Pilot an AI-assisted task in a legal department
For legal operations leads testing an AI workflow.
The tool produces a usable draft. Can our team actually operate the new workflow?
Try one recurring task on a limited queue with a named operator, reviewer and backup. Count the time until a reviewed item is ready for the next person, including time spent waiting or correcting it. Set a review date and identify who may continue, change or stop the pilot.
Follow one item through the existing queue
Choose a bounded job such as routing an incoming request to the right legal team. Record who receives the item, what they check, who can make the routing decision, and how an urgent or ambiguous item gets attention. Establish how long comparable jobs take today and where the existing process already backs up.
Define which items the pilot may handle, the expected volume and the end date. Start with fictional requests or material expressly approved for the exercise. For each proposed handoff, identify the person who accepts the item. Give participants a simple way to return it to the ordinary process.
Measure the work that reaches the reviewer
For each item, record arrival time, suggested route, reviewer decision, correction time and time of accepted handoff. Separate minutes of active work from hours spent waiting. Count items still pending at the end of the pilot and note why. A fast first draft may make the overall process slower if a specialist’s queue grows.
Simulate an absent reviewer, a request outside the permitted scope and an item flagged as urgent. Observe whether the backup can see the item, has authority to act and knows when to return it to the existing process. Keep any unresolved item visible to its owner.
Decide what the department can support
Before the pilot, agree what counts as timely review, which failures require a stop and who may approve a limited extension. At the review date, compare elapsed time, staff effort, wrong routes and unreviewed items with the ordinary process. Assign each open correction a person and a date. Record exactly what the decision owner authorized for the next round.
Worked example
Illustrative pilot. A legal department routes twelve fictional service requests in an afternoon. The operator spends six fewer minutes per item than under the old process. Four items are still waiting the next morning because only one lawyer can approve their suggested route. The team records the backlog, gives a trained backup access in the exercise and repeats the absent-reviewer scenario before deciding whether a larger queue is workable.
Primary sources
Related work at Agentic Attorney
This pilot design guide is in the shared Agentic Attorney resource collection. The hub describes AI implementation for legal teams. Explore Agentic Attorney’s approach to AI implementation.