accepted
Use a CTE that aggregates orders before joining to regions, preserving a cleaner grain.
- Suggestion rejected
WHERE region != NULLReason: incorrect null logic.
Replacement:
IS NOT NULL- Independently checked
- row counts before and after the filter
- total amount against a known Finance extract for March
- reconciliation on the synthetic/toy dataset
- Privacy boundary
No production customer names were pasted into a public model. Synthetic IDs and aggregate-only data were used.
- Rationale kept for review
Model helped structure the CTE; null filtering and reconciliation remained human-owned.
How WorkReady practises this
The AI Decision Log in WorkReady 90 makes judgment visible so the associate remains accountable for the final work in reviews and interviews.
Related insights
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