Measure the work, not the AI

A tool produces a report in seconds. The demonstration looks impressive. But how long does the team then spend checking it, correcting it and getting it back into the case management system?

That is the question I would ask before calling an AI deployment a success.

In legal operations, the useful measure is the cost of reaching a checked result. That includes the model, the existing software, the people reviewing exceptions and any work that has to be done again. A cheap answer can become an expensive process.

I would start with one repeatable workflow. Map how the work moves today. Where does data arrive? Who checks it? Where does it get entered twice? What causes a file to come back? A simple measure, such as the time from one step to the next, can tell us more than an impressive demonstration. The solution can sound simple. Getting a team to follow it consistently is the hard part.

But a case management system can only provide useful data if people follow the process. That needs attention from the partners too: review the deviations, understand why they happen and improve the way the team works. Then measure review time, rework and unresolved exceptions before and after adding AI. Otherwise, we risk moving effort around and calling it a saving.

To me, intelligent legal operations means making the whole process work better. The test is simple: did the firm get to a reliable result with less total effort?