The first order for an AI service should establish whether a bounded workflow is useful enough to adopt. Treat a pilot as an experiment with a decision at the end: continue, change the scope or stop. Paying for an impressive demonstration without an agreed evaluation leaves that decision difficult to make.
Define the pilot before spending
Choose one task and specify who will use the output. Record the current effort and the errors that matter. Agree on the sample cases, evaluation method and acceptable level of human correction. Include difficult cases that appeared during discovery rather than testing only polished examples supplied by the vendor.
Set a spending limit and check how usage is counted. Separate implementation fees, model usage, storage, integration and ongoing support. Identify which costs remain if the pilot ends and whether your team can export the work and data it needs.
Make failure recoverable
Keep a human approval step for consequential outputs and a practical way to return to the previous process. Assign someone to review incidents and user feedback. An output that looks fluent may still be incorrect, so reviewers need the underlying evidence rather than a reassuring confidence label.
At the end, compare observed results with the original criteria. Record the limitations and the work needed to operate the service. Expand only to uses supported by the evaluation; success on one task does not establish reliability on every related task.