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.

Jump to ↓

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 highlights:

• • • AI simulation can replace the “repetition loop” used to train junior lawyers — 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 — 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 — 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’s (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 — if designed with enough precision to force real decision-making — 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’s Liles, this rubric must be paired with a feedback loop that’s 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’s 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’s 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 — 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,” explains Shayesteh. “It is the best gift you can give them.”

You can find more about the

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.

---

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 insights:

• • • Lawyers need two types of judgment — 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 — 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 — 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’ve 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.

“The whole model that corporate clients would subsidize the learning of junior lawyers is all going away [because of AI],” says , founder of Creative Lawyers, a consulting and advisory service dedicated to transforming the future of legal practices. “Corporate clients already hated it, and now they have a way to say, ‘I’m absolutely not paying for this.’”

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’s 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,” Mack states. “It’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.” 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. “If that translation doesn’t happen, the legal work doesn’t have much effect,” 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 — judgment — 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.

“What humans bring to the partnership with AI is judgment,” Mack says, demonstrating the kind of clarity that tends to arrive only after years of building things that work. “This is not optional — it is mission critical.”

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

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:

• • • AI threatens traditional lawyer development — As AI automates entry-level legal tasks like research and writing that historically has honed legal judgment skills, the profession faces a crisis in how new lawyers will develop such judgment abilities.

    • The profession can’t agree on what constitutes “legal judgment” — Unlike other professions, there is no agreed-upon definition of legal judgment or clear standards for when AI should be used.

    • Implementation requires unprecedented coordination and funding — A legal education fund as a proposed solution would require a small percentage of legal services revenue and coordinated action across law schools, legal employers, and state regulators.

This is the second of a two-part blog series that looks at how lawyer training needs to evolve in the age of AI. The first part of this series looked at how lawyers can keep their skills relevant amid AI utilization.

The key skills that comprise legal judgment have received mixed reviews, according to a recent white paper from the Thomson Reuters Institute that advocated for cultivating practice-ready lawyers. The white paper was based on feedback from thousands of experienced lawyers, judges, and law students and raises questions about how legal judgment forms when AI assistance is used for task completion.

notes that calls for “… to accelerate the development of legal judgment early in lawyers’ careers.”

The challenge is that each part of the profession — law schools, employers, state supreme courts (as regulators) — have distinctly separate responsibilities. That means, that in the age of AI, coordination across the entire legal profession is needed, especially as AI reduces the availability of traditional first jobs.

Furlong points out that there is no consensus for what legal judgment is or any agreed upon standards for in what instances AI should be used in legal. To bring clarity to these issues, the white paper proposed a profession-wide model that integrates three critical elements: i) work-based learning that’s modeled on medical residencies; ii) micro-skill decomposition of legal judgment; and iii) AI-as-thinking-partner throughout pedagogy.

Three pillars for an AI-era lawyer formation system

Not surprisingly, overreliance on AI can erode critical analysis and solid legal judgment skills. Addressing these concerns requires a comprehensive reimagining of how lawyers are educated and trained. One solution lies in three interconnected pillars that together form a cohesive system for developing legal judgment in an AI-integrated world.

Pillar 1: Integrate work experience into legal education

Core skills such as legal research, writing, and document review help develop legal judgment; yet these skills could collapse once AI assumes such tasks. The Brookings Institution recently proposed to preserve entry-level professional development in an AI era. This parallels the TRI white paper’s calls for mandatory supervised postgraduate practice as a key part of legal licensure.

While implementing a full residency model presents challenges, several law schools have already pioneered approaches that demonstrate the viability of work-integrated legal education that, if scaled appropriately, could improve new lawyer practice and judgment skills. For example, Northeastern Law School guarantees all students nearly before graduation through four quarter-length legal positions. The program integrates supervised practice into the curriculum so graduates can gain substantial hands-on experience alongside their classroom instruction.

Also, program offers an alternative pathway to bar admission through practice-based assessment rather than the traditional bar exam. The program demonstrates that competency can be evaluated through supervised experiential learning.

Pillar 2: Decompose legal judgment into teachable micro-skills

The legal profession needs to come to a common definition of legal judgment and develop its components to teach the concept effectively. “We can’t teach what we can’t describe,” Furlong says. To develop legal judgment, the profession must define its components, including:

• • • Pattern recognition — The ability to identify when different fact patterns are related to similar legal frameworks and distinguish when superficially similar cases are legally distinct.
    • Strategic calibration and proportionality — This means understanding what level of effort, precision, and risk each matter requires and matching responses to the stakes involved.
    • Reasoning through uncertainty — This is the capacity to make defensible decisions and provide sound counsel even when the law is ambiguous, unsettled, or silent on an issue.
    • Source evaluation and authority weighting — This includes knowing which legal authorities are most suitable and being able to assess their persuasive value.
    • Ethical judgment under pressure — This means spotting conflicts, confidentiality issues, and duty-of-candor moments while maintaining competence and knowing when to escalate beyond expertise.

Breaking down legal judgment into these discrete components makes it possible to design targeted teaching interventions. For example, , former law professor and executive director of , suggests we back into AI-assisted workflows by requiring a short verification log (detailing sources checked, changes made, and why); running attack-the-draft drills (find missing authority, weak inferences, and jurisdictional mismatch); and preserving slow work as formative work (citation chaining, updating, and adversarial research memos).

With judgment skills clearly defined and work experience integrated into training, the profession must then tackle how AI itself should be incorporated into lawyer development.

Pillar 3: AI-as-thinking-partner throughout a lawyer’s career

Warnings that are mounting. The legal profession must provide clear standards for in what instances and how AI should be used, with training in verification and judgment skills. Overreliance on AI could compromise lawyers’ capacity to fulfill their fiduciary duties to clients.

A phased approach in the introduction of AI in legal work helps protect critical thinking while building AI competency. For example, in Year 1, law students could complete core legal reasoning exercises without AI assistance in order to better develop their analytical muscles. In Year 2, students use AI as a research assistant with mandatory verification protocols that teach students to check outputs against authoritative sources. Finally, in Year 3, residencies can immerse students in real-world AI workflows under proper supervision and while providing feedback.

These three pillars form a coherent vision for lawyer formation in the AI era. However, the most well-designed system faces the obstacle of funding.

The challenge of who pays

Perhaps the most difficult part of any overhaul is the cost. The medical residency model works because — up to $15 billion-plus annually — for teaching young medical students to be doctors. Legal education has no equivalent. Without addressing funding, however, even the best reforms will fail.

One idea is to establish a legal education fund that’s supported by an assessment of a small percentage of the legal industry’s gross legal services revenue (while exempting solo practitioners and firms with less than $500,000 in annual revenue). These funds could be used to subsidize thousands of supervised residency placements, fund law school curriculum development, support bar exam alternative assessments, and provide employer training and supervision stipends.

The challenge is that each part of the profession — law schools, employers, state supreme courts — have distinctly separate responsibilities, and that means coordination across the entire legal profession is needed.

This proposal, of course, would require unprecedented coordination and financial commitment from the legal profession. Skeptics might argue that market forces can solve this problem, or that firms will simply create new training pathways, or that AI will prove less disruptive than feared. However, waiting for market forces risks a lost generation of lawyers. The medical profession already when the medical industry’s voluntary reform failed. Only later did coordinated regulatory intervention produce the consistent quality standards the medical industry sees now.

What is clear is that inaction is resulting in degradation of lawyering skills. “Maybe… we need catastrophic external intervention to bring about the wholesale changes we can’t manage from the inside,” Furlong suggests.

However, the question is whether the legal profession will wait for a crisis to force change or act proactively to make the needed changes now, before the crisis hits.

You can learn more about the impact of AI on professional services organizations at TRI’s upcoming 2026 Future of AI & Technology Forum 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 highlights:

• • • Lawyers excel at semantic legal work while AI excels in syntactic tasks — 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 — What distinguishes lawyers isn’t just��whatthey do, but��how and why��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 — The five hours lawyers are expected to gain each week by using AI can either accelerate professional obsolescence or deepen lawyers’ 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 �������� 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 Thomson Reuters Institute’s 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��meaningful��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 — Lawyers process information, generate documents, and recognize patterns at the syntactic level, meaning those tasks in which AI excels and delivers promised efficiency gains. “The danger is that we will use this efficiency merely to generate more syntactic volume,” Lee explains, adding that this will result in faster processing of more documents at greater speeds. “If we do that, we will have automated ourselves out of a profession.”
    • Semantic — 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’ 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 — 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’ semantic qualities.

Fiduciary duty — When a client seeks legal counsel, it’s legal judgment — not information processing — 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 — 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. “The 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,” 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’ 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 — 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 — 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 — 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 — 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 — 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 — 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 —��On 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’s one attendee pointed out they’ve 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’s 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 — something the legal industry whispered about in the early days of generative AI (GenAI) and is now discussing openly — Legalweek 2026 may have found it. It turns out the billable hour was never the thing in danger, rather it’s 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’s the person billing the hours.��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 — who, more often than not, is one of the least experienced lawyers in the building — 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 — 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 — 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 — 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 — 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’s analogy was blunt: You don’t learn to swim by watching videos — 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 — 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 — 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’s 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 — 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 — 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 — just evolving. Mitchell Kaplan called himself a dinosaur — 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��our coverage of Legalweek events��here

Key highlights:

• • • Universities face pressure over pedagogy— Academic institutions are adopting AI as a reputational marker that’s driven by market pressure rather than educational need, creating a risk for students who can work with AI but not independently of it.

    • Entry-level roles under threat— AI is being deployed most heavily to automate the grunt work of entry-level positions in which foundational professional skills are traditionally built through struggle and feedback.

    • K-shaped cognitive economy emerging— Experienced professionals with existing expertise are gaining efficiency from AI, while entry-level workers are losing access to skill-building experiences.

According to Harvard University’s Professional & Executive Development division, innovation is defined as a “process that guides businesses through developing products or services that deliver value to customers in new and novel ways.” Along this journey, professional judgement in decision-making is used numerous times to determine next steps at key stages.

Notably, the word technology is nowhere to be found in this definition — an absence , Assistant Professor of Learning Technologies at the University of Minnesota, has long found revealing. Instead, innovation is framed as creative problem-solving, contextual intelligence, and the ability to work across perspectives. Interestingly, Dr. Heinsfeld adds, none of these require constant automation. In fact, many of them are undermined by it.

However, AI adoption has the real potential to automate away the very experiences that build these capabilities from university lecture halls to corporate offices. With notable data already suggesting that , the risk that the current approaches to AI use in universities and companies are engineering away innovation and professional judgement skills is real, notes , Group Leader in AI Research at Harvard and NTT Research.

Indeed, some observers view AI as the largest unregulated cognitive engineering experiment in human history. Yet, unlike medical drugs that require years of approval and testing, AI systems are reshaping how millions of students think, learn, and make decisions without a comparable approval process or a shared framework for discussing any potential “side effects,” as Dr. Heinsfeld pointed out.

Most worrisome is that AI is being deployed most heavily to automate precisely the entry-level roles where foundational professional skills are built.

So, what happens when an entire generation of future employees learn to delegate judgment before they develop it? And what actions do universities and companies need to take now to avoid this reality?

Risks of universities adopting AI under pressure

For universities, AI “has become a reputational marker, and not adopting AI is framed as institutional risk, regardless of whether an educational case has been made or not,” says Dr. Heinsfeld, adding that this is being driven, in part, by market pressure rather than pedagogical need.

Already, companies can greatly influence universities as employers of new graduates; and as such, AI systems are currently being optimized for speed, agreeability, and accessibility to stimulate ongoing use. However, as Dr. Heinsfeld contends, as universities race to earn the label AI ready without a careful, cautious and detailed understanding of how it may impact students’ cognitive processes, they run the risk of damage to their reputations of pedagogical integrity.

In addition, the “data as truth” paradigm is a complicating factor, she says. Drawing on her research, Dr. Heinsfeld explains how data “is often framed as the idea of being a single source of truth based on the assumption that when collected and analyzed, it can reveal objective, indisputable facts about the world.” Indeed, this ubiquitous mindset across universities and corporations treats data — such as that used to train large and small language models — as objective and indisputable.

Yet this obscures critical decisions about what gets measured, whose perspectives are included, and what forms of knowledge are systematically excluded from AI systems. As Dr. Heinsfeld warns, when data becomes synonymous with truth, “knowledge is what is measurable and optimizable.” This narrows professional judgment to efficiency metrics rather than the interpretive depth, ethical reasoning, and cultural context that are essential for sound decision-making.

Judgment gap widens in workforce downstream

Under the current AI adoption approach, students could leave universities able to work��with��AI but not independently��of��it, a distinction emphasized by Dr. Heinsfeld. Like calculators, AI works as a tool only when foundational skills for its use exist first. Without this, graduates enter the workforce with a critical judgment gap that compounds from their lives as students at college campuses to becoming employees working in corporations.

AI adoption has the real potential to automate away the very experiences that build these capabilities from university lecture halls to corporate offices.

Most worrisome is that AI is being deployed most heavily to automate precisely the entry-level roles where foundational professional skills are built, warns Dr. Tanaka. Indeed, this is exactly the type of grunt work that teaches judgment through struggle and feedback. Over time, overuse of AI will result in quality being sacrificed because critical evaluation skills have atrophied.

Looking into the future, Dr. Tanaka foresees a K-shaped economy of cognitive capacity. Experienced professionals with existing expertise and contextual judgment built through years of experience will gain increasing efficiency from AI. Entry-level workers, however, will lose access to the valuable experiences that build professional judgement. This gap widens between professionals who can independently accelerate their workflows using AI and those whose traditional tasks are merely displaced by it.

Intervention may be able to break the cycle

The pattern is not inevitable, as both Dr. Tanaka and Dr. Heinsfeld explain. Drawing on Dr. Heinsfeld’s emphasis on institutional agency, meaningful intervention will depend on conscious, intentional choices made at every level. Both experts share their guidance for how different organizations can manage this:

Academic institutions — Universities must first recognize that AI adoption is a decision rather than an inevitability and make educational need the North Star for decision-making around AI. In her analysis, Dr. Heinsfeld emphasizes that when vendors set defaults, they quietly redefine academic practice. Defaults shape what is made visible or invisible and what becomes normalized. In AI-driven environments, universities often lose control over how models are trained and updated, what data shapes outputs, how knowledge is filtered and ranked, and how student and faculty data circulate beyond institutional boundaries — especially if decision-making is left to vendors. As a result, the intellectual byproducts of teaching and learning increasingly become inputs into external systems that universities do not govern.

Private entities — For organizations, Dr. Tanaka calls for feedback loops and other mechanisms that will promote more open discussion about AI use without stigma. In addition, companies need to proactively redesign entry-level roles��to ensure these positions continue to cultivate judgment and foundational skills in an AI-driven environment. Likewise, Dr. Tanaka suggests that companies explicitly provide feedback about cognitive trade-offs to employees, fostering an understanding of possible skill entrophy.

Employees — Similarly, individuals working for organizations bear much of the responsibility for making sure critical thinking is enhanced by AI. Indeed, strategic decisions about when to use AI while seeking to preserve cognitive capacity and professional judgement are key.

Looking ahead

In today’s increasingly AI-driven environment, a new paradigm is needed to combat the current operating assumption that optimization from AI is the sole path to progress. And because the current trajectory sacrifices human development for efficiency, the need for universities and companies to choose a different path is urgent — while they still have the judgment capacity to do so.

You can find out more about how organizations are managing their talent and training issues here