Key insights:

• • • UK law firms face a more selective growth market in 2026听鈥� Client demand remains steady, but external legal spend expectations have cooled, with growth concentrated in areas such as Regulatory, Labor & Employment, and international work.

    • Legal expertise alone is no longer enough 鈥� UK legal buyers increasingly favor law firms that combine technical excellence with commercial judgment, business understanding, and practical guidance aligned to client priorities.

    • AI adoption is becoming a client expectation听鈥� Corporate legal teams are moving faster than their outside law firms on GenAI, and many UK legal buyers now expect outside counsel to use AI to improve efficiency, workflows, and the quality of legal work.

The legal market in the United Kingdom today has shifted into a new normal. While law firms saw an explosion of demand and spending immediately following the pandemic, increasing client caution has resulted in a shift in priorities. Today鈥檚 law firms cannot simply rely on their old ways of providing legal service to succeed, as UK clients expect firms to combine expertise, commercial judgment, international reach, and visible AI-enabled improvements in how legal work is delivered.

Jump to 鈫�

2026 State of the UK Legal Market

A new report from the 成人VR视频 Institute, “2026 State of the UK Legal Market,” reveals how the UK legal market is shifting, as more judicious clients are beginning to force law firms to reassess their strategy. Overall anticipated net spend from legal clients has seen declining growth rates in recent years, and while some practices like Regulatory and Labor & Employment continue to see strong demand growth, other practice areas such as Insurance, IP, and Disputes face potential contraction.

This shift is also guided by emerging buyer preferences. The report reveals an increasing commerciality to the UK legal market, one in which clients increasingly favor advisors that combine legal excellence with commercial judgement, and those that are leveraging AI to bolster not only efficiency but improve the overall legal work product.

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Taken as a whole, the report paints a picture of clients that now are moving faster than their outside legal advisors, strengthening their internal capabilities, and setting clearer (and higher) expectations. This means that UK law firms cannot rest on their laurels, as clients increasingly push their outside firms to keep up with new business challenges.

The market is cautious, but opportunity remains

The report reveals that UK legal buyers are more cautious about external legal spend than they have been at any point in the last five years. That may mean law firms can no longer rely on the broad-based demand that defined the post-pandemic period and instead need to be more precise about where opportunity exists 鈥� and where it doesn鈥檛.

The report tracks buyer sentiment through net spend anticipation (NSA), which measures the share of buyers expecting to increase external legal spend over the next 12 months minus those expecting to decrease it. Since its 2021 peak, UK NSA has fallen steadily to +5 percentage points in 2025, returning the market to the more stable, single-digit baseline that was seen before the pandemic.

For those law firms looking to capture increased business, the report makes clear that legal expertise is now the price of entry, not the point of differentiation. The firms that stand out will be those that know how to apply their expertise in ways that reflect the client’s business realities.

Indeed, that is becoming even more important as corporate legal departments face growing pressure to demonstrate their own value to the wider organization, and they鈥檙e increasingly pointing to improvements in their own quality and effectiveness even before mentioning cost savings, efficiency, or time savings. Not surprisingly, more than one-third of UK legal buyers now cite business savviness as a reason they favor a particular law firm.

To help demonstrate their internal value, clients are pushing their outside law firms to leverage advanced technology to improve the overall effectiveness of legal work. Of course, this has resulted in a clear gap, the report notes, between how corporate legal teams are moving and how law firms are responding. For instance, the report shows that more than half of UK corporate legal respondents say their organizations are already using GenAI tools across the business, compared with just about one-third law firm respondents who said this.

That difference in outlook matters because clients increasingly believe AI will become a larger part of how legal work is delivered, and they鈥檙e not content to simply wait and see whether their outside counsel will fully adopt the technology. Indeed, corporate legal departments are expecting their outside law firms to keep pace with how legal work is changing, and they will reward those firms that do.

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a full copy of the 成人VR视频 Institute’s “2026 State of the UK Legal Market” by filling out the form below:

Key points:

• • • AI capability is jagged 鈥� Ethan Mollick’s frontier metaphor describes a coastline of strengths and weaknesses, in which a model that excels at contract analysis can fabricate a citation in the same conversation.

    • Human intelligence is jagged too 鈥� A century of psychology, from multiple intelligences to the Big Five, shows that each lawyer has their own coastline of strengths and weaknesses.

    • Person-AI fit is the next discipline 鈥� Firms that take this seriously will move from one-tool deployments to portfolios that match each lawyer to an AI partner whose jagged edges meet theirs.

Welcome back to The AI Law Professor. Last month, I examined how AI first drafts can blind us to other lines of reasoning and hijack our legal judgment. This month, I want to take up what determines whether an AI works for any given lawyer at all: Not which model is best, but which model is best for this lawyer, on this kind of work, at this point in their career

Professor and author gave us the metaphor that started this conversation 鈥� the jagged frontier of AI capability. Picture a coastline, irregular and unpredictable. On one side, the model is capable; on the other, it fails, sometimes catastrophically. The line itself does not run where you expect. Tasks that look hard turn out to be easy, and tasks that look easy turn out to be hard.

In terms of legal work, this means that a model that has just produced a useful contract analysis will confidently invent a citation. A model that has summarized a 90-page deposition with insight will fail at basic arithmetic. The capabilities of AI form a coastline, with bays and inlets and the occasional cliff. Mollick’s contribution was to give us a way to see this clearly. AI is not uniformly competent or uniformly incompetent 鈥� rather, it is jagged.

Humans are jagged too. Psychology has been telling us this for a century, although the message is uncomfortable enough that we keep flattening it back into a single number. The single-number version is IQ; yet the deeper issue with IQ is that it pretends intelligence is one-dimensional.

Developmental psychologist Howard Gardner’s , whatever its empirical limits, points us toward a more honest picture, one in which linguistic, logical-mathematical, spatial, musical, interpersonal, intrapersonal, and kinesthetic intelligences, are each largely independent. People are not equally strong across all these dimensions. So, it follows that a great trial lawyer and a great patent lawyer are drawing on different intelligences, and each could be lost in the other’s territory.

Human intelligence, like AI capability, is jagged, and each of us has an edge. The jaggedness is not a flaw to be smoothed; rather, it鈥檚 a feature of being a unique individual.

When two jagged edges meet

Place the two coastline maps 鈥� the human and the AI model 鈥� side by side. Press them together at random and they grind, with gaps where neither side fills the space and ridges where both claim the same territory. The lawyer’s strength overlaps with the AI model’s strength, so neither is amplified. The lawyer’s weakness overlaps with the model’s weakness, so neither is covered. The pair produces less than either party would produce alone.

However, align the same two surfaces with attention to their contours and something different happens. The peaks of one fit the valleys of the other. The lawyer’s weakness is met by the model’s strength; and the model’s weakness is met by the lawyer’s strength. The pair becomes more capable than either party alone.

A law firm that takes this seriously will not deploy a single AI tool across all of its lawyers and call the rollout complete. It will offer a portfolio of models and configurations and help each lawyer find the AI partner that works with their actual mind.

Every foundational model now ships with a model card, a document describing the model’s intended uses, training data, performance characteristics, and known limitations. The cards exist because models are not interchangeable. Read three of these cards side by side and the matching question becomes clear. A cautious generalist that hedges and flags uncertainty fits a lawyer who already holds strong views and wants a partner that will test them. A citation-anchored specialist that refuses to invent cases and stays grounded in retrieval fits a lawyer in heavily regulated practice areas in which errors are catastrophic.

The matchmaking discipline

Organizational psychology has worked on a version of this problem for 50 years under the . When a person’s strengths, values, and working style align with the demands and culture of their role, performance and well-being both rise. When they misalign, performance drops and burnout follows.

The same logic applies to person-AI fit. On the human side, cognitive style, domain expertise, personality profile, and the actual tasks performed in a typical week are key. On the AI side, behavior under different prompt styles, default tone, willingness to push back, hallucination patterns, and the shape of strengths and weaknesses across the practice areas in question may matter most. Yet, law firms are still treating AI procurement as a software decision rather than a partnership decision.

A law firm that takes this seriously will not deploy a single AI tool across all of its lawyers and call the rollout complete. It will offer a portfolio of models and configurations and help each lawyer find the AI partner that works with their actual mind. The first generation of legal AI has been dominated by the question of which model is best; however, the second generation will be dominated by a different question: Not which model, but which pairing works best. Not capability, but fit.

Those lawyers that flourish with AI will not necessarily be the most technical or the most enthusiastic users. Instead, they will be the ones that found, by luck or by design, an AI partner whose jagged edges meet theirs.

When two jagged intelligences fit well together, they can accomplish more than what either 鈥� human or AI 鈥� could do alone. Today, fit is the frontier.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and author of the forthcoming

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Key highlights:

• • • AI simulation can replace the “repetition loop” used to train junior lawyers 鈥� AI is taking over the repetitive work junior lawyers used to learn from and replacing it with simulation-based learning.

• • • Three design pillars can determine whether AI simulations will work 鈥� The best simulation tools are built around three pillars: clear learning goals, realistic unpredictability, and specific feedback.

• • • AI simulation tools offer law students spaces to fail 鈥� For law students and junior lawyers, simulation creates a rare low-risk space to practice, make mistakes, and improve.

For decades, junior lawyers learned by doing. Assignments landed on their desks, senior lawyers marked them up, and judgment accumulated through repetition and proximity to experience. Now, as AI takes over these foundational tasks, that repetition loop is breaking down, according to , which underscores how junior lawyers are being thrust into higher-level advisory work far earlier in their careers. Unfortunately, this is occurring before they have developed the instinctive gut feel for judgement that only comes from years of experience.

and , co-founders of legal training platform , and , Executive Director at the Stanford Law School鈥檚 (liftlab) all say they see the need to build new educational programs and pedagogical tools. And these learning capabilities must be heavily focused on the specific skill sets that underlie the judgment of drafting and the judgment of taking a deposition, explains Dr. Ma.

AI and the cultivation of legal judgment

The broken repetition loop demands a substitute that underscored the implicit teaching of legal judgement in the early years of practice. Simulation-based learning is the profession’s most promising answer, and the idea predates AI.

Moot courts and mock trials have existed for years because of the stark difference between understanding something in theory and executing under pressure. Historically, however, simulation was costly as delivering experiential learning to small groups required significant expertise and time from multiple individuals. AI changes that equation by offering scalability at a level the legal profession never could access before. Indeed, role-playing is one of the greatest strengths of AI models, says Dr. Ma.

The traditional dynamic in legal education, in which law schools teach lawyers how to think, and law firms teach lawyers how to practice is no longer tenable as AI-enabled legal practice grows.

Legal judgment has always been difficult to define and nearly impossible to teach directly. Partners describe it as instinct or as something accumulated after enough transactions, depositions, and hard experience. AI simulation 鈥� if designed with enough precision to force real decision-making 鈥� can create the repetitive environments in which that judgment can be developed.

These AI simulation tools work best when designed around three pillars: i) clear learning goals; ii) realistic unpredictability; and iii) specific feedback.

First, a rubric tied to clear learning objectives needs to be established. According to AltaClaro鈥檚 Liles, this rubric must be paired with a feedback loop that鈥檚 anchored to specific skills and expected judgment calls. AltaClaro has been offering online, simulation-based training to the Am Law 200 for almost a decade and uses AI-powered feedback in its simulation tools.

Second, realistic unpredictability needs to be built in. For example, AltaClaro’s uses a lightly scripted framework that gives the witness a fixed truth and significant freedom within it, offering a scenario with enough unpredictability to force adaptation. This non-determinism makes AI outputs difficult to control in some contexts and becomes the source of realistic pressure in a simulation. The tool currently covers commercial and employment litigation deposition simulations, and there are plans to roll out other deposition scenarios, including IP, securities, mass tort/product liability, and antitrust over the next six months.

To further enable adaptation, Dr. Ma and her team inserted personality dials into liftlab鈥檚 deposition simulation tool. Instructors can push a witness toward the extreme of forgetfulness, evasiveness, or hostility. The user must find a path through behavior that no script could have anticipated. Repetitive use of these tools allows the instinctual learning of legal judgement. Similarly, DepoSim, which uses as its underlying engine, also allows for adjustments in witness cooperation or hostility and the opposing counsel’s aggressiveness.

Finally, feedback is the third critical design pillar. Both tools evaluate the user鈥檚 performance with feedback, which can include instances in which the attorney held their ground, or in which a vague answer was allowed to slide, or when an opening to gain ground was missed entirely. Feedback of this specificity is what allows simulations to most mimic practice and transform repetition into learning.

AI simulation tools work best when designed around three pillars: clear learning goals; realistic unpredictability; and specific feedback.

Of course, user experience is the design element that determines whether all of the above actually gets used. Shayesteh describes the range of ways the DepoSim tool is being used in practice to teach judgement. For example, one litigation chair ran the tool as a live teaching demonstration in front of 500 attorneys and paused to narrate decisions as events unfolded on screen. Also, mentor-mentee pairs are using the tool’s embedded feedback as the foundation for coaching conversations; and associates with upcoming real depositions are using the tool for targeted preparation.

AI simulations in law schools

The traditional dynamic in legal education, in which law schools teach lawyers how to think, and law firms teach lawyers how to practice is no longer tenable as AI-enabled legal practice grows. Dr. Ma says she sees simulation fitting naturally into existing experiential courses such as negotiation workshops, trial advocacy classes, and mediation seminars, serving as a between-class practice layer.

Of course, the greatest benefit of AI simulations in law schools is the creation of safe spaces for students to fail, Dr. Ma notes, describing how the law offers very few environments in which failure carries no consequences. Encountering transactions that go wrong, learning to manage impossible witnesses, and experiencing negotiations that collapse in a controlled setting are invaluable experiences for future lawyers 鈥� and now they can be experienced through simulations.

Although signs of progress are visible across the profession, resistance remains entrenched. “The profession needs to wake up and look at training as a really core strategic piece of the [learning] process,” Lilies says, adding that without intentional, rubric-based simulation infrastructure, the default is handing associates a set of AI tools and pointing them toward the work. This approach produces productivity without judgment and will result in lawyers generating AI output without a full understanding of what makes it right or wrong.

As AI tools proliferate across legal workflows, legal education needs to transform in tandem. “Law schools have to embrace this to really prepare students for the world that is three to four years away, by giving them the opportunity to increase reps and receive feedback based on a structured rubric and framework,鈥� explains Shayesteh. 鈥淚t is the best gift you can give them.”

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Key insights:

• • • AI is changing how lawyers develop judgment and expertise 鈥� As AI takes over more legal tasks, firms must ensure that lawyers still gain the experience, reasoning skills, and confidence needed to become excellent practitioners.

    • Law firm leaders must redesign training for an AI-enabled profession 鈥� Beyond adopting AI, law firms need intentional systems for mentorship, feedback, workflow, and evaluation so AI supports lawyer development instead of weakening it.

    • The best firms will use AI to build better lawyers, not just faster work 鈥� Long-term success will depend on whether firms use AI to strengthen human judgment, critical thinking, and client service, rather than replacing them.

For law firms looking to deliver greater value, AI taps into an obvious opportunity to enhance efficiency, accelerate work product delivery, and reduce expenses. With clients as our guiding North Star 鈥� shaping our decisions and defining our purpose 鈥� this is an opportunity that we enthusiastically embrace.

It is tempting, however, to focus only on how AI is changing the way lawyers deliver legal services as legal teams today publicize their deployment of AI tools and track utilization rates. However, firm leaders also need to ask more fundamental questions: How is AI changing the way attorneys learn? Are the assumptions that we have historically made about how we gained expertise and judgment still accurate, or were we conflating causation with correlation? Fundamentally, what does it mean to be a great lawyer, and how will law firms like ours continue to create great lawyers?

A new model for learning

Law firm leaders are facing a far deeper challenge than driving efficiency through technological adoption. We are now tasked with that produce excellent, client-centered attorneys in an environment in which many traditional development pathways are being transformed.

The core apprenticeship model for lawyer development has existed for thousands of years. The case method of formal legal education 鈥� created around 1869 by Harvard Law School Prof. Christopher Langdell 鈥� is a relatively newer phenomenon, but it is hardly new. Roughly six generations of lawyers in the United States have been on the receiving end of the same basic inputs: Case-based instruction followed by apprenticeship, grounded in repetition and increasing complexity over time.

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It is tempting, however, to focus only on how AI is changing the way lawyers deliver legal services. However, firm leaders also need to ask more fundamental questions.

We reasonably assume that this is how one learns to think like a lawyer 鈥� and how we move talented junior lawyers from 1Ls to senior, expert practitioners. The prevailing belief is that lawyers can only learn judgment by muscling through thousands of genuine problems and through the friction that comes from making and fixing mistakes. Yet, these beliefs are largely inferential. We know how we were educated and how we practice, and we know what resulted. We have evidence about the conditions under which expertise developed, but not definitive proof of causation.

With the advent of AI, truly understanding how we make exceptional lawyers matters enormously. Much of the time-consuming work associated with lawyer development can now be completed, or at least materially assisted, by various AI tools. If these tasks were simply an inefficient use of our time, then nothing much is lost. However, if those efforts were integral to developing legal judgment, then their disappearance creates the real risk that we are weakening the very capabilities upon which our profession depends.

We are, in other words, interfering with a developmental system without understanding which component parts are essential to retain.

Leadership in an AI age

That shift reframes the role of leadership. Leaders cannot simply roll out AI tools and tout productivity gains 鈥� to do so risks losing essential developmental opportunities to gain judgment and expertise and produces lawyers that are little more than a set of hands for AI systems. Yet, ignoring the extraordinary capabilities of AI is not an option, either. Instead, leaders must become systems design architects, structuring legal work, training, and feedback in ways that preserve the conditions most likely to produce exceptional, client-centered lawyers.

To do this, leaders in which AI supplements but does not replace effortful thinking, creates opportunities for reflection and feedback, and ensures that lawyers remain active participants in reasoning rather than passive editors of machine-generated output. All the while, law firm leaders also must create environments of trust and connection, without which great legal teams cannot be built.

Clearly, AI introduces both risks and opportunities into our historical education and development models. Beautifully crafted AI work product can create the illusion of competence but may create scenarios in which lawyers fail to grasp fully the underlying reasoning. Over time, this can lead to cognitive offloading and shallow understanding.

If attorneys rely excessively on AI tools, they risk becoming mere managers of AI-generated outputs. Unless human expertise and judgment are fully integrated with the AI tools, those outputs run the risk of being homogenized. AI can also create fear for the future, a condition under which it is nearly impossible to learn, and which would reduce human engagement from which essential observational learning occurs. Without internalizing knowledge and gaining genuine expertise, future lawyers may never learn the fundamental judgment needed to solve clients鈥� most complex problems.

At the same time, AI deployed well can become . AI can play devil鈥檚 advocate, create mock negotiation simulations, identify examples created by the profession鈥檚 greatest advocates, and offer access to data sets far too large for human review. Well-trained, bespoke AI tools can also supply immediate, tailored feedback on work product 鈥� something universally seen as essential to growth but too often in short supply.

We may learn that expertise can be developed with AI-enabled tools far faster than our traditional model has suggested, given that few legal work environments have ever been able to provide feedback with the speed and frequency that AI could supply.

Indeed, we may learn that expertise can be developed with AI-enabled tools far faster than our traditional model has suggested, given that few legal work environments have ever been able to provide feedback with the speed and frequency that AI could supply. AI should be able to expand access to guidance previously limited by time, ego, and hierarchy, effectively supplementing traditional mentorship structures.

These tensions point to a central conclusion: Leaders, and not AI alone, will determine the future of the legal profession. Strong leaders will engage deeply with the question of how we create great lawyers, critically examining to gaining expertise, creativity, passion, and judgment. They will simultaneously challenge the notion that how the last six generations learned is the only way to learn, using AI as a catalyst for reconsidering how we can become even better at our craft.

The new rules of professional growth

Some design elements already seem essential. First, legal work should be performed in a manner that preserves active, deep thinking. This may impact the sequencing of when and how AI is used, and whether AI serves as a reviewer or a starting point. Second, legal education and development should emphasize the importance of critical thinking, of understanding the questions to be answered, the rule of law, and the meaning of justice. Indeed, attorneys should be judged on their work quality, not just quantity, with emphasis on sound judgment and nuanced, client-centered advice. Because you get what you measure, evaluation and compensation systems should overtly take expertise, creativity, and deep analytical skills into account.

Third, legal teams should be purposeful about developing the most human of skills 鈥� connectivity, trustworthiness, integrity, and resilience. This inevitably means spending time with other people, not just machines. Finally, organizations must maintain robust feedback loops, ensuring that human mentorship remains central even as AI tools become more prevalent.

At its core, this is a question of professional identity. The goal is not simply to produce lawyers who can use AI to deliver passable work products, but to develop lawyers whose judgment, adaptability, and commitment to client service are enhanced by new capabilities. AI has the potential to elevate the profession by enabling deeper analysis, access to greater knowledge, and more efficient, responsive service.

Law firm leaders can determine which of these futures emerge in their organizations. The pace of change is breathtaking, requiring us to move at light speed while answering truly fundamental questions. Leaders must embrace AI with optimism, but not uncritically, and build systems in which AI serves as a tool for learning and growth rather than a substitute for human development.

In the age of AI, we can continue to think like lawyers and be even better ones.

You can find out more about the challenges law firms face with

Key insights:

• • • Lawyers need two types of judgment 鈥� AI is exposing gaps in legal judgment and business judgment, both of which attorneys need to differentiate their value as automation increases.

    • Legal and business judgment are not the same skill 鈥� Legal judgment produces lawyers who reason well about the law; business judgment produces lawyers who can translate that reasoning into something a business partner can understand and act upon.

    • Business judgment is essential in the AI era 鈥� Business judgment is the translation layer between legal analysis and business action, and it has emerged as a key part of the value proposition for lawyers in an AI-powered profession.

Every conversation about AI and its impact on how lawyers will learn judgment that is happening right now assumes the profession knows what judgment is. Yet, we鈥檝e spoken to two practitioners who demonstrate how differently they interpret what judgment is: One is talking about the ability to reason like a lawyer; and the other is talking about the ability to act like a business partner.

Both of these interpretations matter, and both are in the spotlight because of AI. Yet, the legal profession’s near-total focus on legal judgment, while remaining almost entirely blind to business judgment, may be a consequential mistake.

Significant discussion about legal judgment

The question about how to teach legal judgment in the age of AI within legal education is urgent and well-founded. For decades, junior lawyers have learned by doing, with legal instincts accumulated through repetition and proximity to experience.

鈥淭he whole model that corporate clients would subsidize the learning of junior lawyers is all going away [because of AI],鈥� says , founder of Creative Lawyers, a consulting and advisory service dedicated to transforming the future of legal practices. 鈥淐orporate clients already hated it, and now they have a way to say, 鈥業’m absolutely not paying for this.鈥欌��

The research, drafting, and document review tasks that once served as the informal training ground for legal judgment are those that AI is absorbing the fastest. The profession is right to sound the alarm. AI-powered simulation and knowledge tools are emerging as credible responses, and Leonard herself sees genuine promise in them. Now, firms can use decades of document management data to create AI-powered coaching environments, pattern-matching a partner’s stylistic preferences so associates can calibrate their work before it lands on a senior lawyer’s desk, she explains, adding that, unfortunately, inertia and the industry鈥檚 resistance to change have emerged as structural obstacles to this advancement.

Development of business judgment is lacking

, CEO at TermScout, a general counsel and product builder of legal and decision systems who has spent years developing tools for legal and business teams, looks at judgment from a completely different place, framing the issue as a practice problem instead of an education one.

The legal profession’s near-total focus on legal judgment, while remaining almost entirely blind to business judgment, may be a consequential mistake.

“Judgment isn’t one skill,鈥� Mack states. 鈥淚t’s a set of small decisions happening quickly: prioritization of what matters, articulation of trade-offs, mapping consequences, and translating all of that into something a business partner can act on.鈥� Her description of judgment is executive decision-making that happens to operate inside a legal constraint. More specifically, she refers to it as the translation layer between legal analysis and business action, or decision-making under constraint. 鈥淚f that translation doesn’t happen, the legal work doesn’t have much effect,鈥� she adds.

Comparing these two viewpoints side by side, legal judgment is focused on producing lawyers who reason well about the law; business judgment goes one step further by describing lawyers who reason well and who can translate that reasoning into something a business can act on.

AI has shined a spotlight on both judgment gaps even as it showcases the value of the AI-enabled lawyer. AI may give you answers, but judgment is deciding which answers matter and what to do. And at a time in which AI can deliver output with some legal reasoning faster, cheaper, and at greater scale than any junior associate, the translation layer is no longer a complement to a lawyer’s value proposition. Thus, that value proposition has to be addressed in an AI-enabled profession.

Why both views need to be addressed

The two judgment problems are equally urgent on the same timeline. New lawyers entering practice right now are expected to be AI-enabled immediately, and if they arrive with only legal reasoning capability and no translation layer, they will be outcompeted by the lawyers who have both legal and business judgment.

The good news is that legal judgment is already taught, but it is not taught evenly. The key question at play is whether the profession is willing to make teaching such judgment more explicit and consistent. Business judgment, like legal judgment, has always been distributed unevenly with the proper understanding of it going to those with the best mentors, the most consequential early experiences, and the greatest proximity to senior decision-makers. Explicit teaching of judgment frameworks, through deliberate simulations could level that playing field in ways the osmosis model never could.

The profession has one word 鈥� judgment 鈥� to teach as two different cognitive capabilities. Closing the gaps on both types requires the profession to stop treating them both as a natural byproduct of legal experience and start treating it as a foundational competency that must be taught deliberately, early, and at scale.

鈥淲hat humans bring to the partnership with AI is judgment,鈥� Mack says, demonstrating the kind of clarity that tends to arrive only after years of building things that work. 鈥淭his is not optional 鈥� it is mission critical.”

You can learn more about听the challenges facing legal talent here

Key highlights:

• • • Three emerging business models, one unresolved tension听鈥� AI is compressing time, which directly threatens the logic of billing by the hour, but the smartest law firms are not waiting for a winner to emerge before building their strategic foundation.

    • Technology strategy and talent strategy are the same conversation 鈥� The talent model must be designed in tandem with the business model, even amid uncertainty, because many of the structural conditions of legal work are changing all at once.

    • The next great lawyer will lead with human skills, not tool proficiency听鈥� Forward-thinking firms are doubling down on their lawyers鈥� curiosity, judgment, client skills, and relationship-building as these capabilities are those that AI cannot replicate.

Every law firm is asking how AI will change the way legal work gets done; but , Chief Legal Operations Officer at , is asking a more consequential question: How will AI change the way legal work gets听paid for?

Planning around 3 law firm business models in the AI era

AI is making law firms more efficient, of course, but efficiency alone does not answer the harder question of how to capture value and how AI-enabled legal services get priced. Olson Bluvshtein sees three paths emerging in law firms:

1. 1. 1. Billable-hour (still) 鈥� The first is the path of least resistance. Firms stay anchored to the billable hour, raise rates, and use AI to move faster and handle more volume, with the idea that more volume will make up the revenue losses of faster work. With this model, however, the client-firm incentive misalignment remains intact, and the fundamental tension between billing for time and AI compressing that time never gets resolved.
      2. Value-based pricing 鈥� The fixed fee pathway also is likely to gain further traction, as it鈥檚 one that many AI-native law firms are pursuing. In this model, value-based pricing creates a natural meeting point between firm and client interests because when incentives align, everyone wins, Olson Bluvshtein explains.
      3. Frontier models rule 鈥� The third scenario is more speculative but worth watching. As foundational models improve, the need for expensive legal-specific tools may diminish. “I could see a scenario in the future in which we don’t necessarily need all the legal-specific tools that are out there,” she says. Even though technology costs historically come down, cheaper tools do not make the business model question disappear, Olson Bluvshtein notes.

Candidly, Olson Bluvshtein admits that 鈥渢he truth is probably somewhere in the middle,” and the firms best positioned for any of these futures are the ones building the strategic and operational foundation now rather than waiting for the answer to become obvious.

Indeed, the most thoughtfully designed business model will fall short without the right talent foundation to support it. 鈥淭echnology strategy and people strategy are not separate conversations,鈥� Olson Bluvshtein says, adding that they are key parts of the same strategy.

Legal innovation consultant reinforces this point in , noting that many aspects of the structural foundation under which the legal profession has operated are changing all at once. This means that addressing the technology strategy separately from the human side, slice by slice, does not make sense.

Boyko says she encourages law firms to take a step back and approach the problem by identifying what the firm will need first in the future and then plan the talent and tech part for that reality.

Aligning the talent model to the future business model

Not surprisingly, a key challenge for law firms right now is that the future is uncertain. Therefore, it is difficult to design a talent model for an uncertain future and an unknown business model. At the same time, there are some known facts, but the unknown aspect is when these certainties will occur.

More specifically, what is known is that there is mounting pressure on the three possible law firm business models because AI is automating the tasks of past junior associates, clients do not want to pay for tasks completed by junior associates, and clients are bringing more legal work in-house, often until the time when the almost final deliverable is handed over to outside counsel for final review.

To explore the right talent model, one experiment that Boyko suggests is to expand the junior associate experience to include rotations through back-office functions, such as knowledge management, professional development, and technology functions.

At law firm Fredrikson & Byron, Olson Bluvshtein says its associate development program is evolving to prepare for the uncertain future based on three current tactics:

• • • Building AI fluency 鈥� This is a near-term imperative that will soon become table stakes. The goal is to move past basic adoption into something more sophisticated and durable. To enable this, the litigation and M&A practices at Fredrikson are actively working with a variety of tools to test prompts that they can then share more broadly with other teams, while also identifying how AI policy guidance will evolve.
    • Accelerating the development of legal judgment 鈥� Shortening the learning curve for developing legal judgment, which includes the ability to supervise and efficiently validate AI-produced work, is the second essential part of the firm鈥檚 talent development framework. Olson Bluvshtein is candid about where things stand. 鈥淚t has not fully happened yet,鈥� she says. 鈥淏ut building the training infrastructure to operationalize this is a stated goal for the year ahead, including formalized curriculum around effectively and efficiently supervising AI output.鈥�
    • Being hyper-focused on the development and recruiting of human skills 鈥� Doubling down on the human skills 鈥� including client development, negotiation, relationship-building, and sound judgment 鈥� that technology cannot replicate are the capabilities that will define the next generation of great lawyers, regardless of which law firm business model ultimately prevails.

This same philosophy is shaping how Fredrikson recruits. Rather than screening candidates for a checklist of AI tools, the firm is prioritizing curiosity, openness, and the ability to demonstrate human skills. Indeed, the firm is looking for lawyers “who are really good at those human skills鈥� and who bring the kind of judgment and adaptability that compounds over time, explains Olson Bluvshtein.

Boyko underscores a similar approach to skills. 鈥淩ight now, the skills needed to be a good lawyer are no longer those rote skills that AI can automate,鈥� she explains. 鈥淚nstead, they are the people skills, the operational skills, and the client skills.鈥�

Of course, moving from broad experimentation to disciplined, firm-wide maturity takes time, and the gap between early movers and late adopters is already widening. Those firms that will define the next era of legal services already are asking how AI changes the way it delivers value and what skills its lawyers will most need 鈥� and not just looking for the next tool to buy.

You can learn more about the challenges facing legal talent here

Key highlights:

• • • Lawyers excel at semantic legal work while AI excels in syntactic tasks 鈥� Syntactic work (document generation, pattern recognition) is where AI excels, but semantic work involving exercising independent judgment, reflecting on consequences, and fulfilling fiduciary duties remains uniquely human.

    • Fiduciary duty as the core of legal relevance 鈥� What distinguishes lawyers isn’t just听whatthey do, but听how and why听they do it. The fiduciary relationship demands human understanding of context, balances competing interests, recognizes unstated concerns, and exercises discretion.

    • 5 hours to deepen or diminish 鈥� The five hours lawyers are expected to gain each week by using AI can either accelerate professional obsolescence or deepen lawyers鈥� relevance, depending on what they do with it.

This is the first of a two-part blog series that looks at how lawyers can keep their skills relevant in the age of AI

Lawyers expect to gain a full five hours per week of worktime due to the efficiency derived from AI use, according to the 成人VR视频 2025 Future of Professionals Report. Yet the fear of job loss among lawyers is rising, as those viewing AI as a threat or somewhat of a threat grew from to almost two-thirds (65%) of those surveyed, according to the 成人VR视频 Institute鈥檚 2026 AI in Professional Services Report.

Many in the legal profession are asking how lawyers are uniquely valuable at a time when machines can process legal information faster and cheaper. The answer lies in understanding the difference between what AI does in processing legal information and what humans do in exercising legal judgment, says , Founding Director of the .

Defining 2 levels of legal work

Understanding what makes lawyers particularly听meaningful听in this current AI moment requires distinguishing between two different levels of legal work in an environment in which AI-enabled information systems are compressing humanity and legal judgment into data points and draining away the storytelling and moral nuance that ground both. According to Lee, these different levels involve the syntactic and the semantic:

• • • Syntactic 鈥� Lawyers process information, generate documents, and recognize patterns at the syntactic level, meaning those tasks in which AI excels and delivers promised efficiency gains. 鈥淭he danger is that we will use this efficiency merely to generate more syntactic volume,鈥� Lee explains, adding that this will result in faster processing of more documents at greater speeds. 鈥淚f we do that, we will have automated ourselves out of a profession.鈥�
    • Semantic 鈥� The semantic aspect of lawyering highlights the irreducible skills of the legal practice, which include exercising independent legal judgment, reflecting on consequences, demonstrating care for clients, and fulfilling fiduciary duties.

This distinction between the semantic level is inherent within the practice of law definition, Lee says, pointing out that many jurisdictions distinguish between “providing legal information” (not practicing law) and “exercising independent legal judgment” (the essence of legal practice).

He also rightly contends that the existential risk facing lawyers is not in AI completing legal tasks, but rather the temptation to reduce lawyers鈥� role to verifying machine output and processing legal information. Conflating these two concepts is a challenge for the legal profession and requires increasing the appreciation for the craft of legal reasoning and judgment.

Making this more difficult is that the current information age complicates this picture by challenging society’s assumptions about reality, consciousness, and the moral meaning of human life 鈥� all at an exponential rate, Lee says. Similarly, AI and information systems threaten to reduce everything, including human beings and law itself, to processable data by stripping away the narratives and meanings that define humanity, he adds.

Semantic qualities of legal judgment

The question of what makes lawyers especially relevant in the AI era is mainly answered in how and why they do what they do, rather than in what they do. For example, Lee points to skills around executing their fiduciary duty and ensuring legitimacy and meaning as key characteristics of lawyers鈥� semantic qualities.

Fiduciary duty 鈥� When a client seeks legal counsel, it鈥檚 legal judgment 鈥� not information processing 鈥� that the client wants. Lawyers, as part of their fiduciary duty to their clients, demonstrate human and legal understanding of the unique context of each case and the consequences of various legal paths forward. This bond of trust between attorney and client demands reflection, consideration, care, and proper purpose.

The fiduciary duty of the lawyer to the client requires balancing competing interests, recognizing unstated concerns, and exercising discretion in ways that honor both the letter and spirit of the law. At the heart of this balance is legal reasoning and professional judgment, which often involves navigating the critical gap between legal rules as written and their meaningful application to human circumstances.

Legitimacy and meaning 鈥� Beyond the fiduciary of care exercised in individual client relationships, lawyers serve a broader purpose in their role to safeguard law’s connection to the narratives of justice and human dignity that legitimize its authority. Indeed, lawyers maintain the connection between law and its humanistic foundations, so that the narratives that give legal authority its legitimacy depend on this connection. 鈥淭he artwork that one associates with the law (in law schools and courtrooms) connects actions and legal judgment of attorneys to the mythic meaning of justice, equality, and the rule of law,鈥� Lee explains.

How to deepen appreciation for the special relevance of lawyers

The five hours that lawyers said they expect to gain each week through AI-driven efficiency represents a choice point for the profession. These hours can either accelerate lawyers鈥� obsolescence or deepen their relevance. To ensure the latter, Lee advises lawyers and legal institutions to examine ways to put those hours to good use by, for example:

Collaborating on apprenticeships 鈥� Bar associations, practicing lawyers, legal service providers, and law schools should consider apprenticeship models that teach professional norms and values through mentorship that allow law students to learn the craft of legal reasoning through guided practice.

Recommitting more fully to legal service 鈥� Law firms and in-house counsel must reclaim humanistic awareness as central to their professional identity. The efficiency gains from AI should be reinvested into semantic work, which include counseling clients, exercising moral judgment, and fulfilling fiduciary duties with greater care and reflection.

Improving legal education 鈥� Law schools must return to the humanistic formation of lawyers, echoing the vision of the pre-2007 , before economic pressures reduced legal education to producing commercially exploitable graduates. In addition, AI ethics must be integrated systemically across the curriculum into doctrinal courses rather than being confined to elective courses.

Looking ahead

The five hours gained through AI represent a defining choice for the legal profession. The special relevance of lawyers in the AI age lies precisely in the human components and semantics aspects of lawyering.

In the concluding part of this blog series, we look at how the legal profession needs to rethink how it trains lawyers in order to prevent AI from eroding legal judgment skills

Key takeaways:

• • • Structural change in firms 鈥� The traditional law firm pyramid, in which junior lawyers perform high-volume work at billable rates, is losing its foundation as AI compresses tasks that once took hours and clients increasingly bring more work in-house.

    • Finding new ways to train 鈥� AI-powered simulations are emerging as a concrete answer to the associate training problem, allowing new lawyers to build courtroom skills faster and fail safely behind closed doors.

    • The associate role isn’t dying, it’s being redefined 鈥� Those law firms that figure out the right mix of legal training, technological fluency, and management skills will have a significant edge over those that are still debating it.

NEW YORK 鈥斕齇n more than one occasion, I have written seriously and at length about the death of the billable hour. I’ve argued that alternative fee arrangements (AFAs) are the future, that the economic logic of hourly billing is irreconcilable with AI-driven productivity gains, and that the industry needs to prepare for a fundamentally different pricing model. I meant every word. I still do.

Yet, at last week鈥檚 one attendee pointed out they鈥檝e been hearing about the death of billable hour since the 1990s. At this point, it’s less a prediction and more of a tradition. Indeed, Matthew Kohel, a partner at Saul Ewing, said despite the legal press coverage connecting AI to the billable hour’s demise that narrative is now entering its third or fourth decade. And Kohel said his firm simply isn’t seeing meaningful client-driven movement toward AFAs.

So let鈥檚 be honest: the billable hour is not dead, and in fact, it may not be even close to dead.

However, if you’re looking for something that is facing a genuine existential reckoning 鈥� something the legal industry whispered about in the early days of generative AI (GenAI) and is now discussing openly 鈥� Legalweek 2026 may have found it. It turns out the billable hour was never the thing in danger, rather it鈥檚 the person billing the hours.

It’s the associate.

The question nobody wanted to ask out loud

The future of the junior lawyer surfaced in virtually every breakout session across the three-days event, and while it may not be the point of inception for the question, it was certainly the moment this idea graduated from a half-whispered aside to main-stage conversation.

Moreover, the problem has grown more urgent since its inception in the early GenAI days, when the question was simply whether a firm would need fewer associates. Now, that question hasn’t gone away, but it’s been joined by harder ones concerning training, hiring, and legal and technical skills. For example, what if AI is already better than a junior associate at some of the tasks that defined the role in the past? And what happens if someone says it out loud?

Someone said it out loud.

If you’re looking for something that is facing a genuine existential reckoning, Legalweek 2026 may have found it. It turns out the billable hour was never the thing in danger, rather it鈥檚 the person billing the hours.听It’s the associate.

During a panel on Measuring What Matters, the conversation turned to client trust. Clients want to know: How can you be sure AI will catch everything? How do you trust it to find what matters across 5,000 pages of documents?

The response from the panel was direct, and it landed like a brick in the room: it’s 5,000 pages, and someone was reading those five thousand pages. That someone is an associate. If that associate 鈥� who, more often than not, is one of the least experienced lawyers in the building 鈥� is the one reading all those pages, why would you trust them to do it better than a machine?

While that question hung in the air during the panel, it does deserve to sit with you for a moment afterward. Because embedded in it is the uncomfortable arithmetic that drives the entire associate question. The traditional law firm pyramid is built on a base of junior lawyers performing high-volume, lower-complexity work such as document review, due diligence, first-pass research, and doing so at rates that generate revenue while the activity is simultaneously (in theory) training the next generation of partners. If AI can do that base-layer work faster, cheaper, and with accuracy that one panelist described as “beyond very good,” then the pyramid doesn’t just shrink. It loses its foundation.

Barclay Blair, Senior Managing Director of AI Innovation at DLA Piper, noted that tasks like due diligence on some types of financial contracts are already being compressed to two hours, down from 15 to 20 鈥� with zero hours being a realistic possibility in the near future.

Further, as one attendee observed, clients increasingly are adopting AI internally, and they’re bringing work in-house that was previously sent to outside counsel. Clearly, the work that trained generations of associates isn’t just being automated 鈥� in some cases, it’s leaving the firm entirely.

Fewer reps, greater weight

Yet here is where it would be easy (and wrong) to write the doom-and-gloom version of the future, in which AI replaces associates, the pipeline collapses, nobody knows how to train lawyers anymore, civilization crumbles, etc. It’s a clean narrative, but it’s also not what Legalweek panels actually said.

Because alongside the anxiety, something else was happening. People were building answers.

In another panel, Developing the Future Lawyer, panelists spent an hour in the weeds of what associate training actually looks like when the old model breaks down 鈥� and the conversation was far more concrete than you might expect.

Panelist spent an hour in the weeds of what associate training actually looks like when the old model breaks down 鈥� and the conversation was far more concrete than you might expect.

Panelist Abdi Shayesteh, Founder and CEO of AltaClaro, laid out the core problem with precision, noting that there’s a growing gap in critical thinking among associates. Templates getting copy-pasted without relevance analysis, and there is a lack of knowing what you don’t know. And the traditional training methods such as videos, lectures, and passive learning, don’t fix it. Indeed, those outdated models may be making it worse. Shayesteh鈥檚 analogy was blunt: You don鈥檛 learn to swim by watching videos 鈥� you need to jump into the deep end.

His solution is AI-powered simulations. Not hypothetical ones, but working deposition simulations available today, with real-time AI feedback, in which associates can practice cross-examination, deal with opposing counsel objections, and build the muscle memory that used to require years of live experience.

Kate Orr, Managing Director of Practice Innovation at Orrick, picked up the thread with two observations that reframed the stakes. First, AI simulations allow associates to fail behind closed doors, a radical improvement over the old model, in which blowing it had real consequences because failure often happened directly in front of the partners Second, the tool isn’t just for juniors. Even experienced lawyers are using simulations to test different approaches, tweak personas, and sharpen arguments. Orrick’s own Supreme Court team had a lawyer use AI to review a draft brief and identify paragraphs that could be tighter.

Todd Heffner, Partner at Smith, Gambrell & Russell, said the real question isn’t whether associates will use AI, but rather whether it gets them to lead at trial in year 10 instead of year 20. Right now, most associates are lucky to see the inside of a courtroom in their first seven years, and even then, they spend most of their time back in the hotel prepping for the more experienced attorneys instead of arguing themselves. If simulations can compress that learning curve, the associate’s career doesn’t disappear, rather, it gets accelerated.

The dinosaur that adapted

During the Measuring What Matters panel, Mitchell Kaplan, Managing Director of Zarwin Baum, introduced himself with a memorable bit of self-deprecation: He’s a dinosaur 鈥� but one, he clarified, who understands how AI can revolutionize what he does.

Kaplan’s perspective threaded through both days of programming like a quiet counterweight to the anxiety. He’d seen this before 鈥� not AI specifically, but the fear of it. He watched the legal industry transition from physical libraries to digital research tools, and he watched attorneys adapt. And his message was consistent: the work changes, but the need for lawyers doesn’t disappear. Associates may be taking shortcuts, but they still need to read, still need to review, and still need to think.

They’re developing differently than his generation did, Kaplan said, but it鈥檚 the same way every generation develops differently from the one before it. And different doesn’t mean wrong.

The work changes, but the need for lawyers doesn’t disappear. Associates may be taking shortcuts, but they still need to read, still need to review, and still need to think.

It’s a perspective that found an unexpected echo in the Enterprise Alignment panel. Mark Brennan, a partner at Hogan Lovells, relayed a comment he heard at a previous AI conference: The next generation of entry-level jobs will be managers 鈥� because they’ll be managing agents and other tech tools. Brennan admitted he didn’t have all the answers on what that means for legal training, but the implication was clear. The associate role isn’t dying, instead, it’s being redefined. And the firms that figure out what that redefined role looks like, what mix of legal training, technological fluency, critical thinking, and management skills it requires, will have a significant advantage over those firms that are still debating it.

Another panelist, Andrew Medeiros, Managing Director of Innovation at Troutman Pepper Locke, made a prediction that felt like the sharpest version of this idea. He said that at some point, new lawyers are going to be doing simulated matters as a standard part of the development process. Eventually, there’s going to be a generation that walks in as new attorneys and finds themselves litigating right away.

That’s not the death of the associate. Rather, that’s the beginning of a different kind of associate 鈥� one who arrives at the courtroom sooner, with different preparation, carrying different tools.

The billable hour, for all the prophecies, refuses to die. The associate, it turns out, has no intention of dying either 鈥� just evolving. Mitchell Kaplan called himself a dinosaur 鈥� but Legalweek was full of dinosaurs, and every one of them was adapting and in that adaptation, thriving. The harder question is whether the firms that forged them will be brave enough to follow.

You can find more of听our coverage of Legalweek events听here

Key insights:

• • • Client expectations around AI have shifted from curiosity to accountability 鈥� Law firms are now being asked not just whether they use GenAI, but to prove how it delivers measurable cost savings on specific matters 鈥� a question most firms still cannot answer with hard data.

    • A growing contradiction defines firm/client relationships 鈥� As clients simultaneously demand AI adoption, require granular billing transparency, and in some cases refuse to pay for work performed with AI, they鈥檙e creating a pricing and value paradox with no clear resolution for their law firms.

    • The ROI challenge around AI is fundamentally a relationship problem 鈥� Driven by a widening gap between what clients expect to save and what firms can demonstrate, a rift has developed between clients and firms, which is compounded by the fact that few firms have a coherent GenAI strategy in place.

NEW YORK 鈥� opened with a keynote conversation featuring Mindy Kaling, the Emmy-nominated writer, producer, and Tony Award-winning playwright, who reflected on a career built around one enduring fascination: messy relationships. She talked about growing up wanting to write something like Sex and the City, only to end up helping to chronicle the internal politics of a Scranton, Pennsylvania paper company in The Office. She talked about her love of watching people navigate breakups and power struggles and then finding the comedy in it all.

If she’s looking for new material, the three standing-room-only panels that followed could keep her busy for seasons.

Not surprisingly, the relationship between clients and their law firms has always been complicated 鈥� bound by mutual need but strained by competing incentives. Now, that tension is starting to reach a rolling boil as many law firms can鈥檛 seem to agree on exactly how the gains of their use of AI tools, especially generative AI (GenAI), are going to be split, or even if they鈥檙e going to be split at all.

AI is no longer optional or experimental 鈥� and many clients simply assume it’s already in use.

Across three 成人VR视频-sponsored sessions during this week鈥檚 Legalweek event, that tension surfaced again and again 鈥� not as a future concern, but as a present reality. Today, clients are arriving at the table more informed, more demanding, and more willing to use AI themselves. Firms are investing heavily in AI, but they still are struggling to quantify returns in terms their clients will accept. With the rates that law firms charge increasing 鈥� averaging more than 7% growth in 2025, and likely to stay on that pace in 2026 鈥� it sets up a collision with savings mandates that have yet to produce a shared framework for measurement. And underneath all of it, a fault line is building pressure 鈥� one that, as Ellen Hudock, GSK’s Chief of Staff Legal and Compliance, is not being resolved.

In 2026, GenAI has become the thing neither side can stop talking about, the thing both sides agree matters, and the thing that neither side can agree on how to handle.

This is not the story of an industry resisting change. Nearly everyone at Legalweek agreed that AI adoption is no longer optional. The harder questions, however, and the ones that echoed through every panel, every audience comment, and every hallway conversation is who benefits, how much, and who gets to decide.

Proving AI鈥檚 path to saving clients money

Three years ago, the client question was simple: Are you using AI, and would you use it on our matters? In 2026, that question has matured, and the new version is much harder to answer.

GSK鈥檚 Hudock described the shift bluntly during one panel. GSK is learning as much as it can from its outside law firms about how they’re deploying GenAI, she said, and are always looking to partner on new use cases. However, she noted that the conversation has moved well past curiosity. The pressure to deliver savings 鈥� internally and externally 鈥� is intense, and the questions have sharpened accordingly: What are you using? How are you using it? How does it generate savings?

Clearly, firms are hearing this message. Matthew Beekhuizen, Chief Pricing and Innovation Officer at Greenberg Traurig, noted that the pace of AI-driven change has accelerated sharply, particularly since October 2025. Clients who had previously said nothing about AI are now asking how it’s being used on their specific legal matters.

Indeed, AI is no longer optional or experimental 鈥� and many clients simply assume it’s already in use, said Mark Brennan, a partner at Hogan Lovells.

The trouble is that firms still can’t give clients the answer they most want to hear. When pressed on how much cost savings AI is actually achieving, the response from the firm side is often: We’re still gathering the data. Mitchell Kaplan, Managing Director of Zarwin Baum, acknowledged the industry is still in the anecdotal phase of measuring returns.

Sergey Polak, Director of Technology Innovation at Ropes & Gray, described the current state of ROI measurement as being based more on conventional wisdom rather than hard evidence. Hudock’s response to this was pointed: That’s exactly the situation in which clients want to partner. Supply the work, and let’s figure it out together.

The contradictions in the room

If the evolution in client expectations were the whole story, it would be manageable; however, the reality is messier than that, because clients are not speaking with one voice.

During another panel, Barclay Blair, Senior Managing Director of AI Innovation at DLA Piper, laid out the contradictions in sharp relief. Blair, who introduced himself as “the extremist on the panel,” is seeing clients who expect AI to be used and are asking how it will achieve specific savings targets. At the same time, many law firms are still receiving directives that feel lifted out of 2023, such as demands for warrants that models are unbiased, and declarations that firms cannot use AI without explicit permission. In 2026, both postures are arriving in the same inbox.

When pressed on how much cost savings AI is actually achieving, the response from the firm side is often: We’re still gathering the data.

The billing conversation captures this tension perfectly. Polak of Ropes & Gray noted that clients are beginning to ask for line-item transparency on invoices 鈥� was AI used on this task, and how much time or money did it save? Simultaneously, as Blair observed, other clients are issuing guidelines stating they won’t pay for certain services if performed by AI. This isn’t clients barring AI outright; rather, its clients demanding firms adopt AI, then using that very adoption as leverage to negotiate a decrease in costs. Not surprisingly, this becomes a self-reinforcing cycle with no obvious exit 鈥� at least, not for law firms.

Meanwhile, Zarwin Baum鈥檚 Kaplan raised a billing paradox that GenAI is making harder to ignore. As AI compresses work that once took hours into minutes, an itemized hourly bill increasingly tells a story that undersells the value delivered. His proposed answer: a return to the single line-item services rendered bill, which actually predated the billable hour. Kaplan then asked whether clients would actually accept it.

The advice to the law firms in the room from DLA Piper鈥檚 Blair was more blunt: Don’t wait for the client to set the terms. Lead the conversation about AI ROI and set the meeting. As Blair described, this is now the time to negotiate how value gets shared, while both sides are still figuring out the rules 鈥� not after one side has already written them.

The pressure hasn’t yet found a release valve

None of these tensions exist in isolation. They are symptoms of a structural mismatch between what clients need from the economics of legal AI and what firms are currently able to demonstrate 鈥� and the numbers suggest the legal industry is less prepared for this conversation than it thinks.

As 成人VR视频’ Steven Petrie pointed out, those law firms with a GenAI strategy are 3.9-times more likely to achieve ROI than those without one. Yet, only 22% of firms have such a strategy, Petrie said. That gap 鈥� between the firms that are thinking systematically about AI’s role in their business and those that aren’t 鈥� may turn out to matter less than the gap between what clients expect to save and what firms can show they’ve delivered.

The ROI question, in other words, is not just a measurement challenge, rather it鈥檚 a relationship challenge. And like all the best relationship drama, the tension doesn’t come from disagreement about whether the relationship matters. It comes from both sides wanting something slightly different from it 鈥� and neither being quite sure if both sides can get what they want.

If Mindy Kaling is still looking for complicated relationships to write about, she knows where to find them. This one鈥檚 going to need a few seasons to work itself out.

You can find more of here

Key takeaways:

• • • Demand slowdown reverses LFFI gains 鈥� The LFFI鈥檚 Q4 2025 dip reflects a modest demand slowdown, marking a shift from rapid post鈥憄andemic rebound to a more stable, steady market.

    • Transactional practices plateaued while counter-cyclical regain momentum 鈥� Transactional practices leveled off while demand in the litigation, bankruptcy, and labor & employment practice areas accelerated, driven by rising disputes, regulatory pressure, and workforce complexities.

    • Clear opportunity for strategic realignment 鈥� Law firms may be able to shift their staffing toward growing counter鈥慶yclical areas, strengthening their pricing discipline and refining their recruiting processes.

After two consecutive quarters of improvements in the 成人VR视频庐 Institute鈥檚 Law Firm Financial Index (LFFI) score, the fourth quarter of 2025 marked a modest reversal in which it fell, albeit slightly to 61. The key driver behind this decline was a deceleration in demand that was meaningful enough to pull the overall score down and may signal that the market is moving into a more normalized rhythm 鈥� less snapback growth and more steady performance.

To understand what this means in practical terms, it helps to look beneath the headline numbers and examine not just what happened in Q4 2025, but also over the last two years. Then, a clear narrative emerges: Transactional work 鈥� M&A, corporate general, real estate, and tax 鈥� was powering the market in Q4 鈥�24 but largely plateaued in Q4 2025. Meanwhile counter-cyclical practices 鈥� litigation, bankruptcy, and labor & employment 鈥� regained momentum during the same timeframe.

Put differently, the practices that powered growth in the last year are fading as measured against their own baselines, while those practices that performed less strongly then are now starting to take the lead for the legal industry.

Practice level demand dynamics

By applying a magnifying glass to each transactional practice鈥檚 behavior over the past three quarters, one can identify a few important contrasts. The practice that stands out for its lowest growth in Q4 2025 is tax 鈥� and, in fact, across the final quarters of the last three years (even when it had a good performance in early 2025), that momentum didn鈥檛 translate to the end of the year. This indicates that tax has constantly posted the weakest demand growth, bottoming out at -0.9% in Q4 2023, when it was again the practice with the lowest growth. Even in the Q4 2024 鈥� a stronger year for most practices 鈥� tax grew only 1.5%, well below both its transactional and counter-cyclical peers.

This persistent underperformance may reflect several factors, such as increased internalization of routine tax work by corporate tax departments, pricing pressure in highly standardized matter types, and slower deal flow in M&A reducing ancillary tax activity. Whatever the cause, tax鈥檚 muted trajectory has had a dampening effect on overall transactional momentum and has acted as a drag on top-level demand growth.

On the other side of the room, counter-cyclical practices strengthened in Q4 2025 after a softer Q4 2024, nearly reaching the same growth that they presented in Q4 2023. Collectively, these practices rose to around 3.2% in Q4 2025, compared to about 1.5% growth in Q4 2024. This represents a true rebound after an unusually strong 2023, which was likely caused by lingering pandemic-related effects and the period鈥檚 surge in inflation.

Litigation leads the pack

Litigation provides the clearest example of this resurgence. During the Q4 2025, litigation led with roughly 4.3% growth, compared to 2.4% in Q4 2024. Indeed, the practice closed 2025 with renewed momentum, making it the standout in performance among major practices.

Litigation鈥檚 acceleration in late-2025 suggests that court systems have fully normalized, backlogs have largely cleared (in relative terms), and organizations are encountering a more contested operating environment. Regulatory scrutiny, geopolitical risk, supply chain disputes, and workforce-related conflicts all contribute to a litigation profile that is less dependent on economic cycles and more tied to the complexity of today鈥檚 business environments.

By contrast, after bankruptcy demand growth surged to 6.4% growth at the height of the pandemic recovery in 2023, the practice area experienced a dramatic cooldown the following year, falling to 0.4% just 12 months later. However, bankruptcy recovered modestly to 2.8% in Q4 2025, although still far below the extraordinary levels seen during its previous spike.

Taken together, these patterns suggest that corporate clients may be contending with a broader set of pressures 鈥� regulatory instability, workforce management complexity, and the downstream effects of post-pandemic backlogs 鈥� that could continue to generate steady legal demand.

Counter-cyclical trends reflect opportunity, not just reactive demand

The upswing in demand growth for counter-cyclical practices is not necessarily a sign of economic turbulence, however. Indeed, it shows the market can be stable and still produce more litigation, it can be cautious and still require restructuring advice, and it can be steady and still demand intensive employment support. The fact that transactional demand continues at a solid, albeit slowing pace, shows that this is not necessarily the recession-boosted practices that are driving law firm performance.

In fact, in a market in which transactional demand has stabilized and disputes and compliance work is rising, many law firms can use the moment to better align their operating model with the practice areas in which momentum is building and by aligning with actual demand.

For example, as litigation, bankruptcy, and labor & employment areas see higher demand growth, a firm may benefit from adding capacity in those areas, improving staffing leverage, and preventing partner bottlenecks. Meanwhile, steady but flattened transactional demand could call for disciplined, pipeline鈥慴ased hiring.

The practices that powered growth in the last year are fading as measured against their own baselines, while those practices that performed less strongly then are now starting to take the lead for the legal industry.

In addition, lower demand for transactional practices can represent an opportunity for law firms to refine their recruitment processes, as recruiters can take the time to seek those candidates whose skill sets offer added value. Prioritizing the hiring of candidates who bring fresh ideas and technological capabilities to support the tech-driven evolution of legal services may be the push some law firms need to meet the expectations of clients that are increasingly demanding greater value for their dollars.

This does not mean transactional work should be deprioritized, however. Instead, firms should adopt a dual鈥憈rack strategy: Optimize and streamline transactional capacity for efficiency, while strategically expanding counter鈥慶yclical teams in the areas in which demand is accelerating.

Making the strategic choice

On the face of it, it seems that many law firms face a strategic choice between doubling down on counter鈥慶yclical practices or continuing to prioritize transactional work. Current demand performance suggests counter鈥慶yclical areas offer the clearer near鈥憈erm opportunity 鈥� they are growing, resilient, and driven by structural forces such as regulatory scrutiny, workforce disputes, geopolitical risk, and more complex compliance environments.

Further, this environment elevates the importance of pricing discipline. As demand normalizes, clients become more price鈥憇ensitive and will expect efficiency and transparent staffing. Litigation and labor & employment may have more pricing power today, but disciplined pricing across all practices is critical for margin stability.

Indeed, the widening gap between transactional and counter鈥慶yclical practices signals a market in transition. The opportunity for firms lies in balancing these dynamics and aligning staffing, pricing, and operations to navigate uneven growth and capture value in a more complex legal environment.

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