Choose a measurable task
Pick one repeated task: finding an answer in documents, extracting request fields or drafting a response. Record how it is done today and where the effort goes.
If a reliable rule solves the problem, a model may add unnecessary complexity. Compare the pilot with both the current process and conventional automation.
Prepare examples before tuning
Include typical requests, difficult cases and questions the knowledge base cannot answer. Define acceptable results and unacceptable mistakes for each.
Keep some examples separate for evaluation after configuration. Otherwise, a strong result may simply reflect tuning for familiar inputs.
Limit data and actions
Decide which documents may be used, who can access them and whether an external provider may process them. Anonymised examples are often enough for an initial test.
Start with search or drafts. Sending messages, changing orders and other consequential actions require explicit permissions and confirmation.
Evaluate quality and cost
Check facts, sources, missed conditions and whether the tool acknowledges missing information. In document search, verify that the cited source actually supports the answer.
Measure latency and cost per request, including long documents and repeat queries. Set a pilot spending limit and a way to stop unexpected usage.
Make a decision after the pilot
Possible outcomes include launching a limited workflow, improving the knowledge base or deciding against AI for that task. Each is useful if the evidence is clear.
Before launch, assign responsibility for errors, source updates, feedback and human handoff. A promise of flawless answers cannot replace these controls.