How are mid-size law firms measuring whether their AI spend actually pays off?
Most are not, at least not with numbers. The majority run on user surveys, adoption rates, and hallway feedback, and over half admit their real reason for spending is competitive pressure rather than a return they have modeled. A small group has landed on hard metrics, and they all got there the same way: they started from the specific outcome they wanted AI to change, then measured that one thing.
| What mid-size firms report | Share |
|---|---|
| Justify AI spend by competitive pressure, not a modeled return | 52% |
| Rely on user surveys or feedback as their main proxy for value | 48% |
| Track at least one hard, quantifiable AI metric | 16% |
Anecdote is standing in for evidence
The most common way firms judge their AI spend is soft: user surveys, focus groups, and informal feedback about whether people like the tools. As a first read that is reasonable, and it is honest about where most firms actually are. The problem starts when budgets grow and partners begin asking harder questions, because a good story is difficult to defend in a budget review. The firms that stay comfortable are the ones already converting those stories into a number.
Usage is a floor, not proof
Adoption rate is the second most common proxy, and it is a real signal that a tool has not been abandoned. But continued use conflates habit with value. People keep using plenty of things that are not moving the business, and no partner reviewing the line item will accept high login counts as evidence of return. Usage tells you a tool is alive. It does not tell you it is paying off.
The firms with real numbers started narrow
The minority tracking genuine outcomes did not begin with a better dashboard. They picked one role and one result, then measured only that: paralegal overtime against billable hours, or a target such as a 5% lift in billable hours year over year. In practices where the outcome is naturally visible, a stronger settlement demand or a faster path to resolution does the measuring for them. The lesson is not which metric to copy. It is that the number follows a clear question about what AI was supposed to fix.
Naming the bet takes the pressure off
Over half of firms are spending because they are worried about being left behind, not because they have modeled a return, and there is nothing wrong with that. It is a strategic bet on staying competitive, and it is defensible as long as the firm names it as one. The trouble comes from dressing a competitive decision up as an ROI calculation it was never based on. Call the bet a bet, and you can stop forcing a measurement story that does not yet exist. Then, when you are ready, pick the one outcome you actually want and start counting.
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