An AI project needs an operational problem, a defined owner and evidence that the proposed system improves the work. Selecting a model is one implementation decision within that project, not the project's purpose.
Define the users, inputs, expected outputs and decisions affected. Agree on acceptance criteria before building the demonstration. Include integration, data preparation and review procedures in the scope so that a successful prototype is not confused with a deployable workflow.
Plan a pilot with representative cases and a clear comparison process. Track failures, correction effort, cost and user feedback. Keep technical changes and evaluation results connected so the team can explain why a release was accepted or rejected.
Assign responsibility for monitoring, incidents and future revisions. Document a fallback and exit process. Close the project only when the operational handoff, not just the demonstration, has met the agreed requirements.
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
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Reading adjacent decision areas keeps the topic from becoming an isolated note.