Why read this
Read this before a demo if you need to turn an AI idea into a business-owned workflow with a baseline, review points, and a stop rule.
Start with a real workflow
Name the team, user, input, output, exception, and accountable owner before comparing models. In Australia, the strongest first use cases are usually bounded support, retrieval, prioritisation, documentation, or forecasting tasks rather than open-ended autonomous decisions.
Measure the change that matters
Compare the proposed system with the current process. Track quality, cycle time, rework, escalation, user adoption, customer impact, and control failures. A faster workflow is not an improvement if it creates hidden review or remediation work.
Scale only after the control works
Move from assistive use to broader automation only after the team can explain the output, correct it, recover from failure, and show who remains responsible. Record the version, data boundary, monitoring, and rollback path.
Questions for the buying team
- What baseline proves that this workflow improved?
- Which decisions remain human-owned?
- What stops the workflow when quality, safety, privacy, or resilience falls below threshold?
Local evidence boundary: this guide organises questions and sources. It is not a legal, security, clinical, financial, procurement, or implementation approval.
Sources and further reading
- Australian Government AI in Government Policy standards guidance
- Australian Government essential AI practices standards guidance
- OAIC artificial intelligence and privacy guidance standards guidance