Describe the work to improve
Choose concrete use cases such as drafting knowledge, categorising requests or assisting an analyst. Establish the current effort, quality and failure modes before asking vendors to demonstrate AI.
Test with representative scenarios
Use sanitised examples that reflect your services, terminology and permissions. Include ambiguous requests and incomplete knowledge. Assess usefulness, errors and the effort required to check or correct an output.
Make control part of the requirement
Ask how access permissions, human approval, logging and sensitive information are handled. Distinguish suggestions from actions that change records or services. Require a clear way to review and reverse permitted actions.
Evaluate the operating cost
Include licensing, usage charges, configuration, content preparation and ongoing supervision. Compare the net effort saved after review and correction, rather than counting only the time to generate an answer.
Define acceptance before the proof of concept
Agree success criteria, unacceptable failures and who makes the final decision. Document limitations and dependencies. Treat AI value as something to demonstrate under your conditions, not as an automatic consequence of buying a platform.