One month to evaluate AI in a law firm: measure what actually helps
A trial involving a few defined tasks can teach more than a broad productivity promise. Here is a concrete protocol without invented performance figures.
Choose work the team already understands
A firm wants to test AI for chronologies, document comparison and rewriting letters. In this fictional example, it selects practice files containing no client data. Evaluators know the expected results and can identify a material omission.
Before the trial, each task has a defined input, expected output and quality criterion. A chronology with a document reference for every event is easier to assess than the general aim of saving time. An incomplete result is not satisfactory just because it arrives quickly.
Measure until the output is usable
Writing time is only part of the job. Preparation, correction requests, reference checks and final review must also be counted. The firm compares similar tasks and takes the operator’s experience into account. A difference in task difficulty should not be mistaken for a tool benefit.
The team also records errors: added dates, omitted documents, changes of meaning and untraceable references. These observations show whether instructions need improvement or the task is unsuitable. A universal efficiency percentage is unnecessary for learning something useful.
Build feedback into the trial
A short weekly meeting considers one successful example and one requiring correction. Team members explain what helped and what demanded more checking. Effective instructions are retained; prompts that repeatedly produce ambiguity are revised or retired.
The CCBE guide on generative AI offers a professional reference point for further examination of use cases. The regulatory framework must also be assessed in light of the firm’s role, the tool and its purpose. An internal experiment does not remove those questions.
Decide from observed results
At the end of the month, the firm can approve a specific task, extend the trial or stop it. A publisher such as Mashaah LLC can receive concrete feedback on AILawyer: a hard-to-find source, an explanation that is too long or a useful feature. This connects product development to actual needs. Success means better work, not the largest possible number of generated answers.
Sources and references
General information. Application to a matter depends on its facts and the rules in force.
