A demonstration answers a narrow question
A useful demonstration shows that a model can perform a bounded task on selected examples. It may use manual preparation, privileged access and a person correcting outputs behind the scenes.
That is legitimate evidence of possibility, but it does not establish accuracy across real inputs, acceptable cost, privacy, uptime or safe integration with business decisions.
Production exposes the exception path
Real workflows contain missing fields, ambiguous documents, duplicates, changing formats and access restrictions. Define representative evaluation material, acceptance thresholds and cases that must be routed to a person.
Permissions should follow roles and data need. Logs must make failures visible without collecting unnecessary personal or confidential content.
- Named process and system owner
- Versioned evaluation set
- Human review and stop conditions
- Monitoring, retries and recovery
- Cost and supplier-dependency limits
Operational ownership is part of delivery
Someone must approve changes, review failed cases, manage model or prompt updates and decide when the workflow is suspended. Documentation should distinguish deterministic rules from probabilistic outputs.
Call an AI system production-ready only within its tested scope. Wider claims require wider evidence, not a more polished interface.