A model can make a demonstration impressive. The systems beneath it determine whether the result can become a dependable part of the organization. AI readiness is therefore as much an IT and data question as a modeling question.
Know what information you have
Inventory the sources relevant to the use case. Understand their owners, formats, update patterns, and quality limitations. A small, well-understood information set can be more useful than a large collection with unclear provenance.
Give identity a central role
Decide how users, services, and integrations authenticate. Separate human access from machine access. Review the permissions needed by indexing jobs, model endpoints, and application components, rather than giving a prototype broad access for convenience.
Make operation visible
Plan how the team will see failures, investigate a poor response, rotate credentials, and recover an unavailable component. Logs should be useful without becoming an uncontrolled store of sensitive prompts or source material.
Understand the cost of the whole workflow
Model calls are one part of the cost. Storage, retrieval, integration, evaluation, human review, and support also matter. Define what to observe so a pilot can lead to a realistic operating plan.
Choose a foundation you can improve
Document the first implementation and the reasons behind its architecture. Keep the boundaries between data access, retrieval, generation, and the user interface understandable. That makes it easier to evaluate changes without rebuilding the entire workflow.