Key insights:

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

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

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

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

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

A new model for learning

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

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

---

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

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

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

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

Leadership in an AI age

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

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

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

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

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

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

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

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

The new rules of professional growth

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

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

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

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

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

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

Key insights:

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

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

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

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

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

Significant discussion about legal judgment

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

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

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

Development of business judgment is lacking

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

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

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

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

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

Why both views need to be addressed

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

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

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

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

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

Key insights:

• • • Capital and human resource drains — Managing the billable hour model is seen as wasting capital and time for both law firms and clients.

    • The billable hour and talent retention��— The billable hour may be contributing to a talent drain in litigation defense and other practices.

    • Data may be the answer — One firm is pivoting to data-driven pricing based on key performance indicators (KPI), which it views as a win-win for both the firm and its clients.

“The billable hour is a gigantic waste of time for everyone involved.” That is the blunt assessment of founding partner Bob Kopka. “It stifles innovation, penalizes efficiency, and has grown into a status quo that costs more than it benefits.”

The administrative-industrial complex

The primary target of Kopka’s critique is the massive administrative overhead required to manage the billable hour. On the client side of the firm’s work in defense litigation, insurance companies employ vast departments and third-party vendors specifically to review, audit, and cut legal bills. In response, law firms like Kopka have had to build their own payment departments to counter this, represented by teams of billers trained on dozens of different platforms and appeals departments dedicated to what Kopka describes as “chasing payments on appeals, most of which are both unfair and unsuccessful.”

This administrative-industrial complex, Kopka argues, creates a world in which lawyers spend as much time justifying their work and subsequent compensation as they do practicing the law.

“The time to set up new matters, add timekeepers, approve budgets as well as approve invoices, and cut separate checks for every matter is arduous,” notes Kopka Law COO Donna Markus. By moving to a monthly retainer or portfolio-based alternative billing model, Kopka Law aims to dismantle this bureaucracy, freeing up significant capital and human resources for both the firm and the client.

Killing the profession 6 minutes at a time

Perhaps the most provocative aspect of Kopka’s stance is the link between billing models and the legal industry’s talent crisis. The traditional model requires attorneys to feverishly capture every one-tenth of an hour, documenting their day into six-minute increments with hyper-specific narratives and present-tense verbs.

According to Markus, this isn’t just an annoyance; it’s an existential threat to the defense bar. “Talent is leaving the defense side because of the tedious nature of capturing their time,” she warns, adding that when a lawyer’s value is reduced to a billing code, the “most valuable time a lawyer can spend” — engaging in free thinking — is often treated as a non-compensable activity because it doesn’t fit into a standard billing code.

This administrative-industrial complex, Kopka argues, creates a world in which lawyers spend as much time justifying their work and subsequent compensation as they do practicing the law.

“We are professionals,” Kopka states. “Our performance should be reviewed and judged by our KPIs [key performance indicators], not on whether a billing entry ‘appears excessive’ or whether the attorney obtained permission to do a jury verdict search”.

Why the billable hour hates AI

Kopka and Markus also highlight a dangerous paradox in the modern legal market: The billable hour actively penalizes law firms and their lawyers for becoming more efficient. As AI increasingly automates routine legal tasks, firms that use AI to finish a task in 30 minutes that used to take three hours are effectively cutting their own revenue under the traditional model.

And as AI starts to replace some billable activities, many insurance clients are refusing to pay for software or AI costs while simultaneously expecting to reap the benefits of the efficiency and cost-savings that those tools provide.

Kopka sees alternative billing models as flipping this incentive. Under a well-constructed billing arrangement, a firm has every reason to invest in cutting-edge technology. If they can achieve the client’s desired outcomes faster and with fewer resources, they are rewarded for their efficiency rather than punished for it.

Data as the solution for the “inertia of fear”

If the benefits are so clear, why then has the rest of the industry been so slow to follow? Kopka and Markus attribute the delay to “inertia born of fear” — including the fear of being underpaid or simply not knowing how to measure value besides using the clock.

They argue that this fear is no longer justifiable because the data exists to solve it. “Fear not,” they insist. “We have metrics.” Between the insurance company’s data on frequency and severity and the firm’s own data on litigation categories, there is more than enough information to fashion a mutually beneficial pricing arrangement.

The Kopka model focuses on key performance indicators (KPIs), rather than simply time spent on a matter. These KPIs include:

• • • cycle time and case disposition
    • early evaluation and consistent communication
    • indemnity outcomes relative to injury type
    • strategic collaboration and value added

Kopka believes this approach restores the firm-client relationship and moves it toward a true partnership. The law firm is finally treated as an independent contractor, rather than a legal services vendor that needs to be micro-managed. This gives the law firm the autonomy to focus on delivering legal services that achieve the client’s goals, instead of having to hold endless discussions about how the firm is managing itself as a business.

A call to action for the defense bar

Kopka Law sees its success with these models as a challenge to its peers. The firm uses the term alternative billing arrangements, arguing the legal industry’s attempts to use what are commonly called alternative fee arrangements (AFAs) actually focus on the wrong objectives. “AFAs are often designed solely to save the client money,” explains Markus. “They’re destined to fail because they potentially force the law firm to compromise or cut corners to meet a low-cost bar.”

Instead, Kopka aims for a win-win model that appropriately — and perhaps even generously, if structured and executed properly — compensates the law firm for excellent service and the achievement of specific, agreed-upon goals.

Kopka Law is actively encouraging other firms and insurance carriers to enter these negotiations. The firm sees it as a necessary evolution to stabilize budgets and make legal spend more predictable for clients.

For Kopka and Markus, the message is clear: The legal industry has the metrics and the tools to do better. And the better approach, they argue, is a firm-client partnership that’s driven by data, aligned incentives, and a commitment to results over activity. And with AI and advanced data analytics, that model is within reach for most law firms.

You can find more about how law firms are managing their billing and pricing issues here

Key highlights:

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

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

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

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

Planning around 3 law firm business models in the AI era

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

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

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

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

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

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

Aligning the talent model to the future business model

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

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

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

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

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

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

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

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

You can learn more about the challenges facing legal talent here

Key takeaways:

• • • New Zealand legal market achieves revenue and profit growth — A new TRI report on the New Zealand law firm market shows firms rebounding strongly from the pandemic, with firm revenue and profits up impressively.

    • Transactional and counter-cyclical practice demand drives success — More than half of the legal demand for New Zealand law firms comes from transactional work, which rose of the past year; meanwhile, counter-cyclical practices saw even higher growth rates.

    • Managed expenses and increased partner utilisation boost profit margins — Despite rising expenses due to technology and knowledge management investments, New Zealand law firms maintained manageable costs and increased equity partner utilisation.

For New Zealand law firms, years of careful investment and strategic pandemic recovery have paid off. Today, strong demand has vaulted firm revenue growth above double digits, leading to profits not seen among New Zealand firms since the early days of the pandemic, according to a new report from the Thomson Reuters Institute (TRI) and data from TRI’s .

Jump to ↓

2026 Report on the State of the New Zealand Legal Market

Demand at New Zealand law firms rose more than 5% last year, following stagnant or decreasing growth rates between 2022 and 2024, according to TRI’s 2026 Report on the State of the New Zealand Legal Market. As a result, overall firm revenue rose by more than 10%, placing it back near pre-pandemic levels. Coupled with managed expense growth, New Zealand law firms saw their first double-digit profit growth since 2021, after declines in demand for transactional practice work scuttled profits in 2022 and 2023.

Overall, more than half of the legal demand for New Zealand law firms comes from transactional work such as corporate general and M&A practices; and indeed, demand for such work rose last year after seeing only modest growth or declines in the the years prior. However, the report shows that even more notable is the rise of demand in counter-cyclical practices such as disputes & litigation, insurance defense, and workplace relations. The growth rate of counter-cyclical demand topped that of transactional demand in the second quarter of last year and continued to separate itself throughout the remainder of the year.

At the same time, firms continued to enjoy steady rate growth, with their worked rate growth over this past year coming close to their average rate growth than was seen from 2022 to 2024.

Interestingly, this represents a different strategy by New Zealand firms, compared to those in the United States or Australia, to capture profits through other means while keeping their rate increases manageable. And indeed, while Australian and US firms have largely seen falling utilisation, New Zealand equity partners averaged more hours worked per month in 2025 than they did the year prior, which helped to drive higher revenues.

Meanwhile, total expenses ticked up slightly last year compared with 2024, with both direct expenses and indirect expenses rising. However, much of this growth in indirect expenses is largely due to increased investments in technology and knowledge management, an increasingly necessary expense in the age of AI.

As a result of the demand rebound and more manageable expenses, New Zealand law firms are seeing their revenues and profits soar.

Overall revenue more than doubled, percentagewise, in 2025, which in turn directly led to sky-high profits in 2025 that were almost triple what they were the year prior. Profit per equity partner also saw similar gains.

Overall, New Zealand law firms on average largely held steady with a profit margin around 43%, while some firms saw profit margins soar above 50%.

As the report shows, all of this represents a very positive financial picture for New Zealand law firms. The return of demand, steady rate growth, and managed expenses has provided firms a solid footing from which to grow further. And if New Zealand law firm leaders can build on those positive metrics, they look poised to take these gains and grow further in 2026.

You can download

a full copy of the Thomson Reuters Institute’s “2026 Report on the State of the New Zealand Legal Market” by filling out the form below:

You can read TRI’s latest “Inside the Shift” feature,��The AI adoption board game: Why law firm leaders can’t afford to play it safe here

Let’s be honest: most law firms know AI is a big deal. They’ve read the headlines, attended the conferences, and nodded along when someone says, “AI will change everything.” The problem? Knowing that AI matters and actually doing something strategic about it are two very different things. And according to our latest Inside the Shift feature article, that gap is where many law firms are starting to lose ground.

Our latest Inside the Shift feature, author Michelle Nesbitt-Burrell, Marketing Strategy Director for �������� (TR), frames AI adoption as a boardgame that’s already underway. Some law firms are moving confidently across the board, while others are stuck on the starting square, not because they don’t see the future, but because they’re hesitating. The latest TRI research shows that while the majority of lawyers say they believe AI will fundamentally transform the legal industry within the next few years, far fewer expect real change inside their own firms anytime soon. That disconnect is risky — especially when competitors and clients aren’t waiting around.

Here’s what should concern every law firm partner — corporate legal departments aren’t just playing the same AI adoption game, they’re winning it.

 

One of the most uncomfortable truths the article reveals is that corporate legal departments are further often ahead on AI adoption and utilization than their outside counsel. In fact, many corporate legal teams are investing in AI faster and using it more deeply in their day‑to‑day legal work. That means clients are reviewing contracts faster, doing more work internally, and increasingly judging their outside law firms on their technological sophistication. In a world like that, the excuse that We’re still experimenting stops sounding reasonable pretty quickly.

The article breaks law firms into three players on the game board:

1. 1. 1. 1. 1. The laggards — Those firms with no meaningful AI plans and very little ROI to show for it.
            2. The adopters — Thos firms that are experimenting with tools but don’t really have a clear strategy. These firms see some efficiency gains but too often hit a ceiling.
            3. The innovators — Those firms with visible, intentional AI strategies. These firms are far more likely to see ROI, revenue growth, and long‑term competitive advantages.

So, what separates the winners from everyone else? The article details the PLAYERS framework: pilot with purpose, leadership that sets the pace, action over perfection, strong ethics, serious education, good data, and — most importantly — strategy before tools. In other words, those law firms that want to become innovators should stop asking, What AI should we buy? and start asking What are we actually trying to achieve?

Clearly, AI isn’t a side project anymore. Law firms that treat it like one may save some time, but as the article fully explains, those firms that approach AI adoption and implementation strategically will reshape how legal work gets done. The game is already moving — the only question is whether your firm is playing to win or quietly falling behind.

You can find more��Inside the Shift feature articles��from the Thomson Reuters Institute here

Key takeaways:

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

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

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

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

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

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

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

It’s the associate.

The question nobody wanted to ask out loud

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

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

Someone said it out loud.

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

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

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

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

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

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

Fewer reps, greater weight

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

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

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

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

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

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

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

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

The dinosaur that adapted

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

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

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

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

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

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

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

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

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

Key insights:

• • • • Creating prototypes of IP-protected teaching tools — Law school faculty can build working AI teaching tool prototypes in one to two hours without IP worries because key optional settings enable a closed system to ensure professors’ intellectual property remains protected.

      • Strong prompting skills create faster prototypes — The best instructions initially set the AI’s character, explains what the AI needs to accomplish, lists which documents to reference exclusively, describes how the response should be formatted, and mentions any applicable legal jurisdiction limits.

      • Feedback from students is positive — Students’ responses show AI simulators reduce anxiety and build confidence by providing unlimited low-stakes practice opportunities that make legal concepts more digestible through active dialogue rather than passive reading.

Law schools face a persistent challenge on how to provide individualized skills practice when one professor must serve many students. And today’s traditional legal education offers limited opportunities for students to practice oral arguments, evidentiary objections, and witness examinations. Indeed, the repetition necessary to build authentic courtroom skills does not scale easily with law professors in the classroom alone.

To address this challenge, at the University of Missouri–Kansas City School of Law�� that simulate trial judges, three-panel appellate courts, witnesses, and evidentiary objection scenarios. Prof. Serra has seen firsthand how these tools give students unlimited, low-stakes practice opportunities that reduce their anxiety while building confidence in their legal reasoning and judgement.

Building your first AI learning tool, step by step

Creating custom AI teaching tools requires far less technical expertise than most professors would assume. As Prof. Serra explains, if you have a general idea of what you want the tool to accomplish, then “you can have a working prototype in less than two hours from idea to execution.”

The process begins with choosing a large language model (LLM) platform, such as ChatGPT, Claude, or Gemini, and securing a paid subscription, which most law schools will provide, she explains. During the sign-up process, optional settings enable a closed system to ensure law professors’ intellectual property is not shown to the students and is not used to train the LLMs.

Next, you should gather class materials, including slides, case files, manuals, and problems the professor has already created. After that, it is necessary to define one specific use case, such as an evidentiary objections practice tool, a Socratic method simulator, or a client interview assistant.

The building process itself takes about one to two hours and requires no coding skills. “You just start talking to the LLM like you are training a teaching assistant to do exactly what you want to do,” Prof. Serra adds.

Having built many tools, she highlights three critical components that are necessary for the efficient, useful, and flexible prototype. These include:

1. Prompting skills

Effective prompting is key to generating a good prototype. ��According to Prof. Serra, the ideal prompt includes defining the AI’s role (You are a trial judge in a federal district court), specifying the task the AI should deliver, identifying which documents to use exclusively, describing the desired output format, and including any jurisdictional constraints.

2. Multimodal features in AI tools

Most platforms allow for voice-activated chat mode, in addition to typing back and forth, which helps students respond out loud in real time. Custom AI tools also have shareable links, which enables easy deployment to students. Once a student engages with the tool, they can send back a transcript of the interaction. Some platforms even allow shareable audio files so students can get feedback from their professors on skills performance, not just content.

3. Verifying reliability

Evaluating the quality of the AI output is important but naturally varies by use case. For classroom tools, Prof. Serra recommends deploying prototypes quickly and using students as testers. If the tool produces outputs with inaccuracies, she encourages students to bring these errors to class for discussion. That way, everyone learns how to critically diagnose problems with AI outputs. A variety of problems cause AI inaccuracies — the AI itself, poor prompting, incorrect legal reasoning, or incomplete training.

For wider deployment without the builder’s direct oversight, Prof. Serra recommends an extended period of testing and iteration. Her tool, MootMentorAI, which simulates a three-judge appellate panel for first-year law students preparing for oral argument, is one example. Because MootMentorAI was developed for use by a colleague, Prof. Serra worked with a research assistant to conduct 80 simulations over the course of a semester — 40 from the plaintiff’s perspective and 40 from the defendant’s perspective — to verify reliability and improve performance before deployment without her supervision.

Overcoming adoption barriers among peers

Faculty resistance remains the most significant barrier to deploying AI-enabled teaching tools in legal education. “There’s lots of faculty pushback, distrust, and a healthy dose of skepticism with AI,” Prof. Serra acknowledges, arguing that even so, AI-powered tools are teaching assets for all law school courses. “Even in doctrinal classes that run on traditional Socratic dialogue, professors can still use AI to reinforce learning outside the classroom through tools, such as podcast-style lectures, a multiple-choice practice assistant, tools to enable issue-spotting, and essay practice tied to course fact patterns.”

Common concerns among law school faculty include confidentiality, intellectual property protection, fear of revealing exam content, and perceived lack of technical expertise. However, Prof. Serra points out that these fears often stem from her colleagues’ misunderstanding of how closed systems work. Indeed, if privacy settings are correctly deployed, uploaded materials will not be used to train public models and students cannot access source documents.

Indeed, the most effective strategy for overcoming resistance is personal demonstration, she says, noting that she frequently sits down with colleagues virtually to build tools based on the colleague’s own use case. She’s built everything from a Startup CEO simulator for a business course, to an interview assistant for Career Services, to a simulated forensics expert for students to cross-examine. This grassroots approach, combined with speaking at conferences and identifying super fans who can champion the technology, gradually builds institutional buy-in, she adds.

Multifaceted student feedback

Student feedback has been overwhelmingly positive, with learners describing how AI simulators make legal skills training more accessible, more engaging, and less intimidating. In fact, students are often surprised by how convincingly AI tools can simulate judges, witnesses, and other real-world lawyering scenarios. They also appreciate having permission to use AI as a legitimate learning aid.

They also report that real-time interaction makes course concepts more digestible because these tools turn learning into an active dialogue rather than passively staring at a casebook. Finally, students say the simulators reduce anxiety before oral arguments or presentations by enabling unlimited, low-stakes repetition that builds confidence and keeps practice from feeling overwhelming.

Clearly, AI tools are quickly becoming essential learning infrastructure, and legal education cannot afford to treat them as optional add-ons if it expects to stay relevant. As a growing chorus of educators and employers warns that institutions must evolve, the real question is whether schools will build responsible, faculty-guided systems fast enough to meet students where the profession is headed.

When deployed thoughtfully, these platforms can scale individualized skills training, deepen engagement beyond the casebook, and build durable confidence that law students can carry into their future legal practice.

You can download a full copy of the Thomson Reuters Institute’s��recent white paper, , here

Key findings:

• • • The market remains strong, but growth is difficult — Australian law firms are still posting solid demand and rate growth in the first half of FY 2026, yet the pace is becoming more challenging to sustain.

    • Australia is no longer a single legal market, but three distinct ones — The report identifies three clearly differentiated law firm segments: Large firms leading demand growth through aggressive investment; Big 8 firms emphasizing pricing power and cost discipline; and Midsize firms pursuing steadier, more moderate growth.

    • Early signals suggest GenAI is reshaping productivity and leverage — Changes in hours worked across seniority levels point to possible early impacts of GenAI; and while overall productivity is stable, non‑equity partners and associates are logging fewer hours, while senior associates and equity partners are working more.

The Australian legal market enters the back half of FY 2026 with strong topline numbers, but beneath the surface, the market is working harder to maintain its momentum. Firms are navigating slower rate growth, shifting demand patterns, and the early tremors of what may prove to be a generative AI-driven transformation.

Solid footing, harder-won gains

Australian law firms built an impressive track record over the post-pandemic era, and the first half of FY 2026 shows that run may not be over yet — although its character is changing. Demand growth of 4.8% year-to-date sits a full percentage point above the average quarterly pace since FY 2022, according to the Thomson Reuters Institute’s just-released 2026 Australia: Midyear Legal Market Update report. Worked rates, meanwhile, rose 4.7%, which is respectable, but a noticeable step down from the 5.4% average growth firms had enjoyed since FY 2022.

At the practice level, the picture is broadly encouraging. Both transactional and counter-cyclical practice groups are accelerating, with workplace relations leading all practices at 9.9% year-to-date growth and corporate general close behind at 7.7%. However, a potential warning sign lies in the divergence among each macro-category’s flagship practice: insolvency & restructuring is surging at 7.9%, while mergers & acquisitions sits in contraction at -2.1%. If dealmaking remains subdued while restructuring activity accelerates, transactional practices could face meaningful headwinds in the quarters ahead.

Three markets, not one

Perhaps the most significant finding in this year’s report is what the market-wide averages have been concealing. Last year’s Australia State of the Legal Market report highlighted growing competition between the Big 8 and a broader group of Large law firms that were challenging the Big 8’s dominance. This year, a refined three-segment framework reveals that the former Large category was actually masking two very different stories, between Large firms and a newly identified set of Midsize firms.

The newly delineated Large firms have emerged as the clear demand leaders, posting nearly 7% year-to-date growth — roughly double their peers — fueled by aggressive investment and expansion. The Big 8, by contrast, are leaning into pricing power and cost discipline, growing demand at a more measured 2.7%. And the Midsize cohort, at 2.4% demand growth, is charting a balanced, moderate course.

The profitability divergence is even more striking. Since FY 2022, the firms now classified as Large have grown profits per lawyer by 27.4%, while Midsize firms managed just 3.1% — much closer to the Big 8’s 7.1% than to their former stablemates. What previously appeared to be a broad-based challenge to the elite was, in reality, concentrated among a smaller group of high performers that were pulling the average upward.

Early signals of AI-driven change

The report also surfaces a potentially significant development in law firm productivity. While overall hours worked per month ticked up slightly for the average qualified fee earner, the gains are unevenly distributed. Non-equity partners recorded their third consecutive productivity decline, and junior and mid-level associates are also slightly down. Yet senior associates and equity partners are logging more hours, keeping overall numbers stable. One possible explanation is GenAI — if firms are deploying these tools most heavily on research, drafting, and document review tasks that traditionally filled junior and mid-level associate hours, this is precisely the pattern we would expect to see. While it’s too early to draw solid conclusions, the distribution of hours may represent an early sign of how AI is beginning to reshape the traditional leverage model.

There is also a note of caution from firms’ clients. �������� Market Insights data shows Australian general counsel growing more conservative in their spending outlook, with net spend anticipation for overall legal work dropping to 0 points. That means just as many GCs see their legal spend increasing as those that anticipating it decreasing.

Interestingly, international legal spend tells a different story — Australia-based GCs are increasingly looking outward, with the Asia-Pacific and Latin American regions emerging as areas of particular activity, while Europe has cooled. For Australian firms with cross-border ambitions, the short-term opportunity may lie to the global east and south rather than west.

Looking into the second half of the year

As the Australian legal market moves into the second half of FY 2026, the story is no longer one of uniform prosperity but rather, one of strategic differentiation. Demand remains healthy, profitability is solid, and expense discipline is improving; however, growth is no longer evenly distributed. The law firms that thrive in the quarters ahead will be those that understand which game they’re playing. In an increasingly segmented market, adaptability — not scale alone — will define success.

You can download a full copy of the Thomson Reuters Institute’s “2026 Australia: Midyear Legal Market Update” report by filling out the form below:

You can read TRI’s latest “Inside the Shift” feature,��Premortem: Your 2028 agentic AI pilot program failed��here

Picture this: It’s 2028, your law firm spent real money on an agentic AI pilot, and now it’s quietly been shut down. No press release, no victory lap — just a post‑mortem that nobody wants to read. In our latest��Inside the Shift feature article, we see that such a future is very likely unless firms start preparing for agentic AI in a way that’s very different than how they think they should.

The big idea is simple but uncomfortable: Success with generative AI (GenAI) does not mean your organization is ready for agentic AI. GenAI works because it’s forgiving. You can paste text into a tool, get a decent answer, and move on — even if your data is messy and your workflows live in people’s heads. Agentic AI doesn’t work that way. It expects clean data, documented processes, and clear rules. If your firm runs on institutional memory, workarounds, and a kind of just ask Linda problem-solving process, then the system will eventually break down.

To examine this and many more situations, the Thomson Reuters Institute (TRI) has launched a new feature segment,��Inside the Shift, that leverages our expert analysis and supporting data to tell some of the most compelling stories professional services today.

Our latest Inside the Shift feature, Premortem: Your 2028 agentic AI pilot program failed��by Bryce Engelland, Enterprise Content Lead for Innovation & Technology for the Thomson Reuters Institute, walks us through two fictional but painfully familiar failure stories of how two separate firms handled their agentic AI pilot programs.

The author explains how the first firm moves fast after crushing their GenAI rollout and assuming agentic AI is just the next logical step. Everything looks great in a sandbox; but then the system hits real‑world chaos: Undocumented exceptions, fragmented document storage, and conflict checks that only work because humans intuitively know when something feels off. One bad intake decision later, client trust is damaged and the pilot is frozen. In this example, the tech didn’t fail — the organization did.

The second firm goes the opposite direction. They’re cautious, thoughtful, and obsessed with governance. They build guardrails, limit risk, and launch a perfectly reasonable pilot. And then… nothing happens. Attorneys ignore the system — not because they hate AI, but because using it only adds risk with no reward. If it works as it’s supposed to, nothing changes; but if something goes wrong, they’ll be blamed. So, unsurprisingly, the rational choice is to nod in meetings and quietly keep doing things the old way until the project dies of inertia.

The challenge is that “preparing” doesn’t mean what most people think. It doesn’t mean buying early, and it doesn’t mean waiting for maturity. Rather, preparing means understanding now why these systems fail, and building the institutional capacity to avoid those failures when the technology arrives in full.

The feature article points out the common thread here: These failures have very little to do with AI capability; rather, they’re about incentives, documentation, and institutional honesty. Firms that succeed with agentic AI won’t be the ones that buy in early or wait patiently. The winners, the piece explains, be the ones doing the boring, unsexy work now: Writing things down, fixing information architecture, identifying hidden dependencies, and aligning rewards so adoption isn’t all risk and no upside.

In short, this article isn’t a warning about technology. It’s a warning about pretending your organization is ready when it’s not — and mistaking optimism or caution for preparation.

So, dive a little deeper behind the headlines about AI adoption and how to make agentic AI work for your organization. Click through and read today’s Inside the Shift feature. It might help you see more clearly than before whether the path your organization is pursuing with agentic AI will carry it over the goal line and into the next decade… or leave your team watching from the sidelines.

You can find more��Inside the Shift feature articles��from the Thomson Reuters Institute here