You don't have to trust a black box. Here is the whole method, in three steps.
We don't start from a formula. We stand up a working model of your firm — the partners, the associates, the AI tools already in the building — and let them work real matters, quarter after quarter, for several years.
Every quarter, drafts get written and reviewed, hand-offs succeed or fail, clients pay early or late, people stay or leave. The profit that falls out of all that motion is your baseline. Nobody typed it in. It's what your firm produces when it runs exactly as it does today.
That's why the baseline should look familiar. If it doesn't, the model is wrong, and the report tells you where to check.
Produces → the baselineThere are five moves on the table: how you bill, how work is handed off, how you pay for AI adoption, how fast you act on results, and how flat the pyramid is.
We try each one on its own, then in combinations, rerunning the entire firm each time. A change that only looks good on paper falls apart here, because it has to survive the same rework and write-offs as everything else.
We keep the combination that lifts profit the most — and we note the order, because some moves only pay off after another one clears the way.
Produces → the recommendationOne good result can be luck. So we take the recommended plan and run it across dozens of fresh scenarios — the same firm, but different rolls of the dice on which matters land, which hand-offs break, and who walks out the door.
If the plan comes out ahead in nearly all of them, it's real, and we say so. If it only wins on average, we tell you to trust the direction but treat the dollar figure as provisional.
That's the difference between a forecast and a stress test. This is the stress test.
Produces → the confirmed resultThe summary above is the shape. Here is the machine. Nothing below is a metaphor — it's what the engine does, quarter by quarter. Read it if you want to know exactly where a number comes from before you stake a decision on it.
Each matter runs through a fixed litigation workflow — intake, conflicts, engagement letter, staffing, research, document review, drafting, the senior-associate review, the partner review, filing, billing, collection, closure. Every step is a hand-off from one role to the next, and every hand-off carries two numbers set by your firm's structure:
0.8); filing and collection are near zero — they run on standardized rails.0 = a checklist a tool can codify (legal research, engagement letters). 1 = pure partner judgment (settlement authority, the final read). AI is strong where tacitness is low and fails where it's high.This is why the firm leaks value at the top of the chain, not the bottom. The cheap steps are safe. The expensive judgment is where a confident-looking AI draft is most likely to be wrong.
Each move flips a specific setting, which changes specific behavior, which lands on the money through three channels: collections, profitability, and billable hours.
Under fixed fees, every hour AI saves is margin you keep — profitability rises with AI use. Under the hour, the same speed bills less, so AI adoption quietly drags margin down. And staying hourly while your clients push for alternative fees leaks collections outright. Transactional work, already effectively fixed-fee, softens the hourly penalty.
Sets the ceiling on how far adoption can climb — from wherever your partners already are, up toward ~95%. How hard the money bites depends on your comp model (lockstep firms move together and respond harder; eat-what-you-kill firms resist) and on whether a few rainmakers own the book and can simply ignore a firm-wide push.
Doesn't change how high adoption can go — changes how fast you get there. A short lag means the firm reaches its adoption ceiling early, so the benefit compounds over more of the run instead of arriving too late to matter.
Every few quarters the firm permanently fixes its single worst remaining hand-off — builds the precedent library, standardizes the transfer — and that seam stops leaking (its failure rate drops for the rest of the run). Fewer broken hand-offs means less redline and fewer exceptions. It costs a little margin each quarter to fund the knowledge-management work.
AI compresses billable hours, and a steep pyramid has more junior hours to compress — so the steeper the pyramid, the harder AI cuts utilization and the more billable time is lost per partner. Flattening reduces that exposure. This is the one lever that reaches profit through hours rather than rates.
Between quarters, the engine runs the human dynamics. Associates who lose faith in how AI is being rolled out leave faster — and one departure raises the odds of the next in the same group, so churn cascades. Meanwhile trust in AI spreads through the firm's actual influence network, where a skeptical (or convinced) knowledge-management partner moves everyone around them.
This is why the same plan can hold in one scenario and wobble in another: the people react, and their reaction changes next quarter's adoption and quality. It's also why the stress test in Step 3 matters — it's the only way to see whether a plan survives the human response, not just the spreadsheet.