Key findings:

• • • Workloads grow, while staffing stays flat — Many government legal department professionals say their work keeps increasing while staffing remains stagnant; and many are turning to AI tools to improve capacity.

    • AI adoption is surging — Over the past year, AI adoption among government legal departments has spread rapidly, with federal and state agencies leading the way.

    • Unfortunately, AI oversight hasn’t surged — Many legal departments report that their AI governance is lagging behind adoption, with 20% of agencies having no AI use policy in place at all.

Government legal departments are facing an all-too-familiar problem: more work, more complexity, and the same number of staff to do the job, according to the Thomson Reuters Institute’s 2026 Government Legal Department Report, which captures the insights from 200 government legal department professionals at varying levels.

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2026 Government Legal Department Report

Threaded through these insights, some clear trends emerged. For example, technology — especially AI and other advanced tools — is increasingly serving as an extension of staff, expanding agencies’ capacity to manage rising workflow demands.

Increasing pressures across all levels

More than one-third of respondents report that their workload increased by more than 10% in the past year, with many handling between 21 and 50 legal matters per week. At the same time, workloads are becoming more complex, with more than one-third of respondents saying that more than half of the legal issues they face are complex, which is particularly notable at the state and federal levels.

Staffing shortages, a top concern in recent years, continue to persist. Three-quarters of respondents say their agencies experienced staffing shortages over the past two years, and almost two-thirds say they anticipate shortages into 2027.

Indeed, despite an increase in complexity and workload, attorney staffing levels have stayed the same for almost 40% of agencies, the report shows. And at the federal and state level, departments were more likely to have experienced a reduction of more than 10% of their staff.

AI adoption skyrockets, making governance more necessary than ever

More than one-quarter of respondents say their agency or department is now using AI tools, up from a meager 5% last year, with this increase taking hold at the federal and state level much more quickly. Among the different groups of respondents, one-third of federal and state government legal professionals report using AI tools compared to just 19% of those at county and city departments. Resistance to AI is diminishing, too; however, more than one-third of county and city legal departments still report having no plans to use AI.

Optimism toward AI is rising alongside implementation, the report shows. More individuals at the federal and state level feel optimistic than pessimistic about AI technology, which is an inversion of last year’s sentiment. Among county and city legal professionals, pessimism still remains more common. Among all respondents, confidential data exposure remains the top evaluation criterion when assessing these advanced tools.

The report underscores that this all points to a need for the establishment of strong governance models before adoption. Nearly two-thirds of government agencies and departments have an AI use policy in place or are developing one, respondents say. However, 1-in-5 departments and agencies are still without an AI use policy, risking unofficial use of prohibited AI tools.

Those agencies hesitant to implement AI technology are encouraged to view AI technology as a way to increase staff capacity amid flat staffing, rising workloads, and growing matter complexity. AI tools can help reduce strain on employees, contributing to better-managed workloads while reducing employee burnout. When appropriately vetted, however, AI technologies can reduce administrative burdens, increase legal research efficiency, and help those organizations facing trying to manage more work with the same staffing levels.

An actionable path forward

As the report makes clear, AI is no longer a future challenge; rather, it’s a present reality in a rising percentage of government legal departments. Indeed, the report outlines ways departments and agencies can move forward in this space, by beginning with lower-risk foundational tools like legal research and case management systems; and then investing time in developing thoughtful AI use policies and evaluation protocols. With responsible staff training and a thoughtful evaluation process, AI technologies can protect the valuable time and work-life balance of government legal professionals.

Increasing workloads are not optional for government legal departments, but how department leaders empower their staff to manage these workloads is becoming the differentiator.

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a full copy of the Thomson Reuters Institute’s “2026 Government Legal Department Report” by filling out the form below:

Key highlights:

• • • AI can help remove interpersonal friction during feedback conversations — Attorneys have taken naturally to soliciting substantive critique from AI that covers tone, argument strength, and clarity in ways they might hesitate to request from a colleague or supervisor.

    • The role of feedback-giver remains irreplaceable — The developmental core of feedback still requires someone who knows both the work and the person receiving the feedback, even if AI is used as a sounding board.

    • Feedback culture can be accelerated using human-AI mentorship — AI makes the conditions for receiving feedback easier and can spark shared human conversations about the work rather than replacing those conversations.

It’s one of those things upon which most people on any legal team would agree — feedback matters. It’s essential to professional development, to mentorship, and to the long-term growth of attorneys at every level. And yet, for all the emphasis placed on its importance, feedback remains one of the more nuanced and inconsistently experienced dimensions of working in the legal profession.

At my firm, Seyfarth Shaw, in particular, there has long been a deliberate focus on building high-performing teams and developing legal talent in a way that is both structured and intentional. We have designed systems that measure and reinforce feedback because we believe it is central to how attorneys grow and how high-performing teams consistently deliver exceptional client service.

Within the legal profession, however, there are dynamics and circumstances that can often make feedback more complex to navigate in practice.

At its core, a lot of feedback still relies on the comfort level of both supervising and junior attorneys to proactively provide it and seek it out, and that is often easier said than done. Time pressures, the structure of legal teams, and the challenge of balancing candor with constructiveness all contribute to making this an often-difficult task.

One of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them — not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact.

Attorneys are trained, as an actual skill set, to question everything and that can naturally extend to how feedback is processed, depending on how it is delivered and by whom. The amount of effort, intelligence, and judgment that goes into legal work is significant, so when that work is challenged, it can feel personal. And on the receiving end, actively seeking out substantive feedback is not a muscle that gets consistently developed in most educational settings leading up to working within a law firm.

The behavioral shift

This is the backdrop against which something genuinely interesting started happening when generative AI entered the picture. Almost immediately, one of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them — not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact. Traditionally, this is the kind of feedback many of these same professionals might hesitate to request directly from a colleague or supervisor.

What struck me was how quickly and naturally asking this of AI became a default behavior. There was an almost instinctive willingness to let AI review and analyze work product in a way that felt qualitatively different from how feedback had traditionally been experienced in interpersonal settings.

Landmark, meta-analysis research on found that feedback interventions actually decreased performance roughly one-third of the time, particularly when that feedback shifts attention to the self and triggers anxiety rather than a renewed focus on the work. More identify three triggers that cause people to reject feedback, including the relationship trigger, in which the reaction is not to the substance but rather to the person delivering it.

And ’s Dr. Larry Richard’s adds a profession-specific dimension: Lawyers tend to score lower on resilience, defined as how one reacts to criticism or rejection, while also ranking high in skepticism, an instinct to question assertions rather than accept them at face value. The combination can make feedback both more essential and, at times, more complex to deliver effectively.

Using AI neutralizes many of these dynamics — there is far less perceived pressure, no interpersonal dynamic to navigate, and no relationship to manage in the moment. We are constantly giving AI feedback about what it did right, what it did wrong, and how to improve. Indeed, we often are already flexing that muscle without thinking twice about it.

What the paradox reveals

This is the part to which I keep coming back. The problem was never that lawyers cannot handle feedback; if that were true, they would not be seeking it so readily from AI. The opportunity lies more in the conditions under which feedback is delivered and received. AI did not make people more open to feedback, rather it removed some of the perceived barriers that can accompany feedback in traditional settings.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better.

However, removing that friction is not the same as providing what lawyers actually need to grow. AI can tell you that your argument has a structural gap, but it cannot tell you why that gap matters in the context of a particular client relationship, a judge’s known preferences, or the broader strategy of a case. It cannot replace the judgment that comes from a supervising attorney explaining not just what to change, but why it matters to change it, and how to think about it differently next time. The developmental core of feedback still requires a person who knows the work, knows you, and is invested in your growth.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better. It has identified behaviors — such as seeking input early, iterating quickly, engaging with critique — that firms like ours have long been working to encourage, and made such behaviors easier to access in the flow of work.

Yet, there is also something more subtle happening. With AI, attorneys retain a clear sense of autonomy over the feedback itself. They can take it or leave it, focus on it, or set it aside without any interpersonal consequence.

AI creates a different dynamic: It functions more as a sounding board, essentially another set of eyes on the work. That makes it easier to engage with feedback in a more exploratory way by incorporating what resonates, questioning what is unclear, and testing ideas before bringing them back into a human conversation. In that sense, AI can help build the muscle not just of receiving feedback, but of engaging with it more thoughtfully — including developing the confidence to ask the “why” that is often where the real learning happens.

At Seyfarth, this is where we see a meaningful opportunity to build on an already strong foundation. Our focus has long been on creating a culture in which feedback is expected, measured, and part of how work improves. What AI allows us to do is take that a step further, making feedback more continuous, more immediate, and easier to engage with as part of the workflow.

In the next part of this series, we will look at what we call SEYmultaneous Advancement, our way of intentionally bringing AI into the feedback process — not as a replacement for human interaction but as a progression — and in true AI form, iterating with the human-in-the-loop along the way.

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Key insights:

• • • Successful AI implementation requires a solid foundation — Strategy, leadership, and the impact on operations and individual users are key elements to any successful implementation.

    • AI success is a skills strategy, not a technology strategy — AI creates a whole new set of skills that are required for both legal department attorneys and department leadership.

    • AI changes how legal work is conducted — If implemented correctly, AI not only improves the end work product, but it also changes how lawyers perform their jobs.

Corporate legal departments are already experiencing the benefits of AI, including improved productivity, and reduced costs and errors, the Thomson Reuters Institute’s recent shows. So it’s not surprising that AI is increasingly becoming a strategic priority for general counsel (GCs).

The report cautioned, however, that success with AI is not a given. AI is not a silver bullet which guarantees improvements across the department. Instead, AI adoption and implementation must be carefully planned in order to realize those benefits.

Crucially, successful AI implementation is not simply about the technology; rather, it’s a reflection of the department itself and often can signal whether the department has the right elements in place to enable that success.

AI enhances successful legal departments — it does not create them

AI implementation is like any other law department strategy — it does not live on its own but instead advances as a direct result of everything that has come before it, including the work of the department’s attorneys and professionals, its daily operations and processes, and the GCs who are guiding the overall vision.

Overall, it’s about having a solid foundation upon which to build AI adoption and implementation.

The Pyramid of AI Success

AI may be one of the most impactful and transformational technologies to come on the scene in recent years, but it’s important to remember that it is still simply one tool among many. And its ultimate success will be determined not only by its capabilities, but by how it integrates with and augments the work that corporate legal department attorneys perform daily.

The technology itself does not perform the work — it enables more efficient work. This means that the rise of AI creates a whole new set of necessary skills for both legal department attorneys and department leadership.

With that in mind, GCs should focus on a few key areas to improve their department’s chances of AI success. The essential steps can be viewed as a pyramid — every step that you take builds, each upon another, creating a solid foundation. Establishing a top-level AI strategy means setting the tone from leadership, which then permeates down through operations and ultimately transforms how individual users work every day.

• • Learning — Most departments have a basic AI understanding and a culture to encourage change, but they often do not have the depth of understanding to move from AI literacy to AI fluency. Be sure to determine where your team is on this learning curve.
  • Empowerment — Empowering your professionals is crucial to drive experimentation and identify new use cases. Ask yourself, does my team feel encouraged to explore new ways of working and empowered to make changes?
  • Ownership — The legal team should feel they have significant input into how AI will be used in the department and throughout the organization. AI can be a major transition, and team members should feel that they can freely share ideas, concerns, and insights.
  • Accountability — Team members with personal goals that are linked to AI adoption are more likely to become top learners and regular users, our research shows, and this leads to greater overall benefits for the department.
  • Usage — Regular use drives adoption, so you should build AI into your team’s daily habits, monitor how many legal team members have tried AI, and how many are using it regularly.
  • Expectations — Balance encouraging uptake with clear expectations around adoption. Offering open encouragement along with access to tools and training to build momentum can be key first steps. As team members become more proficient, set formal expectations around AI usage. Be clear that when targets are set, usage will be tracked and individuals will be held accountable. Then, follow up with low- or no-usage individuals to determine causes, such as difficulty with training.

For GCs, today’s top challenge is how the department can develop needed AI skills in a way that will best augment how lawyers work. If implemented correctly, AI will not only improve the end work product, but it will also better enable lawyers to perform the work they do best.

AI success with outside counsel

The same principles of strategy and leadership that contribute to AI success within the department also extend to working with outside counsel. Currently, more than half of corporate counsel say they believe their outside law firms should be using AI, according to the report; however, two-thirds also say they do not know how their outside firms are approaching their use of AI.

This creates a communication gap, in which some GCs attribute to hesitance or caution. “We do not ask and they are shy to provide answers because they are already under a lot of pressure because their rates are so high,” reports one GC.

About three-quarters of corporate counsel also say they expect their outside law firms to take the lead in AI conversations between the department and the firm. However, that does not mean that GCs should simply accept a lack of conversation if firms are not forthcoming. Those GCs that want their outside firms to embrace AI should be open and transparent, conveying that they believe AI can assist firms with most work tasks, while placing a strong emphasis on output verification and the authority of attorney expertise. Indeed, GCs need to understand how their outside firms are using AI, especially how and when it is being applied, how it’s being supervised, and, perhaps most importantly, how it impacts fees.

Without detailed and regular discussions, GCs could develop a blind spot in this area. “Conversation has been only high level,” another GC explains. “We generally know what AI they are using but not how they are using it.” What’s surprising, the GC adds, is that “the billing has remained the same as it did before — so either they are not using AI tools efficiently, or they are just doing double work.”

You can download a fully copy of the , from the Thomson Reuters Institute here

Key highlights:

• • The AI strategy-execution gap is an organizational problem, not a tech one — Among professionals whose firm or department has a stated AI strategy, more than half say that either the strategy is not visible on a daily basis or that the organization has no strategic AI direction at all.

  • The human cost of inaction is building faster than most leaders recognize — More than 90% of professionals say they are experiencing some degree of this AI-value disconnect, and among them, one quarter is considering leaving their current organization within two years if things don’t change — at an estimated replacement cost of $232,000 per employee.

  • Disrupted development compounds talent risk for the next generation — The flight risk of experienced professionals creates a compounding multiplier effect on the ability of entry-level talent to develop critical skills.

The greatest barrier to AI transformation in professional services is the widening human gap between what organizations promise their people about AI and the spoken and unspoken messages that professionals see and observe in their workplace every day, according to the ¶¶ŇőłÉÄę recent , which surveyed more than 1,800 professionals in 62 countries across areas of law, tax, audit, accounting, compliance, risk, and global trade.

Establishing a visible AI strategy

While our research showed that most organizations have a stated AI strategy, the data suggests that it is not translating evenly to employees in their day-to-day work. Among professionals whose firm or department has a formal AI strategy, 35% say that strategy is not visible in their day-to-day experience, and another 17% say their organization has no strategic direction on AI at all. That means that more than half of professionals with fiduciary commitments are working in an environment in which the AI strategy on paper does not match the reality of how their work gets done.

The reasons that professionals say AI strategies are stalling suggest an absence in AI transformation and change agility across the organization. Indeed, professionals say that drivers of their organizations’ struggles to translate AI ambition into measurable results include the fact that the right tools are not yet in place (with 47% of respondents saying this), people are not equipped or trained to work in the intended way (43%), the strategy has not been translated into clear operational priorities (32%), and there is no shared understanding of the AI strategy across the organization (30%).

When strategic clarity around AI exists, it translates into more visible value. In fact, more than two-thirds of professionals in firms and departments with a stated strategy say AI is meeting or exceeding expectations for creating value at work. When there is no understandable AI strategy, less than one-quarter say this.

The quiet accumulation of human costs

The talent consequences of the gap between stated AI strategy and its daily execution are building faster than most leaders recognize. More than 90% of professionals say they are experiencing this gap to some degree. Among them, 1-in-4 is considering leaving their current organization within the next two years if things don’t change. This can result in an estimated replacement cost of $232,000 per employee, which means the quantifiable financial outlay of this talent flight can add up quickly.

The greatest vulnerability of flight risk sits with mid-career professionals, who often are the most embedded AI users, the most influential in day-to-day operations, and the most impatient with slow adoption. Almost 30% of these professionals would change jobs within two years if AI fails to deliver the value they expect, and 14% say they are considering leaving within the next 12 months.

Of course, the financial risks extend beyond mid-career professionals and could in fact disrupt a generation of early career talent. When experienced professionals leave, they take with them the mentorship and oversight upon which the development of early-career employees depends.

The report shows that 71% of professionals say they believe early-career roles need structured support from experienced peers to develop the skills that are at risk of being displaced by AI. Moreover, nearly half say they are concerned about AI’s impact on the development of independent judgment and learning through experience. In fact, legal professionals specifically say they expect the timeline for early-career lawyers to develop a level of trusted judgment could be stretched by nearly two years.

Taken together, these factors create a compounding multiplier effect that will almost certainly have a negative impact on organizational performance in the near future, the report suggests.

Recommended actions for employers and professionals

The Future of Professionals Report 2026 details three distinct paths for how organizations can deploy AI:

1. 1. 1. Elevate, which allows AI to handle rote tasks while keeping human expertise at the center;
      2. Scale, which uses AI primarily to increase capacity without increasing headcount; and
      3. Reimagine, which puts AI at the core as it rebuilds operating models and service propositions from the ground up.

The data also makes clear that the organizational and human costs of inaction are multifaceted. There are several concrete steps that both employers and professionals can take now, as outlined in the report, which include:

For employers:

• • • Choose a path and make it visible — The aforementioned three paths represent genuinely different futures with different commercial models, talent strategies, and definitions of professional value. Organizations must choose one deliberately and make it easily visible at the individual and leadership levels.
    • Close the rift in alignment before it results in talent departure —ĚýMore than one-third of professionals say they are working somewhere where the AI approach does not match their preference. These professionals are almost twice as likely to consider leaving within the next 12 months — and organizational leaders need to be aware of that.
    • Let strategy drive investmentsĚý— Professionals working in organizations with a stated AI strategy are 3-times more likely to say AI is meeting or exceeding expectations for creating value at work compared to those at organizations without a stated AI strategy. This makes the value of establishing a stated AI strategy and ensuring its visible on a daily basis, a clear step for organizational leadership.

For professionals:

• • • Know which future you are working towardĚý— Almost all professionals say they can see a future in one of the three paths. Understanding which one fits you is the first step to having a productive conversation about where the inconsistency is between your preference and your organization’s direction.
    • Invest in judgment as well as AI tool fluencyĚý— Judgment will always be a human differentiator in regard to AI, and the judgment that professionals apply on top of AI competency is what builds lasting professional value.

Professionals and organizational leaders need to make decisions in regard to their relationship with AI — and these decisions will determine the future of professional services. Those employers and professionals that choose their path deliberately, work to ensure alignment between strategy and experience, and invest in human judgment as seriously as they invest in technology will be the ones that can turn AI’s promise into a competitive advantage that compounds over time.

You can explore the fullĚý

Key highlights:

• • • How AI-native firms redefine the lawyer’s role — 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 — 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 — 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 “60% of small businesses never use a lawyer because of cost” 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. “People feel that maybe it’s just good enough,” Candelmo says.

How AI-native firms redefine concept of a lawyer

AI-native firms like Paralex are discovering they need to develop exclusively the “technician archetype” among its lawyers. The attorneys who thrive in Paralex’s 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’s 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’ role as taking out the unnecessary back-and-forth that occurs in traditional law firms’ 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 — 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, “What cognitive function is being delegated to GenAI at each step in the workflow?”

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.

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Key takeaways:

• • • The dulling effect is real, but the root cause is older than AI — Wherever gatekeeping institutions reward a narrow formula, their output converges long before any chatbot enters the picture. AI then accelerates optimization toward your already selected criteria.

    • Using AI for efficiency alone leaves the creative upside on the table — While most organizations deploy AI for simple drafting tasks, the bigger payoff comes from using it as a discussion engine — a sort of sparring partner that pressure-tests ideas and pushes thinking past the first plausible answer.

    • The highest-leverage intervention is reforming what you reward — The fix for this is upstream of the technology, and it comes from giving people time to make sure unconventional ideas actually survive your organization’s sorting mechanisms.

A tension sits at the center of nearly every serious conversation about AI and organizational strategy, and most leaders can feel it even if they haven’t named it yet.

On one side is the promise that AI can make teams more creative. That it can accelerate brainstorming, provide deeper research, identify hidden connections, and pressure-test ideas before they reach a client or a boardroom. When used well, AI is not a replacement for thinking but an amplifier of it.

On the other side, of course, is the fear that regular AI use is quietly dulling the creativity it’s supposed to enhance. That real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground — and that the less people work their creative muscles, the more they atrophy without them realizing it. This trade-off, swapping originality for efficiency, is a losing exchange.

Both of these intuitions are reasonable and both are, to varying degrees, correct. However, they aren’t equally weighted. The purely cautious camp is taking the bigger gamble, because any competitor that cracks the problem by figuring out how to capture AI’s creative upside while managing the dulling effect gets both the innovation edge and the efficiency gains. The cautious organization doesn’t just miss the upside, it falls behind on both fronts.

The catch is that cracking the problem requires correctly diagnosing what’s actually killing your creativity — and a prominent recent essay on this exact topic gets it instructively wrong.

A good question, poorly tested

Rebecca Winthrop, a senior fellow at the Brookings Institution and director of its Center for Universal Education, recently published in The New York Times arguing that AI is constricting creative thinking. Her central claim is that while chatbots produce polished language, they’re masking a narrowing range of underlying ideas — and this is especially dangerous for students, whose creative development is still taking shape.

The piece is worth reading, and not just as a foil. Winthrop draws on from Georgetown neuroscientist Adam Green, whose team has been tracking the range of ideas in college application essays before and after ChatGPT’s release. Green’s findings related to the before/after tracking study (which have not yet been peer-reviewed) are striking, finding that while post-ChatGPT essays used more diverse and colorful vocabulary, the ideas beneath that language converged. Human judges rated the AI-era essays as more creative, even though the substance had narrowed. In a separate study by Green’s team, cited by Winthrop, human-written essays contributed up to eight-times more novel ideas than AI-generated ones.

The fear is that regular AI use is quietly dulling the creativity it’s supposed to enhance, and the real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground — and the less people work their creative muscles.

And Winthrop flags serious concerns that deserve far more attention than they typically get. For example, AI’s homogenizing pressure falls hardest on those students who sit farthest from the mainstream, including neurodivergent students and those from racial and linguistic minorities. That finding alone should be shaping education policy conversations and acting as a warning for innovation-conscious reformers.

Here’s where Winthrop’s piece stumbles, however, and where it becomes a cautionary tale for organizations that may be thinking about their own AI and innovation strategies. The evidence Winthrop chooses to build her case on — the college admissions essay — is possibly the worst genre in American education for measuring whether AI is killing creativity. Because the creativity in college admissions essays was already dead.

I should know. I’m one of its murderers.

The most templated genre in America

The college admissions personal statement has been reverse-engineered for decades. Well before any large language model existed, applicants had cracked the code: Be damaged, but not too damaged; be resilient but make it look like you did it yourself; and be whole now, because the institution wants guaranteed successes, not risky projects. And all of this must be delivered in a tone that makes the committee feel good about their institution’s role in a meritocratic society. Deviate from this formula and you’re taking a risk, but hit every beat and you’re in the pile that moves forward.

I know this because I lived it recently enough to still remember the specific frustration of trying to fit my own experiences into that template at the age of 17, twisting and contorting experiences I’d actually lived through into the shape I knew admissions readers were looking for while sanding away the human beneath when it didn’t fit. The authentic version of my story wasn’t what they wanted, the version that hit the beats was.

And there’s a further detail conspicuously absent from Winthrop’s essay: The college admissions consulting industry. It’s enormous, it’s been around for decades, and its entire business model is teaching applicants to write to the template. Some of these consultants charge $5,000 or more, and their product isn’t creativity, it’s optimization. They teach students to identify what the admissions committee rewards and deliver exactly that, with the rough edges smoothed away and the personal experiences torqued into the right emotional shape.

My family took this seriously enough to invest in help, and I was fortunate enough they had the means to do so. I had one of those consultants. Mine cost $2,000, and my parents had to sell my mom’s pinball machine to pay for it. I think sometimes about what it says that the path to higher education ran through a professional who taught me, essentially, to write to a formula rather than to present myself in a way that would have given the committee a more honest, unique portrayal of just who they were letting into their institution. The consultant didn’t make me less creative; the system that made the consultant necessary did.

And this is the blind spot in Winthrop’s argument. She treats pre-ChatGPT essays as the baseline for authentic creative expression, but that baseline was already shaped by an industry dedicated to template optimization. So when Green’s research finds that post-ChatGPT essays use richer vocabulary but converge on familiar ideas, the question worth asking isn’t just whether AI caused a measurable shift (Green’s controlled experiments suggest it did) but whether the underlying ideas were already converged at a more fundamental level that the metrics don’t capture. In essence, all AI may have done is make that convergence more visible while democratizing the surface polish.

There’s an entirely different version of Winthrop’s essay waiting to be written — one in which the same data tells a democratization story rather than an erosion story. Where a free chatbot gives a first-generation college student the same surface-level advantage that a $5,000 consultant gave wealthier applicants for years. That’s not a comfortable reframe for institutions already invested in the idea that their selection processes brings forth authentic individuality — but it’s the reframe the data actually supports.

The template always comes first

This isn’t unique to college admissions. Wherever institution rewards a narrow formula, it gets gamed — and the gaming predates whatever technology that has made it easier.

The video essayist Sarah Z traced exactly this pattern in , which makes the gap in Winthrop’s argument clearer. When the publishing industry rewarded a specific shape of trauma narrative in the 1980s and ’90s — suffering resolved through individual resilience — the template grew so predictable that fabricators outcompeted honest writers. Laurel Rose Willson sold a satanic-ritual-abuse memoir and, years later, a Holocaust-survival story, citing the same self-inflicted wounds as evidence for both. Publishing houses weren’t fooled just because they were careless, they were fooled because they’d built a machine that searched for formula — and the system that rewards a narrow pattern is the same one that makes it exploitable.

AI doesn’t create that convergence, it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE — the bottleneck was there long before the tool arrived.

If your organization has ever received a stack of pitch decks, strategy memos, or RFP responses that all hit the same beats in the same order, congratulations! You’ve built your own admissions committee, but don’t blame AI.

AI doesn’t create that convergence; it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE. The bottleneck was there long before the tool arrived.

Threading the needle

Of course, none of this means the concern about AI and creativity is unfounded. The dulling effect is real, and anyone who uses these tools regularly has probably felt its subtle gravitational pull toward the center. Or in the way a chatbot’s first suggestion can quietly foreclose any other directions you might have explored on your own, or how it may produce something that sounds polished but carries none of your voice

A different research team — Anil Doshi and Oliver Hauser, behind the , Winthrop herself points to — put a name to the mechanism, anchoring. Handed an AI-generated idea, writers locked onto it, narrowing the range of what they produced before they’d really begun.

However, the solution on an organizational level isn’t to restrict the tool; rather it’s to address the institutional and behavioral factors that determine whether the tool narrows thinking or expands it.

In this determination, three things matter most:

First, use AI as a discussion engine, not just an automation tool — There’s a meaningful difference between asking a chatbot to draft something for you and using it to create something with you. This article is a case in point. I didn’t read Winthrop’s essay and immediately decide to write a response. Instead, I spent almost half an hour talking to Claude about the article, debating the argument, testing my objections, diving into Green’s research more deeply, and connecting the piece to ideas I’d been thinking about from completely different contexts, such as the Sarah Z essay. This article emerged from that conversation unintentionally, and it would not have existed without it.

Further, the ideas emerged pressure-tested and sharpened through a process that felt more like sparring than delegation — and that’s exactly the kind of process organizations should be targeting. Most organizations deploying AI are using it for efficiency — drafting, summarizing, formatting — and that’s fine. However, if that’s all you’re doing, you’re leaving the creative upside untouched, and your people are feeling the dulling effect without the compensating benefit. It takes an intentional push from leadership to get teams using AI as a thinking partner rather than a shortcut.

Second, give people time — This sounds obvious, but it matters specifically because of how AI interacts with time pressure. When people are rushing, they take the first adequate output and move on. With traditional workflows, shortcuts save time at the cost of quality or risk. With AI-assisted workflows, however, shortcuts save time at the cost of originality, because the first output from a chatbot is almost always the most conventional one. It’s the statistically average response, and reaching the edges takes iteration, pushback, and follow-up prompts that challenge the initial direction. That takes time, and if your people don’t have it, they’ll use AI the way a stressed applicant uses a college essay consultant, producing the safest possible version of whatever the system rewards rather than the innovative one which could change the game.

Third, reform what you reward — This is the intervention that actually addresses the root cause, and it’s the one most organizations will resist because it requires examining their own sorting mechanisms. If your evaluation criteria, your promotion structures, your review processes, and your RFP scoring rubrics all select for the safe and conventional, then AI will only turbocharge that selection.

You’ll get the template faster and more polished than ever, much like the admissions committee that rewards a narrow emotional arc and gets 300,000 identical essays. Or, if your firm rewards the pitch deck that hits every expected beat and takes no risks, AI will produce that pitch deck beautifully — and you’ll wonder why innovation has stalled.

Again, the intervention is upstream of the tool. What does your organization actually do when someone brings in an unconventional idea? What happens to the proposal that doesn’t fit the template? If the answer is that it gets smoothed out in review or tossed altogether, that’s not an AI problem.

The old traps didn’t disappear

Winthrop is right that creative thinking is something to protect and nurture. She’s also right that AI introduces new pressures that deserve serious attention. And she’s right that the stakes are highest for the people whose perspectives are already farthest from the mainstream.

But the college admissions essay wasn’t homogenized by ChatGPT, it was homogenized by decades of institutional selection pressure that rewarded a single template and penalized everything that didn’t fit. AI didn’t create that problem, it just made the template accessible to everyone, including the families that couldn’t previously afford $2,000 and a pinball machine to get their kid across the threshold.

Similarly, your organization’s creative output won’t be determined simply by which AI tools you adopt. It will be determined by what your leadership rewards, what your processes select for, and whether your people have the time and incentive to push past the first plausible AI-supplied answer.

The technology is new, but the traps are very old. And if you want to use AI to make your organization more innovative, the place to start isn’t the tool — it’s the template.

You can find moreĚýĚýfrom the Thomson Reuters Institute here

• • • The real gap in AI transformation is between tools and strategy — 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.

    • “Change agility” is operationally required — 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 — 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 ¶¶ŇőłÉÄę research.

For law firms — organizations in which the human dynamics of transformation are particularly complex — 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 — , Principal Consultant of AI Strategy and Transformation Services at ¶¶ŇőłÉÄę; 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’ve observed that the firms pulling ahead are those that have put their lawyers at the center of the firm’s 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. “Awareness and simple use are not a strategy,” Snavely notes. “The real question is whether lawyers have actually changed how they work and the benefits that brings to the firms and its clients.”

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 — a concept known as “change agility.”

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.

“When 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,” Lein says. “Start with the problem, then the right technology becomes obvious.”

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 — a concept known as “change agility,” Snavely explains.

“Change agility is not a skill you train once,” he adds. “It’s a strategic reflex you build into the organization — change agility means continuous learning is baked in, not bolted on.”

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 — 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 — one that is connected to the firm’s overall direction and not just bolted on — 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 — Firms that have normalized rapid experimentation — trying, adjusting, and moving forward — 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 — 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’s shared identity.

3. Establishing role clarity — 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. “Ensuring AI is a clear part of a lawyer’s role is a meaningful job satisfaction signal and a leading indicator of adoption depth.

4. Aligning performance incentives with AI experimentation — 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 “good” looks like at the work-product level — 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 — both productive and dysfunctional — within a firm. “AI is an accelerator of work, but it is also an exacerbator of bad cultural issues,” 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:

• • • Knowledge flows both ways — The AI era has introduced a new dynamic in courthouses with knowledge flowing in more than one direction.

    • Both sides bring irreplaceable expertise— Clerks bring AI fluency; and judges bring the legal instinct to know when something is wrong. Neither works without the other.

    • Informal learning must become institutional practice— Courts that formalize this exchange, rather than leaving it to chance, will be better positioned for the wave of AI change ahead.

Picture a seasoned federal judge, with decades of experience on the bench and thousands of cases behind them, leaning in to watch a first-year law clerk navigate an AI research tool. The clerk is the one doing the teaching.

This scene, quietly playing out in courthouses across the country, may be one of the most under-reported shifts in how the judiciary is adapting to a rapidly changing technological landscape. On the latest episode of , Kaitlyn Frank, who leads courts-focused market intelligence for ¶¶ŇőłÉÄę, described exactly this phenomenon and what it signals for the future of judicial work.

From skeptics to early adopters — in 18 months

The pace of change has been striking. Courts and judges were among the least willing professional groups to adopt AI tools just 12 to 18 months ago. Today, research from the indicates that more than 60% of federal judges are using at least one AI tool in their work. That is not a gradual drift — it’s a shift.

What drove it? Judges recognized that the question was never really whether AI would enter the courthouse, but how well-prepared the institution would be when it arrived. Courts continue to experience significant staffing shortages — last year more than two-thirds (68%) of courts were facing staffing challenges, with nearly half of court professionals saying they lacked the time to complete their work, according to the . Yet, many court professionals say they have found particular value in tools that can assist with research, document summarization, and administrative workflows. This makes the clear point that AI, used well, does not replace the people doing this work, rather, it gives them room to do it better.

A symbiotic relationship at the heart of adoption

The traditional judge-clerk relationship is one of the most distinctive in the legal profession. Judges impart legal reasoning, institutional knowledge, and decades of pattern recognition. Clerks bring fresh legal training, intellectual rigor, and an outsider’s perspective. It has always been a two-way exchange, even when it was rarely described that way.

AI has made that dynamic more explicit and expanded it. Clerks arriving from law schools where AI literacy is increasingly woven into the curriculum bring a technological fluency that many sitting judges have not had the opportunity to develop. Judges, in turn, bring something AI cannot replicate: The hard-earned ability to sense when a legal argument is wrong before they can fully articulate why. One without the other creates risk; yet together, they form a genuinely effective check on AI output.

This symbiosis extends beyond individual courtrooms. Courts strengthen their institutional resilience when experienced staff and new clerks learn from each other, sharing knowledge about AI tools and their limitations. When the next generation of AI capabilities arrives — and it will — those courts will adapt from a position of strength rather than scrambling from a standing start.

Building the institutional foundation

AI in courts is developing rapidly, and that pace is largely a good thing. Legal research that once consumed hours can be completed in minutes; voluminous case files can be summarized with accuracy; and unfamiliar bodies of law can be mapped quickly, giving judicial staff a workable orientation before they dive deeper. These are meaningful gains for institutions that work under real resource pressure.

Those courts that are making the most of AI are not simply those with the most technologically curious judges or the most skilled clerks. They are the ones treating AI adoption as an institutional decision rather than an individual one. That means creating written policies that define appropriate use, establishing risk tiers that help staff calibrate how much oversight different tasks require, and developing training that evolves as the tools themselves evolve.

Some courts currently have no official AI policy in place, creating a risk that staff may use AI inappropriately. And without a shared framework, every individual is left to make judgment calls that should be made collectively — and the knowledge being built through experimentation stays siloed rather than becoming a shared institutional asset.

Courts that invest in policy infrastructure now are not slowing themselves down. They are building the foundation on which broader, more confident AI adoption becomes possible.

A new kind of judicial wisdom

The best judges have always learned from the people and experiences around them — through difficult cases, insightful clerks, and fellow judges. A willingness to keep learning throughout their careers is itself a mark of judicial wisdom.

AI does not change that instinct; rather it expands the range of what there is to learn from, and who is doing the teaching.

For courts navigating this moment, the reverse mentorship dynamic offers more than a practical model for AI adoption. It offers a reminder that the judiciary has always adapted by drawing on the full range of knowledge within its walls. The same should be true now, because even though the tools are newer, the principle is not.

For more on this download a full copy of the Thomson Reuters Institute’sĚýreport,

Key insights:

• • • AI is creating a growing “value gap” — A new report shows that while adoption is widespread, most professionals feel AI isn’t delivering the expected benefits, leading to frustration and additional client pressures.

    • This gap is driving real business risks — Organizations are seeing growing risks from this value gap, such as shadow AI use, lack of alignment with company strategy, and even potential talent loss as professionals consider leaving if AI value falls short.

    • Success depends on deliberate, well-executed strategy — Organizations need more than just more AI tools, the report shows, noting they must close the gap between strategy and day‑to‑day practice, often through structured change management.

As AI adoption becomes more widespread in professional services — more embedded in professional workflows, offered services, and client expectations — it’s actually compounding the kind of challenges that service professionals can no longer ignore.

Indeed, these challenges have moved past the traditional worries of accuracy and security to include the more subtle ways AI is changing how professionals perceive their workplace and their ability to succeed, and it’s even seeping into areas like retention, professional development, and client expectations.

The latest iteration of takes a deep dive into the ways fiduciary professionals in the legal, tax, audit, accounting, compliance, risk, and global trade areas, are managing these changes, as well as how they’re navigating the pathways their organizations are following.

The 2026 report, distilled from a survey of more than 1,800 professionals across 62 countries, shows that AI adoption, unsurprisingly, is becoming widespread — with 74% of respondents saying they use AI tools several times a week and 44% saying they rely on those tools multiple times a day.

AI-bred challenges appear in new places

As AI adoption spreads and reshapes how professionals work, learn, and interact with clients, it can be a tremendous opportunity for organizations, as long as the human aspect of AI is brought along in tandem.

“AI is a powerful force multiplier, but the judgment, relationships and accountability remain human, and that won’t change,” says Steve Hasker, President and CEO of ¶¶ŇőłÉÄę.

For more on ¶¶ŇőłÉÄę’ “Future of Professionals 2026” report,

For example, while 78% of clients say AI-enabled quality improvements are essential, only 6% say they are consistently receiving them, and that can damage a client relationship. Further, this disconnect ramps up the pressure on those fiduciary professionals delivering these services, leading many to move beyond where their organization may be technology-wise.

In fact, the report shows that more than one-third of professionals surveyed admit they use AI tools that their organization hasn’t sanctioned or in ways it can’t see, simply because they are frustrated by the quality of sanctioned tools or the lack of a clear AI strategy. This frustration — often seen as a gap in what they perceive the value of AI to be and what is actually being delivered — is widespread, with 91% of professionals saying they have felt it to some degree.

Worse yet, this frustration can manifest itself in other ways that can damage firms caught unawares. For example, the report shows that almost 3-in-10 mid-career professionals would change jobs within the next two years if AI fails to deliver the value they expect. This level of exodus could cause a cascading effect as experienced professionals take support staff, operational AI capability, and hard-won industry experience with them when they leave.

At an estimated $232,000 per replacement, this is significant potential liability on the horizon for many organizations, the report notes.

Imagining your professional future

Where fiduciary professionals and service firms go from here depends on the choices firms and corporate departments make about AI, the report states, offering three possible futures that have been drawn from how professionals are describing the current paths their organizations are on.

These three potential futures are based on how organizations choose to apply AI — from enhancing current work to fully reimagining it. And while each path has its advantages and challenges, the common thread through each is that whichever path is chosen, that choice needs to be made deliberately and with a strong commitment. Because better outcomes won’t come from arriving at a path by default — or by ignoring the human element.

Yet, as the report makes clear, no matter which path an organization chooses, it has a difficult road ahead, simply because AI strategy does not easily translate into AI practice. Indeed, almost one-third of professionals whose firm or department has a stated AI strategy say that strategy is not visible on a day-to-day basis; and 18% say their organization has no strategic direction on AI at all.

That means, roughly half of today’s fiduciary professionals are working in an environment in which a stated AI strategy either doesn’t exist or doesn’t match the reality of how their work is actually getting done.

Closing that gap and moving forward

Today, closing this strategy-execution gap is an organizational challenge that needs much more than new AI tools or stated policies — it demands structured change management that will allow organizational readiness to keep pace with evolving expectations around AI.

The most useful starting point, the report suggests, may not be whether your organization has an AI strategy in place, but whether the conditions for making such a strategy work are actually in place. Because as the report makes clear, professionals can tolerate imperfect strategies, but they cannot accept a gap between what is promised and what is delivered.

You can explore the full

Key insights:

• • • Law firms and clients are both redesigning for AI — 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’s a communication gap despite shared dependence — Even though each side’s 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 — 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 — 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 — 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.

Thomson Reuters Institute’s GCO 2030 research maps out what the transformed legal department could look like — 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 — in every leadership conversation that Thomson Reuters 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,ĚýThe 2030 legal department: 5 ways AI will transform how in-house teams workĚýhere

The problem, of course, is that both sides are designing futures that will inevitably shape the other — 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 — whether automating routine work, restructuring how it engages external counsel, or reorienting toward strategic advisory — 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’s 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 — questions that require genuine conversation between firms and the clients they serve.

The first is the question of value and pricing — 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 — or some combination of all three? And which side absorbs the productivity dividend?

The second question concerns where the work lives — 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 — based on, for example, risk, complexity, relationships, and strategic importance — is a conversation that requires honesty from both sides.

Third is the question of trust and transparency — 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 — 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? — 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’t 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 — one honest conversation at a time.

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