Key insights:

• • • AI adoption is now a talent retention and recruitment issue 鈥� Law firms that lack professional-grade AI tools risk losing both current and prospective talent.

    • Client relationships are increasingly tied to AI-driven value 鈥� Corporate legal departments expect their outside counsel to use AI to improve productivity, quality, and innovation; however, few believe most of their law firms are meeting those expectations.

    • Law firms must rethink their business and pricing models 鈥� Although many firms feel financial pressure to accelerate AI adoption, most have not adjusted their pricing structures to reflect AI-driven efficiencies.

Law firms are experiencing unprecedented pressure from the rapid advancement of AI, which is affecting their talent recruitment, client relationships, and business models, according to deeper analysis of the recent 成人VR视频听2026 Future of Professionals Report.

To help law firms navigate this AI-driven disruption, 成人VR视频 has published a new action paper, Future of Professionals Report 2026: Actionable insights for law firm leaders, drawing on insights from 736 law firm professionals and 203 corporate legal professionals.

Indeed, the new paper highlights that almost one-quarter of law firm professionals will refuse a job offer if the prospective firm lacks professional-grade AI tools. Further, any perceived misalignment between a professional鈥檚 AI preferences and the firm鈥檚 strategy increase the risk of attrition, especially among those professionals who value mentorship and skill development.

You can download your copy of the听2026 Future of Professionals Report听丑别谤别

In addition, almost one-third of corporate legal professionals say they are reconsidering relationships with outside law firms that do not demonstrate how they鈥檒l offer clear AI-enabled value within the next 12 months, the paper notes. And clients increasingly expect their outside counsel to deliver efficiency, quality, and innovation through AI; however, only between 3% and 6% say they believe most of their outside firms are meeting each of these expectations.

Finally, almost 4-in-10 law firm professionals say they are feeling financial pressure to act faster on AI, yet almost two-thirds say their firm鈥檚 pricing structure remains unchanged despite clients鈥� demand for new models that reflect AI-driven efficiencies and increased value.

Dealing with AI-driven challenges

The paper notes that firms with approved AI tools are more attractive to talent, while the use of unauthorized “shadow AI” by more than one-third of professionals creates security and compliance risks. To address this, firms should provide transparent AI solutions and invest in training. While AI may reduce demand for some junior roles, it may increase the need for others, especially hybrid tech-legal roles.

On the client relationship front, many corporate legal departments are facing internal pressure to adopt AI and expect their outside law firms to keep pace. In-house legal teams increasingly expect AI-enabled productivity, quality, and innovation, yet many see a significant gap between expectations and delivery. For example, 70% say they expect productivity gains, while only 6% say they believe most of the firms they work with are delivering them.

Clients, for their part, also expect pricing models that reflect AI-driven efficiencies through greater cost certainty and transparency. Outside law firms that fail to adapt may risk fee pressure, ultimately losing business to more agile competitors.

Only half of professionals see their firm鈥檚 AI strategy reflected in their daily work, and this potential misalignment could cause talent and AI adoption problems.

Fortunately, amid all these challenges for law firm leaders, the paper identifies three strategic paths law firms can take, including:

• • • Using AI to elevate by automating routine tasks that would then allow professionals to handle complex, high-value work.
    • Using AI to scale by prioritizing productivity and efficiency and handling high volumes of routine work with AI and human oversight.
    • Using AI to reimagine by rebuilding the firm around AI and offering new models like outcome-based pricing and embedded partnerships.

Unfortunately, some firms are choosing to defer this crucial decision, which increases their risk of client and talent attrition as the market evolves.

Whichever path law firms take, however, the paper makes clear that firm leadership must clearly communicate their AI strategy.听The paper notes that only half of professionals see their firm鈥檚 AI strategy reflected in their daily work, and this potential misalignment could cause talent and AI adoption problems.

The paper encourages firms to move quickly to close the gap between client expectations, talent needs, and operational realities by defining a clear AI strategy, investing in training and tools, and adapting pricing models for an AI-driven market.

Using the guidance from this action paper, firm leadership can navigate these challenges and move their law firm into a more responsive, profitable, and sustainable AI-enabled future.

You can read a full copy of the听Future of Professionals Report 2026: Actionable insights for law firm leaders听paper here

Key highlights:

• • • Build on internal momentum rather than top-down mandates 鈥� Dean Kalb backed faculty who were already experimenting with AI, embedding shared learning outcomes into the legal writing program first before expanding to other courses.

    • Empower students to shape the school’s AI policy 鈥� Dean Kalb formed a 15-person student advisory group that surveyed one-third of the student body and produced AI principles that directly influenced school policy.

    • Create opportunities to get students collaborating with faculty 鈥� Efforts by Dean Kalb uncovered shared concerns of faculty and students, underscoring that students often know AI tools better than faculty and creating a co-learning opportunity in the classroom.

In her first six months as dean at the University of San Francisco (USF) School of Law, Johanna Kalb heard the same message from alumni across sectors: Those students entering law school today would step into a profession that looks meaningfully different from the one that exists now.

So, in her first move to translate that urgency into institutional change, Dean Kalb got behind those faculty members who had already started building toward that future.

Start with what is already in motion and invite others in

Dean Kalb started with the efforts that Profs. Nicole Phillips and Megan Hutchinson had already been doing by conducting their own experiments in their classrooms and building their own tools.

Dean Kalb鈥檚 first step mattered as a strategic choice. Rather than convening a task force or commissioning a study, she identified the faculty who had credibility with their peers and gave them resources and institutional backing. In this way, USF was able to embed shared AI learning outcomes across its legal research, writing, and analysis program in the second semester of the 2024-鈥�25 academic year.

The decision to focus on this program was deliberate because it built upon existing internal momentum and fit into the course鈥檚 existing goals. The structure of the legal research and writing program 鈥� with faculty having autonomy while supporting each other 鈥� also made the integration work by providing natural support.

Expand through optional workshops before adding mandates

Over the following summer in 2025, Profs. Phillips and Hutchinson ran optional hands-on workshops for the broader faculty. “Faculty learn from other faculty,” explains Dean Kalb. 鈥淭hey don’t want a vendor to come in and sell them. They’re not interested in having somebody from central administration try to tell them how they can teach better. But listening to a colleague who really understands the work that they do is very helpful.”

Indeed, some faculty showed up, were excited by the possibilities, and began integrating AI learning outcomes into their elective courses. This voluntary uptake created a visible proof of concept before any additional requirements or mandates were introduced.

Alongside the workshops, Dean Kalb also expanded AI learning outcomes into two required courses on evidence and professional responsibility. The professional responsibility inclusion was straightforward given the ethical dimensions of AI use in legal practice. One colleague, Prof. Tiffany Li, had already been building those outcomes into her section and was willing to share her approach with other faculty members who were teaching the same course.

Give students a formal role in shaping the direction

When USF rolled out access to the AI platform Claude across students and faculty, the response was more complicated than Dean Kalb anticipated. Feedback from students at USF 鈥� a Catholic Jesuit institution with a strong social justice identity 鈥� raised questions about AI鈥檚 social, environmental, and democratic impacts.

Dean Kalb intentionally chose to use the students鈥� feedback to involve them. With the help of another alum, who has deep experience in evaluating and implementing emerging technologies, Dean Kalb convened a student group to develop a set of draft principles for AI use at USF Law. The students conducted structured interviews with faculty, staff, and students, resulting in the creation of a survey in which approximately one-third of the student body participated. The student group drew on these results to draft a series of AI principles and presented them to faculty, staff, and other students. Ultimately, the principles were adopted by the faculty.

What came out of that process has already begun to shape the law school鈥檚 AI practices in concrete ways. For example, a faculty technology advisory committee with student representation has been formed to implement the principles to ensure transparency and ongoing oversight. The school also has begun exploring ways to engage with AI that reflect and enhance its social justice mission.

We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.

The more significant outcome, Dean Kalb says, was the discovery of shared concerns among faculty and students that AI would erode critical thinking rather than develop it. “That was probably the most helpful part of the whole process,” she says. “We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.”

Commit to sharing in the learning

Dean Kalb鈥檚 suggestion for her peers and faculty is to integrate AI tools into their own lives, which would allow them to better 听听听听听keep pace with technology that is moving faster than any curriculum committee can match. 鈥淚t鈥檚 hard to regulate and teach these tools in the abstract,鈥� she explains. 鈥淚鈥檝e found that playing around with them in my personal life 鈥� where the stakes are low 鈥� has helped me come up with ideas for their use at work, and that in turn, means that I notice their evolution.鈥�

For a profession built on expertise and the authority that comes with it, this mindset requires a particular kind of intellectual honesty. Some students are beginning to arrive at law school with more familiarity with AI tools than their professors, Dean Kalb adds, and this may offer an opportunity to shift the classroom dynamic toward co-creation, in which faculty and students are building knowledge together rather than transmitting it in one direction.

This change in perspective can, turn the stress of 鈥渒eeping up鈥� into the more enjoyable experience of collaboration, she says.

You can find out more about the impact of AI on legal education here

Key highlights:

• • • The limits of the current system and good intentions 鈥� While the justice gap is not the fault of legal educators, their good intentions alone cannot close a systemic gap that requires new models of training and delivery designed for the long term.

    • A healthcare model for legal services is needed 鈥� Just as the healthcare industry relies on physicians, nurses, and physician assistants, the justice system needs a wider spectrum of trained and regulated legal providers; and American law schools are best positioned to educate, license, and oversee them.

    • States prove the model works 鈥� Alaska, Utah, and Arizona have already developed programs that train and certify non-lawyer legal service providers to help individuals navigate courts and address common legal issues, offering a replicable framework for those states willing to open regulatory doors.

Our nation鈥檚 healthcare system has wisely evolved past being one built on doctors alone. Yet in the legal industry, access to services remains largely tethered to a lawyer-only model that leaves millions of people unable to secure the help they need. Every day, tenants face eviction without representation, parents navigate custody disputes alone, and workers struggle to secure employment benefits or resolve workplace disputes because they cannot pay for legal counsel.

Legal professionals need to work together to create a broader, smarter, and more efficient legal workforce that can meet the public鈥檚 legal needs while maintaining the United States鈥� current legal standards of excellence. American law schools are best positioned to lead this effort; however, they will need to partner with regulators to educate, license, and oversee new categories of legal service providers who, like nurses and physicians鈥� assistants, can help expand the public鈥檚 access to critical support.

Preserving excellence while expanding access

American legal education has long been the global gold standard, producing leaders in law, politics, and business. Its rigorous curriculum, emphasis on critical thinking, and commitment to developing practical problem-solving skills have established a framework that many systems around the world aspire to emulate.

While meaningful innovations have taken place in legal education over the years, many are best characterized as refinements to the existing model rather than significant reforms. For example, curricular options today are more likely to include a wider variety of subject areas and teaching methods, however, most US legal education is still delivered through an in-person, full-time, three-year post-graduate Juris Doctor (JD) degree. While the overall quality of American legal education is exceptional, it is not filling our nation鈥檚 need for justice work.

The consequences are increasingly difficult to ignore. Low-income Americans receive no or insufficient legal help for 92% of their substantial civil legal problems, according to the Legal Services Corp.鈥檚 report. As a result, in many court systems, self-represented litigants have become the norm rather than the exception, whether the legal challenge involves housing, consumer debt, or family stability.

This is not the fault of legal educators, who often go above and beyond to help bridge the gap through the provision of free legal services and other efforts. Even so, it is the responsibility of legal educators to assist in designing and supporting new models of training and legal delivery to systemically narrow the gap for the long term.

Innovation beyond fine-tuning

Addressing this persistent and growing issue will require more than fine tuning. Instead, to meet the demands of a society increasingly characterized by inequality, social division, and complex interdisciplinary problems requires change that will better prepare our justice system for the future.

To get there, legal educators may have to sacrifice one part of what has long defined them: homogeneity. While a degree from a more elite law school is certainly rewarded in the entry-level employment market, the legal education provided at most of the accredited law schools in the US is more alike than different.

For law schools to help close the justice gap, increasing institutional pluralism is essential. Law schools can and should differentiate themselves by developing tailored solutions to address specific justice challenges within their reach. For example, Medical-Legal Partnership Clinics at and help low-income clients address legal issues that can impact their health outcomes. And students at the University of Arkansas School of Law provide assistance to small businesses, nonprofits, and rural municipalities that often cannot afford legal counsel though the university鈥檚 Community and Rural Enterprise Development Clinic.

To be sure, law schools cannot and should not do this alone. Law school deans have rightly encouraged legal education鈥檚 accreditation process to improve regulatory flexibility and promote responsible change. As a result, many schools are developing high-quality online programs that offer both access and excellence. These programs may expand the pool of lawyers over time, but they remain largely focused on JD education rather than the broader workforce that will be needed to improve the public鈥檚 legal health.

A framework for responsible expansion

To enhance access to justice, the legal profession needs to move beyond 鈥渆ducating lawyers鈥� alone and expand into teaching law more broadly. The traditional JD degree will continue to be vital to our legal system; but just as healthcare relies on physicians, nurses, physician assistants and other licensed professionals, the justice system needs a wider spectrum of trained and regulated providers.

To get there, states must open their doors to a wider range of legal services providers. Unfortunately, many states 鈥� often for political reasons 鈥� continue to resist allowing limited-service legal providers to handle routine but still important legal needs.

Models for this approach already exist. , , and each have developed programs that train and certify non-lawyer legal service providers to help individuals navigate courts, understand their rights, and address common legal issues involving housing, family law, public benefits, and debt.

If state courts and legislators are serious about closing the justice gap, they should begin by opening their regulatory doors to these alternative legal providers, while providing responsible licensing and oversight mechanisms in collaboration with law schools in their state. If those doors are open, law schools can and will step through. Many law schools already have innovative master鈥檚 degree programs that are aimed at law-adjacent fields such as government contracts, human resources, compliance, and more. These non-lawyer educational programs can easily be tailored for alternative legal providers.

Keeping legal education in the hands of American law schools will properly balance access and excellence, ensuring the public continues to be served by qualified practitioners. Law schools have the skilled faculty, ethical underpinnings, and institutional infrastructure that鈥檚 needed to train and oversee the next generation of justice workers.

A robust justice system needs a full spectrum of professionals to meet society鈥檚 legal needs, much as our healthcare system relies on a range of trained providers. Until we build such a structure, the justice gap will remain exactly where it sits today, to the detriment of many citizens.

You can find more about the听challenges facing law schools and legal education here

Key highlights:

• • • How AI-native firms redefine the lawyer’s role 鈥� AI-native firms like Paralex are gravitating toward a “technician” archetype, which puts less emphasis on the trusted-advisor dynamic that has long defined the attorney-client relationship.

    • The profession may be heading toward a two-tier split 鈥� As AI-native firms grow and normalize this operating model for a new generation of attorneys, the legal profession may bifurcate into a smaller cohort of relationship-driven advisors who provide deep, context-rich counsel; and a larger pool of proficient, AI-assisted technicians working at high volume.

    • AI firms can highlight how future lawyers learn 鈥� AI-native law firms are elevating a long-standing mentorship gap that threatens to erode how the next generation of lawyers develop independent judgment; and addressing it will require both creative AI-assisted solutions and more deliberate frameworks for deciding which cognitive tasks should remain done by humans.

The opportunity of starting a native AI law firm to test an idea is intriguing to some lawyers, especially those with an entrepreneurial instinct and determination to see the idea through. When founded , he aspired to democratize legal services for small businesses and leveraged AI to do so. His 29 years of practicing law had shown him the inefficiency and costly downsides of the billable hour; and he hypothesized that if the workflow could be automated with an attorney in the loop and could charge one-tenth of what it normally cost, demand would follow.

The reality has been more complicated and more instructive for the future of legal practice, Candelmo explains, as he offered a candid accounting of what Paralex has learned in practice.

Building for underserved small business owners

Paralex was built around a tiered service model covering everything from verified legal Q&A to AI-assisted contract drafting. AI handles the intake and first drafts at every stage, and the attorney handles the judgment. The small business transactional law vertical was a deliberate bet because the practice area is most amenable to pattern recognition and workflow automation. In addition, small business represents one of the largest pools of underserved legal clients.

Candelmo has learned that affordability alone does not unlock demand. The long-cited statistic that 鈥�60% of small businesses never use a lawyer because of cost鈥� overstates how much of that gap is price-driven. Indeed, a meaningful portion of business owners appear to not want legal counsel at any price. Free AI tools have compounded this learning because ChatGPT, Claude, and Gemini can produce a plausible contract or answer a legal question at zero cost. 鈥淧eople feel that maybe it鈥檚 just good enough,鈥� Candelmo says.

How AI-native firms redefine concept of a lawyer

AI-native firms like Paralex are discovering they need to develop exclusively the 鈥渢echnician archetype鈥� among its lawyers. The attorneys who thrive in Paralex鈥檚 workflow are those most comfortable operating at volume, untroubled by the absence of ongoing client relationships, and motivated by clean execution rather than the slower cultivation of client relationships. Candelmo describes them as comfortable with gig work because they want to be paid for what they produce rather than chasing invoices.

Young attorneys need to master the tools but not outsource their judgment to them. And they should seek out senior attorneys and cultivate human relationships that will make them more than a technician.

Candelmo shares that the trusted-advisor attorney who deeply knows a client鈥檚 business, anticipates problems before they arise, and provides counsel grounded in years of accumulated context is largely absent from the Paralex experience. He describes AI-native firms鈥� role as taking out the unnecessary back-and-forth that occurs in traditional law firms鈥� practices. At the same time, AI-native firms start out narrowly servicing a vertical by providing legal services that are optimizing for efficiency and relatively less complex.

The implication is significant for lawyers and their professional identity. As native AI firms grow and attract a generation of attorneys for whom this model is normal, the profession might be more likely to bifurcate between a smaller cohort of relationship-driven advisors on the one hand, and a larger pool of technically proficient, AI-assisted attorneys working at volume on another.

A generation of lawyers with no one to learn from

What Candelmo says he worries most about is who will teach the next generation of lawyers how to think. In AI-native environments, a junior attorney working at high throughput may review AI-generated output quickly, trust it, and move on. The output looks complete 鈥� but there is no obvious signal that something important was missing and no senior attorney to say why it matters.

Candelmo’s proposed solution is a second layer of AI tools, such as simulation tools, that can function like a senior lawyer. It reviews the initial output, flags gaps, and provides the kind of annotated feedback that would have come from a partner review in a traditional law firm.

His advice to young attorneys is to master the tools, but do not outsource your judgment to them. And they should seek out senior attorneys and cultivate human relationships that will make them more than a technician, Candelmo adds. “Ensure that your humanness, your human relationship skills make you stand apart.”

As native AI firms grow and attract a generation of attorneys for whom this model is normal, the profession might be more likely to bifurcate between a smaller cohort of relationship-driven advisors on the one hand, and a larger pool of technically proficient, AI-assisted attorneys working at volume on another.

In addition, , Partner at Foley and Gardner and adjunct professor at the teaches at the University of Wisconsin Law School, goes one step further and advocates for adding a conscious step before instinctively turning to AI tools. He suggests each lawyer first ask themselves, 鈥淲hat cognitive function is being delegated to GenAI at each step in the workflow?鈥�

In the current state, the AI conversation within the legal ecosystem continues in a good-or-bad binary rather than simply asking when AI use is beneficial and when it is risky, which is increasingly what law students are asking for. For example, the announced a policy that bans students from using AI for class assignments and during exams, although students can still use AI for research to identify sources.

The experiences of Candelmo and Paralex, alongside the broader debate playing out across the legal ecosystem, make it clear that the legal profession is being forced to make deliberate choices about what lawyers are for, which cognitive tasks should remain human, and how professional judgment is developed and passed on.

The law firms and legal institutions that build thoughtful frameworks for when and how AI should be used will create a profession that is both more efficient and more capable of producing the kinds of lawyers that clients and society will continue to need.

You can find more about

• • • The real gap in AI transformation is between tools and strategy 鈥� Many law firms mistake owning AI tools for having an AI strategy, measuring success through usage data alone rather that measuring the created value for clients and the firm.

    • 鈥淐hange agility鈥� is operationally required 鈥� Because AI reinvents itself every few months, firms must embed continuous learning as a standard operating reflex rather than a one-time training event.

    • There are 5 key markers that denote true transformation 鈥� Firms pulling ahead share five consistent traits that compound into a durable competitive advantage.

Organizations with a visible AI strategy are 3.5-times more likely to experience critical AI benefits compared to those without one, and almost twice as likely to be experiencing revenue growth because of their AI investment, according to recent 成人VR视频 research.

For law firms 鈥� organizations in which the human dynamics of transformation are particularly complex 鈥� new details are emerging that separate those firms that are executing AI transformation well from those firms that are not, say two practitioners that work on law firm AI transformation every day 鈥� , Principal Consultant of AI Strategy and Transformation Services at 成人VR视频; and , an organizational and process transformation specialist on the same team. Together these two help firms move from adopting AI tools to rethinking how their legal work gets executed and delivered.

Both have said they鈥檝e observed that the firms pulling ahead are those that have put their lawyers at the center of the firm鈥檚 transformation strategies.

The gap between activity and accomplishment

Many law firm lawyers who are serious about their AI strategies have attended AI webinars, learned the vocabulary, and can readily name the leading tools in their practice area. Going one layer deeper, however, begs a key question on whether those tools have changed how those lawyers work and deliver value to clients.

Snavely says he sees this disconnect often, especially as firms can confuse having AI tools with having an AI strategy. In fact, many firms measure the effectiveness of their AI strategies with usage data, but Snavely and Lein argue that only focusing on usage does not give a full picture. Rather, they say that the key to effective AI transformation is driving measurable value for clients and the firm. 鈥淎wareness and simple use are not a strategy,鈥� Snavely notes. 鈥淭he real question is whether lawyers have actually changed how they work and the benefits that brings to the firms and its clients.鈥�

Since AI is always evolving, law firms need to alter their cultural paradigms to better prioritize a proactive mindset that treats constant technological change as a standard operating environment rather than a temporary disruption 鈥� a concept known as 鈥渃hange agility.鈥�

Lein underscores the challenge by pointing out that the technology-first mindset is getting the order of operations backwards. When firms lead with the tool rather than the lawyer’s problem, they are asking people to change their entire workflow for a solution that does not yet feel worth the investment of time and mental bandwidth to change.

鈥淲hen you lead with the tool, you are asking lawyers to change their process or approach to the work for something that has not yet proven its value,鈥� Lein says. 鈥淪tart with the problem, then the right technology becomes obvious.鈥�

To address this challenge, Snavely and Lein recommend that law firm leaders do the harder work of mapping lawyer problems to AI capabilities and identifying those professionals who can bridge that gap before investing broadly in AI adoption. Simultaneously, they should also focus on opportunities that AI can unlock for clients that were not previously possible.

What ‘change agility’ looks like

Since AI is always evolving, law firms need to alter their cultural paradigms to better prioritize a proactive mindset that treats constant technological change as a standard operating environment rather than a temporary disruption 鈥� a concept known as 鈥�change agility,鈥� Snavely explains.

鈥淐hange agility is not a skill you train once,鈥� he adds. 鈥淚t鈥檚 a strategic reflex you build into the organization 鈥� change agility means continuous learning is baked in, not bolted on.鈥�

Lawyers are being asked to keep up with a technology that reinvents itself every few months; and without careful prioritization, firms will see their professionals burn out from the noise of the technology changing.

At the same time, the pair acknowledge that constant change is exhausting. Lein flags a particular fatigue risk that leaders often underestimate. With prior technology cycles, there was a stabilization window in which people could absorb, adapt, and refine 鈥� however, this does not exist with AI tools.

Lawyers are being asked to keep up with a technology that reinvents itself every few months; and without careful prioritization, firms will see their professionals burn out from the noise of the technology changing.

Emerging indicators that some firms are succeeding

To strike the balance, both experts agree that a key part of the solution to better AI transformation within a law firm is clarity of direction. People can tolerate a great deal of ambiguity if they understand where the firm is heading and what their role is in getting there. Firm leaders who communicate a clear AI strategy 鈥� one that is connected to the firm’s overall direction and not just bolted on 鈥� give their people something on which to orient themselves amid constantly changing dynamics.

Further, Snavely and Lein identify five markers that consistently distinguish those law firms making progress from those generating activity without resulting AI transformation. These five markers include:

1. Fostering an acceptance of failure 鈥� Firms that have normalized rapid experimentation 鈥� trying, adjusting, and moving forward 鈥� without the expectation of getting it right the first time are outpacing those that still operate under the assumption that AI adoption will occur solely through webinars and one-time training events.

2. Developing consistent storytelling as a key tactic in communications 鈥� In the highest performing firms that Snavely has assessed, the same client success stories circulate repeatedly and consistently across interviews with different lawyers. These firms treat these success stories as cultural infrastructure, repeating them until they become part of the firm鈥檚 shared identity.

3. Establishing role clarity 鈥� Lein observes a meaningful difference between firms that formally incorporate AI into job descriptions and those that have left it as an informal add-on. 鈥淓nsuring AI is a clear part of a lawyer鈥檚 role is a meaningful job satisfaction signal and a leading indicator of adoption depth.

4. Aligning performance incentives with AI experimentation 鈥� Most law firms are still in early thinking mode on compensation structure alignment, but those firms with incentive frameworks that reward AI-driven value creation with new service offerings, recovered time that can be redirected to higher-value work, and measurable client outcomes, will more effectively reinforce the behaviors that drive transformation.

5. Defining what 鈥済ood鈥� looks like at the work-product level 鈥� Firms that define explicit standards for quality and build those standards into how AI output is supervised and evaluated will position themselves better over the next few years than those that leave expectations undefined.

Lein frames all these markers as both a cultural and a structural imperative because AI can amplify existing organizational behavior 鈥� both productive and dysfunctional 鈥� within a firm. 鈥淎I is an accelerator of work, but it is also an exacerbator of bad cultural issues,鈥� he explains.

Together, these five signals can complement each other and more importantly, compound into a durable competitive advantage for those law firms that act upon them.

You can find out more about the challenges of AI in the legal industry here

Key insights:

• • • The headline obscures the math 鈥� Kirkland’s roughly $100 million a year is about 1% of its $10.6 billion in annual revenue, a level most research-driven industries would consider a maintenance budget.

    • Build versus buy is the real signal 鈥� Choosing to own a proprietary platform rather than license the same tools that competitors can buy reflects a belief about where advantage now accumulates.

    • R&D is a habit before it is a budget 鈥� The discipline of continuous reinvestment, not the size of the check, is what compounds investment into greater capability.

Welcome back to The AI Law Professor. Last month, I examined the jagged fit problem: Why the same AI tool produces uneven results from one lawyer to the next, and why matching capability to task matters more than the brand on the box. This month, I want to widen the lens from the individual lawyer to the law firm itself and ask what it actually means for a firm to invest in its own future.

On May 28, Kirkland & Ellis claimed it will spend $500 million over the next three to four years building its own AI platform, starting with a roughly $100 million investment this year. The firm will fund the work from its annual revenue, which reached about $10.6 billion last year.

Outside technology companies are helping to build the system, but they will not be permitted to resell it to competitors. Some 250 Kirkland lawyers, including 100 partners, have already contributed detailed accounts of how they work so the platform can be tuned to the firm’s own methods. The ambition is end-to-end. They鈥檙e creating a system that can carry a complex mandate from initial scoping through execution, rather than a scattered collection of point tools for document review, due diligence, and drafting.

A half-billion dollars, in context

Obviously, $500 million dollars is an enormous sum in absolute terms 鈥� but, as a share of Kirkland’s revenue, it is far more modest. The roughly $100 million the firm expects to spend this year sits close to 1% of annual revenue, a level that firm chair Jon Ballis has framed as the firm’s appetite for taking 鈥渂ig swings.鈥�

Place that 1% against how other industries fund their own future. Software and internet companies reinvest an average of about 13% of revenue into research and development. The United States pharmaceutical industry routinely spends north of 20%. Defense contractors land somewhere between 10% and 15%; consumer electronics makers, 8% to 12%. By those benchmarks, a 1% commitment reads less like a moonshot and more like minimal maintenance.

For most of its history, the legal industry has carried no R&D line item at all. This means that there are two things that are true at once: Kirkland is leading the profession, and the profession as a whole still invests a fraction of what the most innovative industries treat as the cost of staying alive.

The deeper signal is ownership

The dollar figure is the headline, but the strategy underneath it matters more. Kirkland concluded that if every firm can license the same AI from the same vendors, that AI stops conferring any advantage at all. So, the firm chose to own its platform rather than rent it, and to bar any outside builders from selling the result to rivals.

This instinct is not new for Kirkland. In 2017, the firm built CTRAN (Corporate Transactions Database), a proprietary database of past M&A transactions that let its lawyers spot patterns in deal terms that its competitors could not see. That data advantage proved difficult to replicate, and it helped carry the firm to the top of the global revenue tables. The firm鈥檚 planned AI platform is the same instinct on a vastly greater scale: Treat institutional knowledge as an asset to be compounded, not a byproduct to be discarded.

That is the signal worth absorbing. The strategic question is no longer only which tool to license; rather, it鈥檚 what you are building that a competitor cannot simply purchase for itself.

Three ways to invest in R&D without a big budget

Certainly, most law firms do not have $500 million, or even $5 million, to commit. Yet, they may not need it. R&D is a discipline before it is a budget, and the discipline can be scaled down.

First, make R&D a standing commitment rather than an occasional impulse. Set aside a fixed share of revenue, even 1% to 3%, and a fixed block of protected, non-billable time each month for experimentation. Name someone to own it and stick to it.

Second, turn what you already own into a proprietary knowledge asset. You do not need to train a model to draw an edge from your own data. Your closed matters, briefs, clause libraries, and playbooks can be organized into a structured, searchable knowledge base, then connected to a retrieval system. This gives you the small-firm version of CTRAN, which can compound with every matter you handle.

Third, run experiments with a clear measure of success. Pick one workflow, define a baseline for time, cost, or error rate, pilot a tool against it for a fixed period, then decide deliberately whether to keep it or kill it. And write down what you learn.

The strategic habit is the asset

The temptation when reading about Kirkland’s half-billion-dollar bet is to conclude that R&D belongs only to firms with billions to spend. Actually, the opposite is closer to the truth. Kirkland’s real advantage is not the size of its check; rather, it鈥檚 the decision to treat building as a permanent part of its long-term strategy and how it operates. That decision is available to a solo practitioner with a free weekend and a stack of old briefs just as surely as it is to the largest law firm in the world.

The half-billion-dollar figure will make the headlines, but it鈥檚 the strategic habit that compounds in value.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and author of the forthcoming (Globe Law and Business), where he shares exactly how you can build your own strategic habits and assets for your own law firm.

Key insights:

• • • Law firms and clients are both redesigning for AI 鈥� Both sides are rethinking how legal work gets done, including thoughts on operating models, talent, technology, and the role of automation in delivering services.

    • There鈥檚 a communication gap despite shared dependence 鈥� Even though each side鈥檚 AI choices directly affect the other, many law firms and legal departments are still planning separately, without enough transparency or coordination.

    • There are 5 critical shared questions they need to address together 鈥� Law firms and their clients need joint conversations about pricing, work allocation, trust, talent development, and wider industry standards to better shape a sustainable future together.

A law firm choosing its 2030 strategic business model without knowing how its clients are evolving is navigating blind 鈥� and vice versa.

And yet, across the legal profession, that is exactly what is happening. Law firms and corporate legal departments are each embarking on significant transformations 鈥� redesigning their operating models, reimagining their talent models, and making decisions about technology. What is striking is how often they are doing so in isolation from each other, retreating into their respective silos at precisely the moment when their futures are most deeply interconnected.

The pace of change raises the stakes. Ninety-one percent of corporate C-Suite leaders say the rise of AI will have a significant impact on their five-year business strategy. Further, AI adoption has nearly doubled across the legal sector over the past 12 months, and half of legal professionals say they expect agentic AI to be central to their workflow within two years.

Clearly, the decisions being made today about talent, technology, pricing, and relationships will lock in outcomes that are hard to reverse.

The AI view from corporate law departments

On the in-house corporate side, General Counsel are contending with broadening mandates, increasing demand and complexity, and a pace of business that shows no signs of slowing. Not surprisingly, AI is increasingly the strategic response: , up from 25% who said that last year. And for most that means AI-enabled capability to do more, faster, and at greater scale.

成人VR视频 Institute鈥檚 GCO 2030 research maps out what the transformed legal department could look like 鈥� from tech-forward functions that scale routine work through automation, to seamlessly integrated teams that blend internal and external expertise, to legal departments that actively supercharge peer functions like HR and Finance.

The common thread through all of this is a shift toward strategic selectivity: Doing more with sharper focus and engaging outside counsel differently as a result.

The AI view from law firms

Among law firm leaders, AI is unavoidable 鈥� in every leadership conversation that 成人VR视频 Institute researchers held with managing partners in recent months, the issue of AI came up. For many, it is seen as a lever for growth, although law firms vary considerably in how far they have moved from consideration to execution.

In fact, our recent research points to four possible models emerging on the horizon that have AI-native disruptors built around agentic automation, elite advisory boutiques in which senior judgment is the product, integrated powerhouses that combine top-tier brand with AI-enabled delivery at scale, and those that hold back from AI adoption (although the research suggests this is a delay, not a strategy). What unites the more progressive scenarios is that strategy requires genuine commitment: A firm simply cannot pursue all models at once, and the choices made about talent, pricing, and client relationships will compound over time.

You can access the full feature article,听The 2030 legal department: 5 ways AI will transform how in-house teams work听here

The problem, of course, is that both sides are designing futures that will inevitably shape the other 鈥� yet two-thirds of GCs say they do not know how their outside firms are approaching AI, and law firms report genuine uncertainty about what their clients want. This shows a clear communication gap at the heart of the legal ecosystem, and it is opening at precisely the moment that demands coordination.

The futures being designed in those silos are not mutually exclusive. When a corporate legal department shifts its model 鈥� whether automating routine work, restructuring how it engages external counsel, or reorienting toward strategic advisory 鈥� it changes the demand profile that law firms face. When a firm repositions itself around premium complexity or agentic delivery, that changes what clients can rely on externally, and therefore what they must build internally. Each side鈥檚 choices narrow or expand the options available to the other.

Addressing 5 critical questions together

Against that backdrop, there are several questions the legal profession cannot answer from within a single organization 鈥� questions that require genuine conversation between firms and the clients they serve.

The first is the question of value and pricing 鈥� In an AI-enabled legal market, how is value defined and paid for, and can the answers be fair to both sides while still encouraging innovation? If AI dramatically accelerates the delivery of advice, does efficiency become the new floor or the new ceiling? Are clients paying for outcomes, risk reduction, speed 鈥� or some combination of all three? And which side absorbs the productivity dividend?

The second question concerns where the work lives 鈥� As both law firms and legal departments expand their AI capabilities, the traditional allocation of work between in-house and external counsel will shift. Determining what genuinely belongs in each place and why 鈥� based on, for example, risk, complexity, relationships, and strategic importance 鈥� is a conversation that requires honesty from both sides.

Third is the question of trust and transparency 鈥� How can firms and their clients build shared frameworks for disclosure, governance, and accountability around AI use in a way that strengthens relationships rather than undermines them? Without these frameworks, AI integration risks eroding the relationship foundations upon which legal advice depends.

Fourth, the talent pipeline question 鈥� As the type of routine work that historically served as the apprenticeship model for past generations of lawyers rapidly disappears, both firms and legal departments face a shared responsibility for how legal talent is trained and developed.

Fifth, and perhaps most structurally significant, is which challenges are ecosystem-wide? 鈥� Data standards, interoperability, shared risk frameworks, and ethics and assurance are not problems any single organization can resolve alone but rather, are ones that require coordinated action across firms, legal departments, technology providers, and academia.

Indeed, none of these questions can be resolved in isolation, and avoiding them does not preserve the status quo, it simply locks in poor defaults. Leadership in this moment doesn鈥檛 mean having all the answers, but it does mean being willing to ask the questions out loud, with the people who need to be in the room.

The firms and legal departments that come to these questions together, rather than arriving at the table with entrenched positions already locked in, will be better positioned to build a future that is resilient, transparent, and sustainable.

To start, pick one of the five questions above and put it on the agenda for your next client or firm meeting. Not as a negotiation, but as an open conversation worth having.

That is how the communication gap between law firms and corporate legal departments gets closed 鈥� one honest conversation at a time.

Start your legal department鈥檚 future planning using our reimagine guide from the Value Alignment Toolkit

Key highlights:

• • • The theory-practice gap is now an AI-era crisis鈥� Integrating legal training with hands-on pro bono experience is the future of legal education.

    • A collaborative model merges learning and doing into a single platform鈥� The model connects law students with vetted pro bono opportunities from legal services organizations, while also offering targeted, skills-based training at the moment students step into those matters.

    • Pro bono work is uniquely suited for responsible AI training鈥� On-demand programs led by expert faculty are available to help students sharpen pro bono skills, understand the use of AI in today鈥檚 legal practice, and stay on top of developments in numerous industry and practice areas.

Legal education has operated on a familiar, decades-long divide that saw students spend their first years learning the law in the classroom and then after graduation, gaining substantive experience practicing the law in the real world. This gap has always been costly for both students and legal employers, and now it鈥檚 emerging as untenable in an era in which AI is rapidly reshaping what junior lawyers do.

Pro bono and skills training close this gap

A new partnership between , a pro bono management platform, and the (PLI), a nonprofit provider of learning resources for legal professionals, is designed to close this gap while showing something larger about where legal education must go.

The partnership is designed to equip students with on-demand, actionable training that supports effective pro bono engagement by offering access to PLI’s training programs directly through Paladin’s platform. Since launching with 30 law schools in August 2025, students have signed up for thousands of pro bono cases through the platform, according to , Co-founder and CEO of Paladin.

For years, experiential learning in law schools was something students had to piece together on their own by hunting across spreadsheets, clinic listings, and externship postings for opportunities, says Sonday, adding that too often students were given little guidance on what they were walking into.

The partnership is designed to equip students with on-demand, actionable training that supports effective pro bono engagement

“What’s fundamentally different is the integration and centralization of learning and doing,” Sonday explains. “Historically, legal education has separated theory, training, and practice.” Now, she notes, a student can learn a concept, build confidence through targeted training, and apply it in a real-world setting within a short amount of time.

, Chief Strategy Officer at PLI, describes the experience from the student’s perspective: 鈥淲hen a first-year logs into the Paladin platform, they are not thrown into the deep end. Instead, they can access skills-based programs, such as a PLI program specifically on how to interview a pro bono client before they ever sit across from someone in need. This leads to a better experience for the student, the law school, and especially for the client.”

Pro bono work suited to responsible AI training

The urgency behind this partnership is inseparable from the impact AI is having on the entry-level legal market.

“We’re already seeing AI reduce the time spent on tasks like initial legal research, document review, drafting memos, and summarizing case law,鈥� Sonday says. 鈥淭his is work that has traditionally formed the foundation of junior associate training.鈥� The skills AI cannot replicate 鈥� such as judgment, issue spotting in ambiguous situations, client communication, and ethical decision-making 鈥� are what students need to develop deliberately earlier in their legal careers.

Indeed, those human skills are essential to the effective use of AI, Talmage says. The lawyer of the future will be a strategic advisor and creative problem solver, which are the very attorney roles that AI cannot fill, she explains, adding that those must be cultivated through experience. “You always need to be questioning and verifying and authenticating 鈥� and that’s generally a lawyer鈥檚 role.鈥�

For years, experiential learning in law schools was something students had to piece together on their own by hunting across spreadsheets, clinic listings, and externship postings for opportunities.

There is a particular logic as to why pro bono work is the right fit for learning to use AI responsibly. Pro bono is “a built-in, humans-in-the-loop model” in which students are always supervised by attorneys, Sonday says. And this supervision creates a structured environment in which to learn how to use AI tools, apply them to real matters, get feedback, and iterate. The result, Sonday argues, will be more attorneys who are AI-fluent early on and throughout their careers.

A message to law school leaders

For law school leaders, both Sonday and Talmage highlight that AI use has already changed the legal profession. The choice then for law schools is whether they evolve by design or by default.

Students know the legal profession has changed and so do employers, CLE providers, and clients, Talmage explains.

Sonday agrees. “The pace of change in the legal profession is accelerating, and students need to be prepared not just for the law today, but also for the practice of law in the future,鈥� she says. 鈥淚ntegrating pro bono platforms and AI-specific training aligns legal education with reality.”

The Paladin/PLI partnership offers a blueprint for what legal education must become in the future, transforming itself into a space that鈥檚 grounded in applied legal knowledge, human-supervised, and AI-informed. Indeed, the best way to train the next generation of lawyers is to give them real clients, real cases, and real responsibility while they still have room to grow.

You can find more about the challenges facing law schools and legal education here

Key insights:

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

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

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

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

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

A new model for learning

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

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

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

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

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

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

Leadership in an AI age

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

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

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

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

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

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

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

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

The new rules of professional growth

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

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

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

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

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

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

Key insights:

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

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

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

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

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

Significant discussion about legal judgment

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

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

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

Development of business judgment is lacking

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

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

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

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

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

Why both views need to be addressed

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

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

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

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

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