By: Grant Hewlett, Vice President, Product-Firm Intelligence, Litera
There’s a tempting assumption running through most conversations about AI and law firm profitability: if AI makes lawyers faster, firms will become more profitable. While that may prove true for some firms, it isn’t a given.
For law firms that still predominantly rely on the billable hour, speed can inadvertently impact revenue, and not in a good way. Firms don’t make money because work is completed quickly, they make money when work is properly scoped, priced, staffed, managed, billed, collected, and expanded into durable client relationships. AI can increase the speed at which work is done, but not the quality of the work itself.
AI assisted document review, matter intake, contract analysis, drafting and research are already compressing days of work into hours or even minutes, which creates capacity. However, if the firm doesn’t decide where that time goes into — better matter management, faster billing, stronger realization, more proactive business development or higher-value work — the value may be lost or will be expected by clients to be shown in discounted rates.
We’ve seen versions of this before. As legal research tools became more powerful and transparent, clients became less willing to treat research time as an unquestioned pass-through cost. But technology didn’t make legal advice less valuable as a result — firms will just need to become more intentional about translating tech-enabled efficiency into material value.
Sophisticated in-house legal teams, many of whom are running the same AI tools as their outside counsel counterparts, are increasingly asking a direct question: if it takes you half the time, why does it cost the same?
That question becomes more acute as alternative fee arrangements mature, which require firms to understand the relationship between effort, value, risk and margin more clearly than the billable hour alone ever demanded. The firms that can’t explain how the value of the work has increased will have to explain why the price hasn’t decreased.
This is the profitability paradox of legal AI: a firm can invest in efficiency and still inadvertently damage its economics if it doesn’t also improve pricing discipline, scoping accuracy, matter management, realization, and business growth. This is why time saved through AI must be invested in better matters, margins, client conversations, and business development.
The same principle applies to lockup. Faster work does little for firm profitability if bills go out late, WIP sits too long, partners negotiate value away at the end of the matter, or clients are surprised by a number they weren’t prepared to pay.
For many firms, the issue isn’t simply whether the work was efficient, but whether the firm had the commercial visibility to manage the matter while there was still time to change the outcome. That means understanding budget performance, scope movement, billing behavior, write-down risk, and collection exposure before the matter becomes post-mortem.
This is where tech can help firms become more commercially minded by giving partners better information at the moments where judgement matters: when pricing the work, staffing the team, managing client expectations, reviewing WIP, and deciding whether similar work is worth pursuing.
Profitability is determined by what the firm does with the time AI gives back. If the value AI creates only results in a lower fee, thinner leverage or the same client relationship at a lower margin, you have simply funded an efficiency gain that someone else captured.
The better answer isn’t to resist efficiency; it’s to manage it commercially. Reclaimed capacity should be redirected to into activities that improve the economics of the firm, like better scoping, earlier budget intervention, cleaner staffing models, more proactive partner-client conversations, stronger cross-sell identification, and more disciplined pursuit decisions.
General-purpose AI tools operate at scale across industries, and while they can make legal work faster, they don’t know the business nuances of a specific firm — like which partner has the strongest relationship with a client preparing for a restructuring, which pricing assumptions were wrong, or which client expectations created collection issues. That intelligence lives in the firm’s own data: matter history, experience records, relationship activity, pricing data, financial performance, client feedback, pitch history, and institutional knowledge.
The firms that won the post-Westlaw era understood the importance of this intelligence. Those that pulled ahead built knowledge infrastructure on top of the efficiency gain, and invested in precedent libraries, matter tagging systems, and formal knowledge management functions precisely because they recognized the long-term value of such actions.
Matter scoping is another good example. Too often, scoping is treated as a pricing exercise or an administrative step before the ‘real work’ begins. In reality, it is one of the most important business intelligence moments in the matter lifecycle. Done well, it tells the firm what the matter is worth to win, what it’ll likely take to deliver, what comparable matters should inform the fee, and whether the opportunity fits the firm and practice group’s strategy.
That’s the difference between using technology to move faster and using it to run a better firm.
Measuring the “return on AI” (RoAI) in hours alone is an incomplete economic story. Firms must assess whether it captured the value of the efficiency created. Did realization improve? Did lockup come down? Did partners identify more opportunities from existing clients? Did the firm win more of the right matters and avoid the wrong ones?
Those are harder questions than “how much time did the tool save?” but they’re the questions that determine whether AI investment shows up in revenue, margin, cashflow and client growth, e.g. RPL, PPEP/PPP. That’s what separates an AI investment from an AI advantage.
Grant Hewlett, Vice President, Product-Firm Intelligence, Litera, helps clients leverage AI to surface relationships and intelligence already sitting inside their firm’s own systems. Connect with Grant on LinkedIn or check out Litera’s growth solutions here.