Key findings:

• • • Law students understand risks and opportunities of AI use — Almost three-quarters (72%) of students surveyed say they see AI literacy as essential, while an even larger portion (74%) say they also recognize the risks of over-reliance.

    • Student AI adoption is already widespread — Almost 6 in 10 law students use AI several times per week for academic work, but much of this learning is happening through self-education rather than structured teaching.

    • AI guidance in law schools remains inconsistent — Close to a majority (48%) of students report that AI policies vary by professor, and almost one-third (32%) say that their schools do not give them the AI skills needed for their future career.

There is a significant and growing divide between how law students understand artificial intelligence and how legal institutions, such as law schools, are responding to it, according to a new Thomson Reuters Institute white paper.

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2026 Law Student Pulse Survey

The 2026 Law Student Pulse Survey, based on responses from more than 1,800 law students that were collected in April 2026, challenges two assumptions that have long dominated institutional thinking. The first is that students are reckless adopters who use AI to bypass the hard cognitive work of legal education. The second is that students are passive and uninformed consumers of a technology they do not fully grasp. The data shows that neither characterization is accurate.

In reality, 72% of responding students identify AI literacy as an essential professional skill, while 74% simultaneously acknowledge that over-reliance on AI could undermine the development of their own core legal competencies. Holding both of these positions in tandem reflects a level of professional maturity that many institutions have yet to demonstrate in their own policies and curricula.

The survey also exposes a serious institutional gap. Nearly one-third of students report that their school does not provide the AI skills needed for their future legal careers. And nearly half indicate that AI policies vary by professor, leaving students without coherent and consistent institutional guidance on what responsible AI use actually looks like.

Far-reaching consequences

The consequences of this AI-understanding gap extend well beyond the classroom. Students are entering the workforce self-taught and inconsistently prepared, at a moment when legal employers are moving quickly to embed AI fluency into their hiring and development expectations. The profession is at risk of producing graduates who are sophisticated enough to recognize the stakes but underprepared to meet them.

The full white paper outlines specific, actionable recommendations for law schools, bar associations and accreditors, and legal employers to follow to better address this gap in AI understanding.

You can download

a full copy of the Thomson Reuters Institute’s “2026 Law Student Pulse Survey” by filling out the form below:

Key points:

• • • AI capability is jagged — 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 — 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 — 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 — 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 — 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’s a feature of being a unique individual.

When two jagged edges meet

Place the two coastline maps — the human and the AI model — 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 — human or AI — 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 insights:

• • • AI is changing how lawyers develop judgment and expertise — 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 — 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 — 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 — shaping our decisions and defining our purpose — 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 — created around 1869 by Harvard Law School Prof. Christopher Langdell — 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 — 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 — 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’ most complex problems.

At the same time, AI deployed well can become . AI can play devil’s advocate, create mock negotiation simulations, identify examples created by the profession’s 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 — 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 — 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 highlights:

• • • Three emerging business models, one unresolved tension��— 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 — 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��— Forward-thinking firms are doubling down on their lawyers’ 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��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) — 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 — The fixed fee pathway also is likely to gain further traction, as it’s 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 — 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 “the 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. “Technology strategy and people strategy are not separate conversations,” 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 — 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 — 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’s talent development framework. Olson Bluvshtein is candid about where things stand. “It has not fully happened yet,” she says. “But 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.”
    • Being hyper-focused on the development and recruiting of human skills — Doubling down on the human skills — including client development, negotiation, relationship-building, and sound judgment — 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” and who bring the kind of judgment and adaptability that compounds over time, explains Olson Bluvshtein.

Boyko underscores a similar approach to skills. “Right now, the skills needed to be a good lawyer are no longer those rote skills that AI can automate,” she explains. “Instead, they are the people skills, the operational skills, and the client skills.”

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 — and not just looking for the next tool to buy.

You can learn more about the challenges facing legal talent here

Key takeaways:

• • • Agentic AI poised for adoption uptick — Agentic AI is following GenAI’s rapid adoption in the legal industry, with less than 20% of firms currently implementing agentic systems but half planning or considering adoption in the near future, according to a new report.

    • Adoption depends on human oversight answers — Legal professionals are generally optimistic about agentic AI’s potential, but successful adoption depends on explicit guidance about human oversight and the lawyer’s role in maintaining ethical standards.

    • Time to retool AI education? — Agentic AI’s increased autonomy introduces new oversight and ethical challenges for law firms, making targeted education and clear guidance essential to understanding the differences from GenAI.

Over the past several years, law firms and corporate legal departments have turned towards generative AI en masse. At the beginning of 2024, just 14% of all law firms and legal departments featured an enterprise-wide GenAI tool. Just two years later, that number had already risen to 43% of all firms and departments, according to the 2026 AI in Professional Services Report, from the Thomson Reuters Institute (TRI). For large law firms or legal departments, those percentages — not surprisingly — are beginning to approach 100%.

With GenAI adoption now this widespread, legal industry leaders are now turning their attention to two primary initiatives. One, of course, is how to get the most out of the AI tools they already have — a task that is proving a bit elusive. Currently, less than 20% of lawyers say their organizations measure AI’s return-on-investment, and most corporate lawyers say they have no idea how their outside law firms are approaching AI. Thus, instituting not just AI tools, but also an AI strategy is the second top priority for law firms and corporate legal departments in 2026 and beyond.

However, even as the legal industry reaches a tipping point in adopting GenAI tools, technology innovation still continues unabated. Agentic AI has emerged as the next wave of innovation that could change how lawyers work on a daily basis, offering a way to autonomously complete multi-step tasks. For example, agentic AI systems are already being built for the legal industry that independently researches a regulation or law, drafts a document based on the finding, identifies pitfalls, and revises the document, with stops for human guidance only instituted as desired.

According to the AI in Professional Services Report, the legal industry is already making headway towards implementing agentic AI systems. For agentic AI to truly take hold in legal, however, lawyers still require more education around not only how it differs from the GenAI systems they already have in place, but also when and where human intervention needs to occur within an agentic system.

The early stages of agentic AI

Examining current agentic AI adoption for the legal industry almost takes one back in time — two years, to be exact. Following the public release of GenAI in late-2022, many legal industry organizations spent 2023 evaluating and experimenting with AI systems, usually with a small working group of interested guinea pigs. As a result, only 14% of survey respondents said their law firms or corporate legal departments were engaged in organization-wide GenAI rollouts at the start of 2024. However, more than half of respondents said their organizations expected to be rolling out large-scale GenAI systems over the next 1 to 3 years. The intervening two years since then have proved that prediction to be largely true.

Agentic AI usage in the first half of 2026 looks largely similar to GenAI in 2024. The legal industry started to experiment with agentic AI at the beginning of 2025, with an eye towards actual implementation in 2026 and beyond (particularly as legal software providers began to integrate agentic systems into their own products). As such, less than 20% of recent survey respondents say their organization is engaged in widespread agentic AI adoption, but with about half of respondents said their organization is either planning to use or considering whether to use agentic AI in the near future.

By and large, lawyers feel positive about the agentic AI movement. When asked about their sentiment towards agentic AI, 51% of legal industry respondents said they felt excited or hopeful, while just 19% said they felt concerned or fearful. Further, about half (47%) said they actively believe agentic AI should be used for legal work, while 22% felt it should not, with the remainder saying they were unsure. These figures largely track with the sentiments expressed about GenAI in 2024, which have only grown over time from about 50% positive two years ago to two-thirds of all legal professionals feeling positive currently.

This all lends further credence to a rise in agentic AI usage similar to what law firms and corporate legal departments experienced with GenAI over the course of 2024 and 2025. Indeed, when asked when they expect agentic AI to be a central part of their workflow, few have baked agentic systems into their daily work currently, but a majority of legal industry respondents expect it to be central within the next 3 to 5 years.

The unique barriers of agentic AI adoption

Agentic AI does differ from GenAI in one crucial area that may limit its growth potential within the legal industry, however — autonomy. By and large, GenAI systems operate on a back-and-forth basis: Users provide the tool a prompt, receive its output, and then iterate back-and-forth from there. Agentic AI is intended to be more automated by design, only requiring human input at pre-determined points in the process. And that makes some lawyers understandably nervous.

When asked why they might feel hesitant about using agentic AI for legal tasks, the most common answer was a general fear of the unknown, but the second most common answer dealt with the need for careful monitoring and oversight. In fact, some respondents said they were excited about GenAI, but more cautious about agentic AI’s potential.

“Agentic AI, while exciting, to me removes oversight a step too far,” said one such lawyer from a US law firm. “I like the idea of prompting and reviewing a result. It is something else to have a machine have so much autonomy in the actual doing of a thing and potentially acting on my behalf without that very concrete review.”

Agentic AI usage in the first half of 2026 looks largely similar to GenAI in 2024.

An assistant GC at a US company also pointed to potential privacy and security concerns, adding: “The fact that agentic AI operates in a much more autonomous way, with a lack of control from the user, means there are many unknowns that are hidden beneath the process.”

For law firm and corporate legal department leaders looking to potentially implement agentic AI systems into their practice, this means re-thinking what AI education and training will mean moving forward. Beyond that, however, legal AI educators also will need to make sure to pinpoint and perhaps over-explain those specific instances in which human oversight needs to occur in agentic systems. More autonomous does not mean fully autonomous, and particularly for lawyers with ethical duties to their work product, lawyer oversight will in fact be a necessary part of any agentic system.

For law firm or legal department leaders, that means that finding the right balance between efficient workflows and human intervention will be key to agentic AI adoption. And those organizations that can best communicate human-in-the-loop to their professionals up-front will be rewarded with more increased and reliable adoption.

Clearly, lawyers feel positively about the agentic AI future, after all. They just need it spelled out explicitly as to what the lawyer’s role will be in this new paradigm.

“Agentic AI is powerful, but its moral compass must come from humans,” one UK law firm barrister noted aptly. “Lawyers are trained to safeguard fairness, rights, and the rule of law — principles that should guide how AI is designed, governed, and deployed. Hope lies in our ability to shape AI through these values for fairer values for society as a whole.”

You can download a full copy of the Thomson Reuters Institute’s��2026 AI in Professional Services Report��here

Key takeaways:

• • • Employees face contradictory messages or none at all — Nearly 40% of professionals surveyed report receiving conflicting directives about AI usage from clients and leadership, while half report no client conversations about AI have occurred at all.

    • Workers lack feedback on whether their AI efforts matter — Professionals who are experimenting with AI tools without knowing if their efforts are valued are left uncertain about whether investing time in developing AI skills is worth it.

    • Job displacement fears are rising — While employees remain cautiously optimistic about AI usage in their workplace, concerns about job displacement have doubled over the past year.

As generative AI (GenAI) tools flood into legal and accounting workplaces, organizations are deploying powerful technology without giving their employees clear directions on how to use it. Worse, some have received no guidance.

New research that underpinned the recent 2026 AI in Professional Services Report��from the Thomson Reuters Institute (TRI), reveals a disconnect between AI availability and organizational guidance, which is creating confusion that may undermine both employee experience and the technology’s potential value. (The report’s data was gathered from surveys of more than 1,500 legal, tax, accounting, and compliance professionals across 26 countries.)

Employees navigate inconsistent AI policies or none at all

Approximately 40% of the professionals surveyed said they received contradictory guidance from clients and leadership about AI tool usage, with directives both encouraging and discouraging their use on projects and in RFPs. This ambivalence is slowing down decision-making at the front lines — a place in which AI could deliver the most value.

Equally concerning is the fact that half of professionals indicated that no conversations with clients about AI tool usage have taken place yet. And when discussions do occur, concerns about data protection and accuracy are the main topics.

This confusion extends to external relationships as well. More than two-thirds of corporate and government clients remain unaware of whether their outside professional service providers are even utilizing GenAI. And the majority of clients have provided no direction whatsoever to their outside law firms concerning AI use, respondents said.

Organizations often ignore what employees need to know

Perhaps most revealing is how organizations are measuring — or failing to measure — whether their AI investments are paying off. Almost half of respondents said their organizations are not measuring return on investment (ROI) at all. Among the minority (18%) of respondents that said their organizations do track ROI, the metrics they use tell a story about organizational priorities. That fact that internal cost savings and employee usage rates lead the list suggests a focus on efficiency over innovation or quality improvements.

This measurement vacuum has consequences for employee experience. Without clear success metrics, employees lack feedback on whether their AI experimentation is valued, discouraged, or even noticed. The absence of ROI frameworks also makes it hard to justify training investments or dedicated time that allows employees to develop AI fluency.

AI usage doubles while support systems fall behind

AI usage among professional service organizations has nearly doubled over the past year, and professionals are increasingly integrating these tools into their workflows, the report shows. Yet organizational infrastructure that could support this adoption surge lags badly. Most professionals said they expect GenAI to become central to their work within the next two years — but that may be happening without roadmaps from their employers.

In addition, notable barriers in employees’ usage of AI remain. When asked what barriers could prevent their organization from more widely adopting GenAI and agentic AI, almost 80% of professionals cited concerns over inaccurate responses. Other concerns included worries over data security, privacy, and ethical use. Most of these suggest an ongoing lack of trust in GenAI.

The tool landscape adds another layer of complexity. Publicly available tools dominate current usage, with more than half of respondents (57%) citing their use, while proprietary or industry-specific solutions remain largely in the consideration phase. This suggests employees are often self-provisioning AI tools rather than working within enterprise-supported ecosystems. This potentially opens organizations to increased risk exposure because of security gaps, compliance risks, and inconsistent quality.

Employees’ job displacement fears increasing

Despite these challenges, employee sentiment toward AI remains cautiously optimistic. More than half (57%) of respondents said they are either hopeful or excited about the future of GenAI in their industry. Clearly, employees see AI’s potential to enhance their efficiency, automate routine tasks, and free up their time for higher-value work.

At the same time, hesitation and concern among employees are rising, particularly around accuracy, job displacement fears, and the unknown implications of autonomous AI systems. Notably, concerns about job displacement have doubled over the past year, and this trend demands organizational attention and transparent communication about a workforce strategy to combat this concern.

What organizations need to do now

Organizational leaders who are serious about positive employee AI experiences need to step up their efforts to provide guidance to employees and gain the ROI that AI promises. Specific steps they can take include:

• • • Draft clear and consistent guidance — Create explicit policies for employees about in which instances AI use is encouraged, required, or prohibited. This includes client communication protocols, data-handling requirements, and escalation procedures in those situations in which AI outputs seem questionable.
    • Develop and implement meaningful ROI metrics — Organizations must move beyond usage rates and cost savings as key success measurements. Tracking data points that capture quality improvements, time redeployed to strategic work, and client feedback on AI-enhanced deliverables present a more comprehensive picture. Also, leaders need to share these metrics transparently in order to give employees an understanding about organizational priorities.
    • Invest in structured learning — The survey shows professionals are experimenting with dozens of different tools from ChatGPT to specialized legal tech platforms. Organizations should curate recommended toolsets, provide hands-on training, and create communities of practice in which employees can share effective prompts and use cases with other users.

Our data shows that the employee experience around AI adoption reveals a workforce that is hopeful but hungry for direction and concerned about job impacts. Leaders who implement these actions effectively are more likely to unlock the strategic value that AI promises while building the trust and competence needed for their organizations and its employees to thrive in an automated future.

You can download a full copy of the Thomson Reuters Institute’s��2026 AI in Professional Services Report��here

Key findings:

• • • AI adoption accelerates across professional services��— Organization-wide use of AI in professional services almost doubled to 40% in 2026, with most individual professionals now using GenAI tools, and many preparing for the next wave of tools such as agentic AI.

    • Strategic integration and measurement lag behind usage — While AI use is widespread, only 18% of respondents say their organization tracks ROI of AI tools, and even fewer measure AI’s impact on broader business goals such as client satisfaction or revenue generation.

    • Communication around AI use remains inconsistent��— While most corporate departments want their outside firms to use AI on client matters, less than one-third are aware whether their firms are doing so. Meanwhile, firms report receiving conflicting instructions from clients about AI use, highlighting a need for clearer dialogue and shared strategy around AI adoption.

Over the past several years, AI usage within professional services industries has come into focus. As we enter 2026 in earnest, the early adoption phase of generative AI (GenAI) has come and gone. Today, most professionals have experimented with some form of GenAI, and many organizations integrated GenAI into their workflows — and now, a number are preparing for the next wave of technological innovation such as agentic AI.

Given this, the question for professionals and organizational leaders has now become: What will be AI’s long-term impact on my business?

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2026 AI in Professional Services Report

To delve into this question further, the Thomson Reuters Institute has released its 2026 AI in Professional Services Report, which takes a broad view into the current usage and planning, sentiment towards, and business impact of AI for legal, tax & accounting, corporate functions, and government agencies. Taken from a survey of more than 1,500 respondents across 27 different countries, the report finds a professional services world that has embraced AI’s use but is continuing to evolve business strategy around its implementation.

For instance, the report shows that to 40% in 2026, compared to 22% in 2025 — and for the first time, a majority of individual professionals reported using publicly-available tools such as ChatGPT. Additionally, a majority of respondents said they feel either excited or hopeful for GenAI’s prospects in their respective industries, and about two-thirds said they felt GenAI should be applied to their work in some manner.

At the same time, however, many are exploring GenAI tools without much guidance as to how that use will be quantified or measured. Only 18% of respondents said they knew their organization was tracking return-on-investment (ROI) of AI tools in some manner, roughly the same proportion as last year. And even among those tracking AI metrics, most are tracking mainly internally-focused, operational metrics; and only a small proportion analyzed AI’s impact on their organization’s larger business goals — such as client satisfaction, external revenue generation, and new business won.

This slow move to strategic thinking also impacts client-firm relationships. Although more than half of both corporate legal departments and corporate tax departments want their outside firms to use AI on client matters, less than one-third said they were aware whether their firms were doing so or not. From the firm standpoint, meanwhile, confusion reigns: 40% of firm respondents said they have received orders both to use AI on matters and not to use AI on matters from various clients.

Indeed, bout three-quarters of corporate respondents and firm respondents agreed that firms should be taking the lead in starting these conversations around proper AI use. Yet these discussions have not yet happened en masse. “Firms are reluctant — they claim it would compromise quality and fidelity,” said one U.S.-based corporate chief legal officer. “I think they are threatened by it.”

All the while, technological innovation progresses ever quicker. This year’s version of the report measures agentic AI use for the first time, finding that already 15% of organizations have adopted some type of agentic AI tool. Perhaps more interesting, however, is that an additional 53% report their organizations are either actively planning for agentic AI tools or are considering whether to use them, indicating perhaps an even more rapid pace of adoption than we’ve already seen with the speedy rise of GenAI.

Overall, the report makes it clear that most professionals do understand that change, driven by AI in the workplace, is undoubtedly here. Even compared with 2025, a higher proportion of professionals said they believe that AI will have a major impact on jobs, billing and revenue, and even the need for legal or tax & accounting professionals as a whole. The percentage of lawyers calling AI a major threat to the unauthorized practice of law rose to 50% in 2026 from 36% in 2025.

Further, this report paints the picture of a professional services world that has embraced AI, begun to see its impact, and realized that it will have broader business and industry implications than previously imagined. As a result, the time for professionals and organizations to begin planning in earnest for an AI future has already arrived.

As a corporate general counsel from Sweden noted: “We cannot keep up with the modern-day corporations’ demands unless we also develop and adapt our way of working.”

You can download

a full copy of the Thomson Reuters Institute’s 2026 AI in Professional Services Report��here

Key findings:

• • There’s a gap between AI awareness and understanding — The recent 2025 Future of Professionals Report shows that while 96% of professionals have at least a basic awareness of AI, 71% lack a strong understanding of its practical applications. This gap limits professional services organizations from truly maximizing their investment in AI tools.

  • AI strategy drives both professional development and ROI��— Professionals with good or expert AI knowledge were found to be 2.8-times as likely to see organizational benefits from AI when compared to those with lesser knowledge. Similarly, companies and firms with a visible, top-down AI strategy are 3.5-times as likely to see positive returns on investment from AI. Both show a clear benefit to aligning skills training and overall organization AI strategy.

  • Identifying and addressing AI-related skills gaps is essential — Nearly half of professionals said they see skills gaps within their teams, including both technical and soft skills needed for successful AI adoption. Leaders who identify these gaps and tailor AI training to specific team needs will maximize the benefits of AI in the workplace.

In the nearly three years since ChatGPT introduced generative AI (GenAI) to a wide public audience, AI applications have increasingly been making their way into business tools and workflows. Whether AI, GenAI, or (increasingly) agentic AI, professionals in the legal, tax & accounting, and government industries have been introduced to new AI concepts at a dizzying speed.

As many professionals try to keep up, they are understandably having trouble staying ahead of the pace of change. According to results from the recent �������� 2025 Future of Professionals Report, which examines the trends impacting professionals careers, most professionals at this point know what AI can do. Where they’re struggling, however, is making the next step in determining how those use cases apply to them. This leap is even more pronounced with more senior members of many organizations, the report shows.

Although many professionals now have access to these next-generation tools, it’s clear that despite their best efforts, some don’t quite know how to apply AI, GenAI, and other related technologies to the best of their ability and for maximum advantage. For senior leaders of organizations, this means a change in approach is needed — and that may mean crafting an overarching AI strategy that allows professionals to achieve real goals that will also help the organization at large.

A gap between awareness and understanding

The idea that professional services organizations are behind the times with regards to technology may be an antiquated concept. Law firms of all sizes, for instance, have continued to invest in technology at a rate above inflation for the past decade, according to the Thomson Reuters Institute’s Law Firm Financial Index. Studies in tax & accounting and government have yielded similar results. Further, the interest in GenAI has amplified, with many large organizations adopting GenAI technologies, and even beginning to build their own proprietary systems.

With this in mind, it’s not surprising that the Future of Professionals Report found that 96% of surveyed professionals said they have some basic awareness of AI capabilities. AI has been baked into the systems that underpin daily work product and back-office functions at a rapid pace.

However, when asked whether they have an understanding of AI’s practical applications, rather than simply awareness, many professionals begin to falter. In fact, 71% said they feel they do not have a good understanding of the practical applications of AI to their own careers. This percentage is even higher for the Baby Boomers, who due to seniority are more likely to hold positions of leadership.

There are a number of reasons why this gap can have occurred, according to the research. For one, less than half (39%) of all professionals say they have personal goals linked to AI adoption, creating less of an impetus to actually setting aside precious time to discover the practical applications of these tools. Some professionals also reported that they do not feel like they have input into AI policy, or do not feel encouraged to explore new ways of working, particularly at more junior levels.

The business implications of this are clear. The research found that knowledge of AI’s applications has a direct correlation with receiving benefits from AI’s use in the organization, as professionals with good or expert AI knowledge were found to be 2.8-times as likely to see organizational benefits from AI when compared to those with lesser knowledge.

The evolution of the modern professional

Given the rapid rate of AI adoption, it’s no surprise that corporations and firms alike are increasingly looking to develop more business strategy around AI usage. And indeed, the Future of Professionals research indicates that organizations with a visible AI strategy are 3.5-times as likely to be experiencing at least one form of positive return on investment from overall AI usage.

So, how does that top-down strategy filter down to legal, tax & accounting, and government professionals themselves? According to the research, the discrepancy between AI awareness and AI understanding is not a matter of desire. Professionals want to be upskilled in this area. In fact, more than three-quarters of professionals said they are voluntarily reading reports and articles about AI in their industry, and more than two-thirds have said they’ve voluntarily experimented with AI tools or held informal learning sessions with their colleagues.

Yet, the difference between awareness and understanding remains, even with these increased opportunities for learning. According to the research, however, it’s not one way of learning alone that lends itself best to closing this gap. Instead, the biggest predictor of AI proficiency is engaging in a wide variety of learning methods, both on an organizational and personal level. Put another way, it’s a plan for comprehensive training and education, rather than simply a single training session or a module.

This clearly indicates that organization leaders need to take an active role in developing a more comprehensive strategy to convert awareness into understanding — and map AI understanding to what skills their teams need to grow.

Almost half of professionals reported skills gaps within their teams that are need to be addressed before the team can become a fully actualized contributor to the organization. In many cases, these skills gaps may be technology or data skills and could include the ability to use technologies such as GenAI. In other cases, however, there may be gaps in more soft skills, areas that touch technology but are not inherently technical — such as organizational and efficiency skills, interpersonal effectiveness, and higher-order thinking.

Closing the gap between AI awareness and AI understanding will not be the same for every person and every team. The most effective leaders will be the ones who take the time to identify where those gaps exist and determine the specific use cases in which AI can be leveraged to aid those deficiencies. As the 2025 Future of Professionals Report shows, however, taking this time can yield tangible results — both in getting the most out of these new technologies and helping professionals reach their true potential in an AI-enabled future.

You can download a copy of���������� Future of Professionals 2025 here

However, as global trade continues to grow — both in volume and complexity — there are growing concerns whether existing skills and training are keeping pace with today’s needed requirements of the profession.

The double-edged sword of technology

Technology is helping organizations deal with the growing complexity — automating numerous tasks that are otherwise laborious and time-consuming, and handling everything from the voluminous paperwork involved in shipping, logistics, and customs to drafting contracts and translating documents. And this has enabled businesses and organizations to greatly improve their efficiency, even as global trade complexity increases.

However, Rowden says she is worried that the growing use of technology could be eroding some core skills needed by global trade professionals. Many senior trade professionals gained their experience over recent decades during which global trade grew tremendously, providing them with critical grounding and foundational knowledge of how the current trade systems and policies evolved into their current state.

Citing customs as one example, Rowden explains that while specific details and policies may differ between countries, the primary building blocks are generally similar. This is largely because many of the key attributes — such as place of origin, classification, valuation, bills of lading, intellectual property ownership, etc. — have been defined, measured, and tracked literally since the dawn of cross-border trade centuries ago by caravan and sea. While processes have grown more sophisticated over time, the fundamental concepts remain largely the same.

“It’s like playing 3D or even 4D chess in order to balance and manage the multiple factors, issues and scenarios that are involved every day.”

There have been significant shifts in recent decades in how global trade professionals are trained, however, Rowden says. One catalyst was the aftermath of the events of the 9/11 terrorist attacks, which prompted a renewed emphasis on security. “While both understandable and necessary, given the circumstances, the increased orientation around security oftentimes came at the expense of core training on many of the basics,” she adds.

Another growing trend has been the emergence into the profession of digital natives — people born after the birth of Internet — a transition that is still taking place. Rowden says she is concerned that the new generation of global trade professionals may be overly reliant on technology and are not as accustom as previous generations to thoroughly validating everything from record-keeping to decision-making. She says she believes it’s critical for today’s trading professionals to understand and question the underlying data, calculations, assumptions, and scenarios rather than simply relying on the outputs contained in a spreadsheet or other application.

“Trade is a highly data-intensive field,” explains Rowden. “I teach global trade as an adjunct professor, and I’ve increasingly noticed that many students can’t discern between different sources of information and have difficulty validating information.

“You may have heard the phrase ‘there is but one truth,’” she adds. “For trade, you want there to be one truth: the system of record. Validation is a critical part of the process for ensuring the integrity of data within the system of record. And not having the necessary expertise for carrying out that validation could pose serious problems. I see this as a growing exposure for both government agencies and private sector companies.”

Need for global trade education

Academia can play a key role in bridging these skills gaps, she says, noting that academic programs may be best equipped to develop the multi-disciplinary curriculum that will be needed. Indeed, today’s global trade environment requires that professionals possess a comprehensive range of skills to tackle the growing complexity of the field.

“There’s an incredible number of moving parts in global trade,” says Rowden. “It requires sophisticated understanding of economic, tax, and trade policies and how they interact, as well as technical skills such as data analytics and management. Developing curriculum to cover all of these fields and integrate them into a global trade discipline will be challenging.” Further, Rowden says she believes that certification programs will be essential to improving and expanding global standardization of trade practices. Currently, however, the industry generally lacks certification programs that are dedicated specifically to global trade.

Looking ahead, the continued adoption of generative AI into trade operations will change how global trade work is conducted, what skills trade professionals will need, and what educational tools will be available and most effective. For Rowden, that is all the more reason why improved education and certification are essential ways to ensure that global trade professionals are in a position to successfully manage global trade as it continues to grow in complexity.

You can download a full copy of the Thomson Reuters Institute’s����here

For professionals in legal, tax, risk & fraud, and government industries, the shift has been both quick and dramatic. Already more than half of all legal, tax, risk & fraud and government professionals have used GenAI in some fashion, with a wide range of use cases already arising, according to the newly published Thomson Reuters Institute’s 2025 Generative AI in Professional Services Report. In fact, the report shows that professionals from across the United States, the United Kingdom, Canada, Latin America, Australia, and New Zealand are not only expecting GenAI to become a more common part of their work, but they’re feeling more positive about the impact GenAI will have on their profession.

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However, even with usage skyrocketing, many of the firms, departments, and agencies in which these professionals work still have a way to go to fully extract value from GenAI. The report shows that few organizations are capturing return-on-investment (ROI) metrics, regularly training staff on GenAI updates, or integrating GenAI use into their policies. In addition, few professionals say their firms and their clients are having conversations around GenAI use. And even more worrisome, questions about GenAI’s impact on billing rates and costs remain unanswered.

This year’s report reflects a crossroads: GenAI itself is seeing adoption on a wide scale, but professional services industries largely still have yet to discern what it means for the future of their businesses.

Key findings in the report

Some key findings in this year’s Generative AI in Professional Services Report include:

• • • Steady usage increases — A large portion (41%) of respondents said they personally use publicly-available tools such as ChatGPT, and 17% said they personally use industry-specific GenAI tools. On an organization-wide level, the percentage of respondents who said their organizations were actively using GenAI nearly doubled over the past year, to 22% in 2025, compared to 12% in 2024.
    • Soon to be central to workflow — Just 13% said GenAI is central to their organizations, workflow currently, but an additional 29% said they believe it will be central within the next year. Further, 95% of all respondents believe it will be central to their organization’s workflow within the next five years.

• • • Maintaining positivity — More than half (55%) of all respondents categorize their sentiment towards GenAI in their profession as excited or hopeful. Meanwhile, the proportion who said they were hesitant, concerned or fearful fell 12 percentage points over the past year.
    • Business questions remain — Only 20% said they knew their organizations were measuring ROI of GenAI, and many firm respondents remain unsure about GenAI’s impact on rates or client costs. And while 57% of corporate clients want their outside firms to be using GenAI, 71% of law firms’ clients and 59% of tax firms’ clients said they did not know whether their firms were using it or not.
    • Policies & training still needed — More than half of respondents (52%) said they believed their organizations had no policies around GenAI at work, whether a standalone policy or as part of a larger technology policy. Nearly two-thirds (64%), meanwhile, said they had received no GenAI training at work.

At this point, it is evident that GenAI is here to stay. Professionals across multiple industries are adopting it for their own personal use in droves, whether leveraging free tools or, increasingly, business or industry-specific technologies. Organization-wide adoption is beginning to occur as well, albeit slowly.

The question, then, becomes what will professional services industries do with GenAI once it’s adopted. As the report reveals, many may not yet know — but they do know they need to begin having those conversations quickly.

“The next 24 months will be extremely telling on the impact of GenAI on the legal industry and professional work more broadly,” said one Australian law firm attorney. “As products move out of development [and in]to production, we will be able to see the actual effects of this technology across various sectors.”

You can download a full copy of the 2025 Generative AI in Professional Services Report here