An AI feature needs a purpose that can be explained without naming a model. Consider what it helps a person do and what a useful result would look like.
A narrow use case gives the team a clearer way to evaluate whether the feature belongs in the workflow.
Identify the information a feature needs, where it comes from, and whether it can be shared with the selected service. Do not send data simply because it is available.
Privacy, access, and retention decisions need accountable owners. Technical implementation should follow those decisions, not substitute for them.
Think about an unavailable service, an uncertain response, or a result that needs a person to check it. A feature should leave the wider product usable when the AI path cannot complete.
Define where human review is needed and how people can recover or continue. Useful integration is as much about boundaries as capability.
An editorial draft awaiting company review, not a client case study or a claim of guaranteed results. Project decisions should be evaluated in their own context.