Experience Curves and the Future of Large Law Firms

Over the next five years, the experience curve will collide with radical technology change to reshape large law firms’ talent models, pricing structures and legal practices. The experience curve demonstrates that as law firms handle more engagements, they accumulate learning, refine methodologies, and standardize repeatable elements of their work, reducing the cost, time, and variability of delivering similar outcomes in the future. Historically, law firms have resisted experience-curve dynamics. Legal work was viewed as bespoke – partner-driven, judgment-heavy, and difficult to standardize. But technological evolution, and in particular generative AI, is changing that equation. As more legal tasks become repeatable and partially automated, large law firms will begin to operate less like collections of individual artisans and more like production teams. Once that shift occurs, experience curves take over. The firms that learn and systematize fastest will pull ahead. The firms that do not, will find themselves facing competitive pressures on talent, profitability, market share, and client expectations.

AI adoption provides law firms with the potential to turn a wide range of legal tasks into process-driven work. Large law firms already utilize AI to perform numerous tasks, including those containing repeatable patterns, such as research memos, first-draft brief sections, contract redlines, due diligence summaries, deposition synthesis, sorting and prioritizing documents in large document reviews, citation checking, and regulatory monitoring. With AI, these tasks are increasingly becoming structured workflows rather than open-ended exercises.  As volume increases, large firms will accumulate learning, collecting data about which prompts produce better drafts, which clauses trigger negotiation friction, which judges respond to which argument framing, which diligence issues predict post-closing disputes. Once that learning is captured and embedded into templates, playbooks, and structured workflows, firms will benefit from tremendous efficiency gains.  

Some may fear that this shift from performing matter specific, one-off tasks to more structured process and workflows will eliminate the need for legal judgment. That has not been the experience of firms already implementing these technology and process changes. Instead, it separates the layers of work more clearly. High-volume, pattern-based tasks move down the experience curve quickly. Truly novel issue framing, deal structuring, negotiations, regulatory strategy, and jury strategy remain craft-driven—but augmented by AI. The result is a bifurcated practice model of practice where more work becomes operationalized, while high-end judgment becomes more explicitly premium.

Large law firms rely heavily on leverage through a traditional pyramid structure staffing model. AI disrupts that model. If billable time for drafting and research tasks drop dramatically, associate billable hours will follow suit. In response, these firms are likely to reduce the size of first year associate classes, and some firms have already forecasted this likelihood for the fall of 2027 and 2028, signaling an anticipated movement from the large firm pyramid to a cylindrical staffing model.

At the same time, new roles will emerge. Process-oriented lawyers who design workflows, maintain playbooks, and manage AI systems will become incredibly valuable. Firms will require additional legal operations professionals embedded within practice groups, including workflow managers and quality control specialists to ensure accuracy and mitigate risk. Perhaps even more novel, law firms will focus on hiring a new type of talent – those offering a hybrid legal-technologist skill set who have the ability to bridge the efficiency opportunities of automation and AI to substantive legal expertise.

Other talent model changes may include earlier specialization for associates or a bifurcation of recruiting in associate classes, with some being hired into more productized practice areas while others being hired into high-level strategy, negotiation, and complex advisory practices.

While the industry has been predicting a shift towards non-hourly pricing for over two decades, adoption of alternative fee arrangements has been modest, at best. Over the next five years, non-hourly pricing will start to gain greater traction across more practices. This will be driven by both technological change as well as experience curves. Once a task becomes predictable in cost and time, it becomes easier to price it on a basis other than the billable hour.

While hourly billing will persist in bespoke and specialized practices, we expect expanded use of alternative fees in production heavy practices. For example, fixed fees for defined deliverables, subscription models for recurring advisory work, portfolio-based pricing for litigation phases, and risk sharing arrangements which offer clients the benefit of enhanced efficiency while also offering law firms an upside for driving efficiency.

Firms that harness pricing capabilities and experience data will benefit from a significant competitive advantage. These firms will more effectively mine data to understand and manage costs, offering clients pricing arrangements which will protect profitability but also expand market share. Firms that lack the data or the know-how will struggle, especially against alternative legal service providers and in taking on work for sophisticated, technology-forward in-house teams.

At the center of this transformation lies knowledge management. Experience curves only generate an advantage if learning is captured, structured, and reused. For decades, much of law firms’ institutional knowledge has remained relatively untapped, tied to individual partners or buried in document management systems. AI provides law firms with the opportunity to capture and systematize that knowledge, and in doing so, more fully benefit from the experience curve. This involves adopting and training personnel on far more advanced AI-enabled knowledge management systems. Each engagement generates data, where time was spent, what revisions were required, which issues recurred, and what risks materialized. That data can then refine templates, workflows, and AI-assisted drafting. Over time, this creates compounding advantage – more matters generate more data, improving work product, reducing cost, and increasing reliability, all of which attracts more matters. However, scale becomes meaningful only if it is organized. Firms that invest in more effective and widely harnessed knowledge management systems will expand market share at a disproportionate rate.

For many small and mid-size firms, the experience curve will likely present more competitive pressure than advantage. Unlike large firms, small firms often lack the capital, business professional resources, data tools or capabilities, and matter volume necessary to adopt AI at scale or to benefit from the experience curve. As large firms systemize high-volume work processes and reduce costs, smaller firms may struggle to compete on efficiency or price in production-heavy practices.

Rather than attempting to match scale-driven automation, small and midsize firms will need to differentiate through specialization and bespoke advisory services—focusing on niche industries, more complex matters, or relationship-driven counsel where judgment and personal attention outweigh process efficiency. By concentrating on tailored solutions, local market knowledge, and partner-level engagement, small and mid-size firms can reduce exposure to technology-enabled cost competition.

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The collision between experience curves and AI will redefine how large law firms operate over the next five years. As more legal work becomes measurable, repeatable, and partially automated, the economics of production will shift. The firms that thrive will be those that harness the experience curve and productize what can be standardized while elevating and protecting the high-value, strategic counsel that remains bespoke.