A useful AI conversation begins with someone trying to get something done. Naming that person, the task, and the friction creates a better starting point than a broad ambition to adopt AI. It also gives your team a concrete way to judge whether an experiment deserves a place in daily work.
Name one task and its owner.
Choose a bounded activity: preparing a draft account summary, locating information in approved documents, or organizing the context for a service handoff. Describe the input, the expected output, and where a person needs to review the result.
Talk to the people who perform the task. Understand which parts require judgment and which parts involve finding, assembling, or reformatting information. This establishes what assistance would be useful before a particular tool is selected.
Follow the information.
List the systems and content the task depends on. Identify who owns the information, who is permitted to see it, and which source should be treated as authoritative when records disagree. Note where content is incomplete or difficult to trust.
Keep the first experiment within a defined information boundary. If a use case depends on solving a larger data problem, make that dependency part of the plan rather than leaving it for the team to discover during a pilot.
Decide what a good result means.
Create representative examples and agree how to evaluate the output. Consider usefulness, factual accuracy, missing information, and the effort required to review or correct it. Include difficult cases as well as straightforward ones.
Compare the assisted workflow with the current process. A polished answer is not sufficient evidence that the work has improved. The evaluation should reflect what the task owner needs to accomplish and the consequences of an error.
Design the handoff to daily use.
Agree where the output will appear, who reviews it, and what happens when the system cannot complete the task. Define a fallback path and a way for users to report problems. Keep responsibilities clear when the underlying process or information changes.
A pilot becomes useful when the surrounding workflow is ready. Start with a clear question, evaluate it honestly, and use the findings to decide whether to expand, revise, or stop.
Turn the questions into a practical next step.
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