Build useful AI assistants around measurable tasks
The starting point for a professional AI assistant is the user's problem, not the number of model features. A reliable assistant should accept defined inputs, use approved evidence, produce an inspectable output and communicate what it could not verify.
Implementation method
Choose a single repeatable business task, such as classifying support tickets or summarizing approved policy documents. Write acceptance cases with correct outputs and failure cases. Set limits on data access, external tool use and autonomous actions. Compare performance with an existing manual workflow and document human review requirements.
Illustrative scenario
A support-triage assistant can label incoming requests by category and urgency, but it should not promise refunds or disclose account details without verified permission. Pilot it on a small set of anonymized examples, review disagreements and only then consider deploying it on actual tickets.
Quality checklist
- Task-specific acceptance criteria
- Verified evidence and restricted tools
- Human-reviewed pilot before expansion