Before evaluating any AI product, law firms and judicial offices should first define the use case with precision. What problem is the tool supposed to solve, for whom, and in what workflow? Is it reducing administrative burden, speeding review, improving consistency, increasing billable capacity, or supporting a higher-quality service experience? A vague goal like “use AI more” is not enough. The strongest evaluation process begins with a clear understanding of the task, the people affected, the data involved, and the level of human judgment the work requires.

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That front-end discipline also makes it possible to assess whether a product is worth the cost and risk. Any proposed solution should be measured against a realistic business case: expected savings, new revenue opportunity, or meaningful operational improvement, weighed against licensing costs, implementation burden, training needs, security exposure, confidentiality concerns, and governance overhead. In many cases, the right question is not simply whether a tool can perform a task, but whether it can do so well enough, safely enough, and economically enough to justify adoption.

 

The 10-page presentation (download below) moves from data inputs, storage, and vendor dependencies through testing, human oversight, confidentiality, audit rights, exit planning, and institutional fit, offering a structured framework for responsible AI adoption in legal and judicial settings. But the use case analysis must precede the product evaluation in order to test the right parameters.