At a glance
- Distinguish suggestions from actions.
- Define data access and review points.
- Evaluate difficult examples before wider use.
The scenario
Imagine an operations team that spends time locating approved information and preparing updates across different systems. The team wants to explore an assistant, but responsibility for changes must remain clear. This is a hypothetical use case, not a deployed AI customer system.
A possible approach
Bound the workflow, identify the sources it may use and name the people responsible for the information. Begin with assistance that can be reviewed, then define explicit approval boundaries for any proposed actions. Plan how users can inspect supporting context, reject an output and recover from an error.
How an outcome could be assessed
Create representative tasks and challenging examples from the intended workflow. Have the responsible reviewers assess the outputs against agreed criteria. Document errors, access limitations and cases where the system should defer. No accuracy, productivity gain or autonomous capability is claimed by this example.
What a publishable case would need
An actual case would need approved scope, evaluation methodology, reviewed results and a clear account of human oversight. Evidence would need to distinguish a controlled evaluation from operational performance.
