Key highlights:

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

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

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

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

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

Building for underserved small business owners

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

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

How AI-native firms redefine concept of a lawyer

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

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

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

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

A generation of lawyers with no one to learn from

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

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

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

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

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

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

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

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

You can find more about

Key takeaways:

• • • The dulling effect is real, but the root cause is older than AI 鈥� 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 鈥� While most organizations deploy AI for simple drafting tasks, the bigger payoff comes from using it as a discussion engine 鈥� 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 鈥� 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 鈥� 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 鈥� 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鈥檙e masking a narrowing range of underlying ideas 鈥� 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 鈥� 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鈥檚 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 鈥� the college admissions essay 鈥� 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鈥檓 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鈥檛 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 鈥� 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 鈥� 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 鈥� 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鈥檚 argument clearer. When the publishing industry rewarded a specific shape of trauma narrative in the 1980s and 鈥�90s 鈥� suffering resolved through individual resilience 鈥� 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 鈥� 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 鈥� 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鈥檛 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 鈥� Anil Doshi and Oliver Hauser, behind the , Winthrop herself points to 鈥� 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 鈥� 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鈥檚 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 鈥� and that鈥檚 exactly the kind of process organizations should be targeting. Most organizations deploying AI are using it for efficiency 鈥� drafting, summarizing, formatting 鈥� 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 鈥� 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 鈥� 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 鈥� 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 鈥� it’s the template.

You can find more听听from the 成人VR视频 Institute here

Key takeaways:

• • • Productivity softened at an unexpected time 鈥� Productivity declined to -0.4% in Q1 2026, even as demand remained strong at 2.7% growth.

    • Value per lawyer provides the clearer signal 鈥� Fees worked per lawyer (or value per lawyer) continues to grow, reflecting the combined impact of hours and rates despite volatility in productivity.

    • Margin pressure is emerging unevenly 鈥� In the Midsize segment, value growth is trailing expense growth, creating early signs of compression.

The Q1 2026 results present an unusual combination. Demand grew by 2.7%, well above historical norms, and worked rate growth remained elevated, with Am Law 100 firms pushing toward double digits, according to the 成人VR视频 Institute鈥檚 recently released听Q1 2026 Law Firm Financial Index. These inputs would typically support strong overall performance.

Yet productivity declined slightly, falling to -0.4% after six months of positive growth. On its own, that change is modest. In context, however, it reflects a more interesting shift in how performance is being generated.

The key is understanding what productivity measures and what it does not. Law firms鈥� traditional measure of productivity is hours worked per lawyer, which tracks the average number of hours logged by a firm鈥檚 lawyers to give an estimate of efficiency. However, that traditionally has not incorporated pricing mostly because, historically, law firms have focused more on the hourly output of lawyers as the mark of success. This means firms were often not concerning themselves with whether the pattern is profitable, let alone taking into account factors like demand elasticity or automation鈥檚 impact on the equation.

This is changing, however. As more and more revenue growth is being driven by rate increases rather than increases in demand or hours per lawyer, the disconnect is being magnified. And this distinction helps explain why strong inputs are producing a more muted output and why understanding that relationship is vital for firm leaders to get an accurate picture of how large law firm economics are evolving.

The divergence between hours and value

To see that situation more clearly, it is necessary to move from activity-based metrics to value-based ones 鈥� and fees worked per lawyer (or what we鈥檙e calling value per lawyer) provides that view. Fees worked is a pre-realization revenue proxy, representing the total value a firm produces before billing and collections take over, then averaging it across lawyer headcount. This method accounts for scale, giving a cleaner read on efficiency than looking at just raw hours. Because fees worked per lawyer folds rates and hours into a single measure, it captures the value that the traditional productivity metrics leave out.

As shown in the chart below, the industry is experiencing a widening gap between the hours lawyers work and the value that their work generates even as demand has remained consistently positive and rate-driven growth has stayed strong across recent quarters. As a result, productivity has been more volatile and recently turned negative. At the same time, fees worked per lawyer (or full time equivalent) has continued to trend upward, indicating that value per lawyer is still increasing rapidly despite what the old metric might have historically implied.

That means that law firms are producing far more value per lawyer even though hours per lawyer have softened slightly. The factor magnifies once you consider what period firms are measuring against. The first quarter of 2025 was exceptionally strong, creating a high baseline that can subdue the current level of growth 鈥� against a 鈥�normal鈥� year, law firm performance would be even greater.

While this is a historically recent phenomenon, it鈥檚 not one unique to 2026. Strong rate growth has often offset weaker productivity for the last couple of years. What makes the first quarter of this year more unique is that it no longer seems uniformly true across the market.

Where performance is beginning to diverge

The Q1 2026 data shows a clear separation in how firms of different sizes are translating demand and rates into value per lawyer. Among Am Law 100 firms, for example, strong rate growth remains the primary driver of revenue performance, which is being supported by disciplined headcount management that鈥檚 kept efficiency high. These firms have continued to push pricing while maintaining selectivity in hiring, allowing value per lawyer to remain resilient even as productivity softens.

The Am Law Second Hundred has embraced a different strategy. Firms in this segment are continuing to pursue growth through lateral hiring and increased capacity. This supports overall revenue but can dilute per-lawyer metrics as new lawyers ramp up. The result is softer value per lawyer despite the segment鈥檚 continued headcount expansion.

The most consequential shift is occurring in the Midsize law firm segment. Rate growth has slowed for Midsize firms, while those in other segments have maintained or exceeded prior pacing. At the same time, expenses are accelerating and are now outpacing overall fees worked growth. This creates a dynamic in which value per lawyer is still increasing, but it鈥檚 running closer to expenses.

What this all means for profitability

In the near term, there is no indication of a broad downturn. Value per lawyer continues to grow despite declines in hours per lawyer, and pricing remains strong, particularly at the top of the market. At the same time, however, the balance between value and cost is beginning to shift, most notably in the Midsize segment, where expenses are rising faster than revenue proxies.

Looking ahead, we will keep our eyes on value per lawyer, which is developing into a critical performance metric. If this metrics continues to strengthen as comparisons normalize, the softness in Q1 will likely prove temporary. If it remains constrained, particularly in segments already facing cost pressure, however, it may point to a more persistent challenge.

The broader takeaway reflected in the Q1 2026 LFFI data is that law firm performance is no longer defined primarily by the traditional measures such as hours per lawyer at the forefront without the context of rates. Indeed, this should no longer be given as much psychological weight as it was before the pandemic. In a market shaped increasingly by pricing power, the more important question for today鈥檚 law firm leaders is how much value each lawyer is generating and whether that value is keeping pace with the cost of delivering it.

You can download a full copy of the 成人VR视频 Institute鈥檚听Q1 2026 Law Firm Financial Index here

Key insights:

• • • AI is now a common facet of the legal landscape 鈥� AI is increasingly a part of legal workflows across aspects of the practice, involving not only work matters, but also interactions with clients, opposing counsel, and the courts.

• • • Foundational understanding of AI in legal is crucial鈥� The guide provides concise, practical information that lawyers and legal professionals can use to get a better grasp on AI use in legal practice

• • • Guidance needed in a fast-changing environment 鈥� AI technology and its uses, its limitations, and lawyers鈥� professional responsibilities in the practice of law are evolving rapidly 鈥� and this guide provides needed guidance and help in navigating today鈥檚 environment.

AI is influencing virtually every corner of the legal profession, impacting how legal research is conducted, documents are drafted, discovery is handled, client expectations are managed, and how courts are addressing questions of professional responsibility. Whether lawyers themselves are using AI or not, they are likely to at least be on the receiving end of AI-assisted work product from opposing counsel or clients.

To help bring clarity to this rapidly changing legal arena, the 成人VR视频 Institute and the have released the 鈥� a resource for lawyers and legal professionals who want to approach AI with clarity, confidence, and professional rigor. This 鈥淔oundational Overview鈥� is the first installment of the 鈥淎I Guideline Series鈥� being published by 成人VR视频 Institute and ILTA, with additional guides to be published within coming months.

You can also access the newly published

For AI-enabled lawyers to be the most effective, it鈥檚 important that they first understand the complex legal and technical terminology related to AI, as well as the different categories of AI, within which legal practice these technologies best fit, and the professional responsibilities that accompany their use.

Practical, concise overviews

The AI Guide is a resource for establishing a solid foundation by using the most current information in this fast-moving environment. Written with contributions from a variety of leading attorneys, legal scholars, and legal technologists, the Guide offers lawyers a practical orientation to today鈥檚 AI landscape and the issues that matter most for their legal practice.

The Guide contains concise overviews on:

• • • the current state of AI adoption across the legal profession
    • essential AI terminology
    • the major categories of AI technologies and platforms
    • the situations in which AI is often used to support legal work
    • the ethical and professional responsibility considerations that lawyers must understand, and
    • the emerging trends likely to shape AI use in legal in the years ahead.

The Guide also offers a collection of additional resources for more in-depth exploration.

As AI shows itself to be remarkably effective at assisting with many routine, time-consuming, and information-intensive legal tasks, it also continues to require careful human judgment, verification, and oversight to be most effective. That鈥檚 why understanding AI鈥檚 strengths and its limitations is becoming an essential professional skill.

A different way of interacting with information

Unlike previous technologies, AI is not simply another software application. It is a fundamentally different way of interacting with information 鈥� one that鈥檚 capable of generating analysis, drafting documents, identifying patterns, and assisting with increasingly sophisticated legal work.

AI鈥檚 application within the legal profession brings forward unique, specific considerations. It also raises questions 鈥� as well as answers that are still evolving around accuracy, trustworthiness, ethics, professional responsibility, and many other issues.

Today, these are no longer theoretical discussions; rather, they鈥檙e practical questions that lawyers are confronting every day, regardless of whether those lawyers are currently using AI in their workflows.

The 鈥�AI Guide for Legal Professionals: A Foundational Overview鈥� can give lawyers a foundational understanding on how they and other legal professionals can integrate AI into their legal practice, better understand their responsibilities, and critically evaluate new AI technologies as they evolve.

You can access the newly published

Key findings:

• • • Pricing and demand are exceptionally strong, but profits aren鈥檛 keeping up 鈥� Despite worked rate growth reaching above 12% for the largest of the Am Law 100 firms and demand growth hitting almost three-times its historical average, the LFFI landed at a flat 55, its own long鈥憆un historical average.

    • Rising costs, falling productivity, and geopolitics are quietly offsetting gains 鈥� Overhead expenses climbed, productivity slipped back into contraction, and a widening performance gap between large firms and the rest dragged on overall results; meanwhile, the Iran war appears to be dampening demand on the edges of both transactional and counter-cyclical work.

    • The market is splitting sharply by segment 鈥� Am Law 100 firms continue to drive pricing power and lead technology investment, while Midsize firms have seen rate growth slow, demand lag, and costs rise faster than revenue, all reinforcing an increasingly scale鈥慸riven competitive divide.

The 成人VR视频 Institute鈥檚 Law Firm Financial Index (LFFI) for the first quarter of 2026 landed at 55, exactly matching the long鈥憆un historical average since the Index began tracking the market in 2006. On its face, that may sound unremarkable; but dig one layer deeper, and Q1 2026 becomes one of the more puzzling quarters we鈥檝e seen in years.

Jump to 鈫�

Q1 2026 Law Firm Financial Index

Let鈥檚 start with the inputs. Am Law 100 firms pushed worked rate growth to almost 10%, building on an already record鈥憇etting 2025 and marking one of the strongest pricing environments in recent memory 鈥� and at the very top of the market, the largest law firms cleared 12%-plus rate growth. Meanwhile, demand clocked in at 2.7%, nearly triple the industry鈥檚 long鈥憆un average.

Clearly, these are not average conditions by any stretch. And yet, the LFFI score 鈥� a composite output of law firm financial performance 鈥� remained stubbornly ordinary.

So, what鈥檚 eating the gains? It turns out that the answer is multifold. For example, the report cites climbing overhead expenses, productivity that has slipped back into contraction after six months of gains, and a growing performance gap between the largest firms and everyone else 鈥� all joined forces to drag down the LFFI score.

On top of that, a new geopolitical variable 鈥� the ongoing war in Iran 鈥� weighs heavily, darkening the storm clouds further. Early indicators suggest the conflict is blunting both sides of demand at once, the report notes, freezing both the transactional M&A work that thrives on confidence and the counter-cyclical restructuring work that thrives on distress. When both the upside and downside stall simultaneously, strange results likely will follow.

The segments鈥� strategy split

Indeed, one of the clearest stories of Q1 is how sharply law firm segments are splitting apart. After years of moving largely in lockstep, pricing strategies diverged in Q1. Am Law 100 firms, for example, leaned hard into rate growth, while Midsize firms slowed their rate growth, marking the first deceleration in rate growth for any segment since 2021. Meanwhile, the Second Hundred held steady, neatly threading the middle.

This nuance matters. Large firms continued raising standard rates faster than worked rates, accepting deeper discounts to move the prices clients paid higher. Midsize firms did the opposite 鈥� allowing standard rates to lag while negotiated rates rose 鈥� signaling restraint. Midsize firms鈥� strategy may have been to capture price鈥憇ensitive demand migrating down鈥憁arket; but in practice, it hasn鈥檛 worked. Midsize firm demand growth now trails the Am Law 200 average, expenses are accelerating faster than revenue, and productivity per lawyer is declining. As a result, profit growth for the segment is running at roughly half the pace of its Am Law peers.

Rain in the forecast?

Demand, meanwhile, still remains above historical norms, even as a few raindrops are starting to fall. While several practice areas contributed meaningfully, the mix of transactional and counter鈥慶yclical practices are growing at nearly the same pace, signaling not balance, but simultaneous deceleration. Add in tough year鈥憃ver鈥憏ear comparisons against early鈥�2025鈥檚 demand surge, and the growth picture going forward becomes more stormy.

As the report makes clear, the takeaway from Q1 is not that the market is in trouble, but rather that momentum is slipping under the surface. A score of 55 isn鈥檛 a storm warning siren; it is, however, an odd resting point for a market with inputs this strong. The question for the legal market moving forward is simple: Is this just a passing sprinkle 鈥� or the first sign of a heavier storm?

You can download

a full copy of the 成人VR视频 Institute’s “Q1 2026 Law Firm Financial Index” by filling out the form below:

Key takeaways:

• • • Demand slowdown reverses LFFI gains 鈥� The LFFI鈥檚 Q4 2025 dip reflects a modest demand slowdown, marking a shift from rapid post鈥憄andemic rebound to a more stable, steady market.

    • Transactional practices plateaued while counter-cyclical regain momentum 鈥� Transactional practices leveled off while demand in the litigation, bankruptcy, and labor & employment practice areas accelerated, driven by rising disputes, regulatory pressure, and workforce complexities.

    • Clear opportunity for strategic realignment 鈥� Law firms may be able to shift their staffing toward growing counter鈥慶yclical areas, strengthening their pricing discipline and refining their recruiting processes.

After two consecutive quarters of improvements in the 成人VR视频庐 Institute鈥檚 Law Firm Financial Index (LFFI) score, the fourth quarter of 2025 marked a modest reversal in which it fell, albeit slightly to 61. The key driver behind this decline was a deceleration in demand that was meaningful enough to pull the overall score down and may signal that the market is moving into a more normalized rhythm 鈥� less snapback growth and more steady performance.

To understand what this means in practical terms, it helps to look beneath the headline numbers and examine not just what happened in Q4 2025, but also over the last two years. Then, a clear narrative emerges: Transactional work 鈥� M&A, corporate general, real estate, and tax 鈥� was powering the market in Q4 鈥�24 but largely plateaued in Q4 2025. Meanwhile counter-cyclical practices 鈥� litigation, bankruptcy, and labor & employment 鈥� regained momentum during the same timeframe.

Put differently, the practices that powered growth in the last year are fading as measured against their own baselines, while those practices that performed less strongly then are now starting to take the lead for the legal industry.

Practice level demand dynamics

By applying a magnifying glass to each transactional practice鈥檚 behavior over the past three quarters, one can identify a few important contrasts. The practice that stands out for its lowest growth in Q4 2025 is tax 鈥� and, in fact, across the final quarters of the last three years (even when it had a good performance in early 2025), that momentum didn鈥檛 translate to the end of the year. This indicates that tax has constantly posted the weakest demand growth, bottoming out at -0.9% in Q4 2023, when it was again the practice with the lowest growth. Even in the Q4 2024 鈥� a stronger year for most practices 鈥� tax grew only 1.5%, well below both its transactional and counter-cyclical peers.

This persistent underperformance may reflect several factors, such as increased internalization of routine tax work by corporate tax departments, pricing pressure in highly standardized matter types, and slower deal flow in M&A reducing ancillary tax activity. Whatever the cause, tax鈥檚 muted trajectory has had a dampening effect on overall transactional momentum and has acted as a drag on top-level demand growth.

On the other side of the room, counter-cyclical practices strengthened in Q4 2025 after a softer Q4 2024, nearly reaching the same growth that they presented in Q4 2023. Collectively, these practices rose to around 3.2% in Q4 2025, compared to about 1.5% growth in Q4 2024. This represents a true rebound after an unusually strong 2023, which was likely caused by lingering pandemic-related effects and the period鈥檚 surge in inflation.

Litigation leads the pack

Litigation provides the clearest example of this resurgence. During the Q4 2025, litigation led with roughly 4.3% growth, compared to 2.4% in Q4 2024. Indeed, the practice closed 2025 with renewed momentum, making it the standout in performance among major practices.

Litigation鈥檚 acceleration in late-2025 suggests that court systems have fully normalized, backlogs have largely cleared (in relative terms), and organizations are encountering a more contested operating environment. Regulatory scrutiny, geopolitical risk, supply chain disputes, and workforce-related conflicts all contribute to a litigation profile that is less dependent on economic cycles and more tied to the complexity of today鈥檚 business environments.

By contrast, after bankruptcy demand growth surged to 6.4% growth at the height of the pandemic recovery in 2023, the practice area experienced a dramatic cooldown the following year, falling to 0.4% just 12 months later. However, bankruptcy recovered modestly to 2.8% in Q4 2025, although still far below the extraordinary levels seen during its previous spike.

Taken together, these patterns suggest that corporate clients may be contending with a broader set of pressures 鈥� regulatory instability, workforce management complexity, and the downstream effects of post-pandemic backlogs 鈥� that could continue to generate steady legal demand.

Counter-cyclical trends reflect opportunity, not just reactive demand

The upswing in demand growth for counter-cyclical practices is not necessarily a sign of economic turbulence, however. Indeed, it shows the market can be stable and still produce more litigation, it can be cautious and still require restructuring advice, and it can be steady and still demand intensive employment support. The fact that transactional demand continues at a solid, albeit slowing pace, shows that this is not necessarily the recession-boosted practices that are driving law firm performance.

In fact, in a market in which transactional demand has stabilized and disputes and compliance work is rising, many law firms can use the moment to better align their operating model with the practice areas in which momentum is building and by aligning with actual demand.

For example, as litigation, bankruptcy, and labor & employment areas see higher demand growth, a firm may benefit from adding capacity in those areas, improving staffing leverage, and preventing partner bottlenecks. Meanwhile, steady but flattened transactional demand could call for disciplined, pipeline鈥慴ased hiring.

The practices that powered growth in the last year are fading as measured against their own baselines, while those practices that performed less strongly then are now starting to take the lead for the legal industry.

In addition, lower demand for transactional practices can represent an opportunity for law firms to refine their recruitment processes, as recruiters can take the time to seek those candidates whose skill sets offer added value. Prioritizing the hiring of candidates who bring fresh ideas and technological capabilities to support the tech-driven evolution of legal services may be the push some law firms need to meet the expectations of clients that are increasingly demanding greater value for their dollars.

This does not mean transactional work should be deprioritized, however. Instead, firms should adopt a dual鈥憈rack strategy: Optimize and streamline transactional capacity for efficiency, while strategically expanding counter鈥慶yclical teams in the areas in which demand is accelerating.

Making the strategic choice

On the face of it, it seems that many law firms face a strategic choice between doubling down on counter鈥慶yclical practices or continuing to prioritize transactional work. Current demand performance suggests counter鈥慶yclical areas offer the clearer near鈥憈erm opportunity 鈥� they are growing, resilient, and driven by structural forces such as regulatory scrutiny, workforce disputes, geopolitical risk, and more complex compliance environments.

Further, this environment elevates the importance of pricing discipline. As demand normalizes, clients become more price鈥憇ensitive and will expect efficiency and transparent staffing. Litigation and labor & employment may have more pricing power today, but disciplined pricing across all practices is critical for margin stability.

Indeed, the widening gap between transactional and counter鈥慶yclical practices signals a market in transition. The opportunity for firms lies in balancing these dynamics and aligning staffing, pricing, and operations to navigate uneven growth and capture value in a more complex legal environment.

You can download the听成人VR视频 Institute鈥檚 Q4 2025 Law Firm Financial Index听here

Key takeaways in Q3:

• • • Strong Q3 performance 鈥� The Law Firm Financial Index (LFFI) score increased by 8 points compared to Q2 2025, highlighting a quarter of robust demand and industry resilience.

    • Client-driven demand shift 鈥� Midsize law firms led the increase in transactional practices, while Am Law Second Hundred firms dominated counter-cyclical growth, driven by large corporate clients shifting work to lower-rate providers.

    • Strategic caution advised 鈥� Persistent risks, rising costs, and unresolved long-term challenges mean firms must remain cautious and strategic.

Law firms demonstrated remarkable performance through a geopolitically tense third quarter of 2025, as clients increasingly sought legal guidance to navigate market complexity and global uncertainty. This surge in demand propelled the 成人VR视频庐 Institute鈥檚 Law Firm Financial Index (LFFI) score to 63 for the third quarter, marking a notable rise from earlier in the year.

Jump to 鈫�

Q3 2025 Law Firm Financial Index

Yet, as a closer look reveals, the industry鈥檚 strong performance sits atop tectonic forces that, while driving change, also carry the potential to disrupt long-term stability.

Firms on shifting ground

At the core of this shift is a surge in client activity that鈥檚 breaking records 鈥� and coinciding with a period in which the price for legal services is rising like never before. Transactional practices are thriving, with mergers and acquisitions, corporate law, real estate, and tax practices seeing a marked uptick in demand. Midsize firms have stepped into leadership roles within these practices, demonstrating agility and resilience as they capture fresh business opportunities and respond swiftly to evolving client needs.

However, this isn鈥檛 just a story of expansion. The competitive landscape is being redrawn as clients reassess their legal partnerships. Many are prioritizing value and flexibility, shifting work to firms that offer more competitive pricing 鈥� a trend we鈥檝e noticed for the past year or so. This is obviously working to the advantage of those firms seeing significant demand growth as a result, but the more expensive law firms are also seeing boosted performance, as the trend helps them secure higher rates on the work they do maintain.

In response to this rising demand, many firms 鈥� especially those in the Midsize and Second Hundred tiers 鈥� are investing heavily in talent and technology. Even as the cost of hiring continues to climb, some firms are broadening their search beyond traditional legal roles to include specialists in technology, data, and knowledge management. These strategic hires are aimed at boosting operational efficiency and enhancing client service in an increasingly AI-driven environment.

With overhead rising and competitive pressures mounting, law firms must strike a careful balance between strategic investment and disciplined cost management.

Emerging fault lines of legal strategy

As the Q3 2025 LFFI report shows, the current environment is marked by both promise and risk. Economic and geopolitical uncertainties loom large, and the next shake-up could be just around the corner. Law firms are enjoying a period of robust growth certainly, but the ground beneath them remains unsettled. The ability to navigate uncertainty, anticipate change, and respond with agility will be critical in the months ahead.

For law firm leaders, partners, and strategists, this is a moment to reflect on the lessons of the past and to prepare for the challenges of the future. The industry rewards those who can balance ambition with caution, invest wisely in talent and technology, and stay attuned to the evolving needs of clients. A firm鈥檚 success will depend on its leaders鈥� ability to rise above the turbulence and seize the opportunities that lie ahead.

As the legal landscape continues to shift, one thing is clear: The forces reshaping the industry demand careful navigation, and firms must now approach the path forward with greater caution and strategic foresight.

You can download

a full copy of the 成人VR视频 Institute’s “Q3 2025 Law Firm Financial Index” by filling out the form below:

Key takeaways:

• • • Penalties spike when resources and controls are stretched thin 鈥� Under-resourced tax departments face significantly higher penalty exposure, with nearly half reporting at least one penalty and one-in-eight experiencing fines exceeding $1 million.

    • Reactive workloads erode savings and accelerate burnout 鈥� Tax professionals spend most of their time on reactive or tactical work despite preferring a 70/30 strategic/tactical split, creating an environment in which reaction consumes planning capacity.

    • Incrementally targeted technology deliver faster returns than big-bang overhauls 鈥擭early 70% of tax departments remain in chaotic or reactive stages of digital maturity, with many tax professionals saying they lack confidence in their department鈥檚 ability to upgrade systems within two years.

The numbers are blunt. A large portion (44%) of respondents to the report, published by the 成人VR视频 Institute and Tax Executives Institute,听say their department had at least one penalty 鈥� and among under鈥憆esourced tax departments, it was nearly one鈥慼alf. And one-in-eight say these fines topped $1 million.

And when it comes to technology, large portions of tax department professionals say their departments鈥� approach to technology is either chaotic or reactive (69%), and two鈥憈hirds say their departments aren’t currently using generative AI (GenAI) to improve efficiency within the department.

This isn’t a skills problem 鈥� it’s a system problem.

Fortunately, as the Corporate Tax Department report showed, there are steps that corporate tax department leaders can take, including:

• • Treat penalty reduction as a board鈥憀evel KPI, tracking the number, value, and cause of penalties to better pinpoint control gaps
  • Direct a defined slice of the technology budget toward core preventives 鈥� such as data accuracy, filing automation, indirect鈥憈ax determination, and reconciliation tools 鈥� that can cut errors before they become fines
  • Frame resource requests around real avoided鈥憄enalty scenarios, because showing that incremental investment could have offset last year鈥檚 losses builds a more persuasive case for future funding

Ultimately, penalties and fines are data points that reflect a deeper through-put problem and solving that requires visibility at the corporate governance level, not reactive patchwork after the fact.

The reactive鈥憌ork trap that quietly kills savings

This year鈥檚 report found that tax professionals spend most of their time on reactive or tactical work, even though they say they鈥檇 prefer to see a 70/30 strategic/tactical mix. Also, nearly 60% describe their departments as under鈥憆esourced 鈥� up from 51% a year earlier.听 Having an under鈥憆esourced tax department, our research shows, can create an environment in which reaction consumes any planning and strategic work.

Indeed, the consequences of being under-resourced compound quickly. More than half of respondents from under-resourced departments say they face penalties, and many also report missing tax鈥慶redit opportunities, delaying cross鈥慺unctional projects, and operating with less confidence in their forecasts or liability management.

Not surprisingly, burnout is another hidden cost that under-resourced departments pay daily: Tax teams that are stretch through overtime to compensate for structural and personnel shortfalls often see reduced accuracy, just when judgment is most needed.

Again, there are steps that corporate tax department leaders can take, including:

• • Establish a proactive鈥憈ime floor and mandate that each week a fixed block of time is reserved for modeling, forecasting, or credit discovery 鈥� then, measure results in saved cash or lower effective鈥憈ax鈥憆ates
  • Create a rapid鈥憈riage lane for repetitive fire drills that would allow you to codify recurring crises 鈥� such as late adjustments, jurisdictional queries, or document chases 鈥� and then automate the intake so these tasks stop devouring cognitive bandwidth
  • Invest in targeted capacity, not generic headcount; adding a tax鈥憈ech analyst or process鈥慳utomation specialist yields more lasting leverage than simply dividing the same tasks among already overworked staff

In much of this, the bigger insight is cultural: Reclaimed time is reclaimed value. Every hour shifted from reactive compliance to predictive analysis strengthens your tax department鈥檚 compliance posture.

Tech hesitation is expensive, while smaller faster wins matter more

As the report shows, almost 70% of respondents say their tax departments are still in the chaotic or reactive stages of digital maturity, and barely 6% operate optimally. Further, nearly 60% of respondents say they lack confidence in their ability to upgrade systems within the next two years. This correlation between reactive approaches and technological stagnation can feed directly into a department seeing increased penalties and an overreliance on manual processes.

Interestingly, corporate tax departments in smaller organizations, those with less than $50鈥痬illion in annual revenue, and those from very large organizations, with more than $5鈥痓illion in annual revenue, are outpacing their midsize peers when it comes to technology purchases and integration. In fact, these two groups 鈥� at opposite ends of the market 鈥� are more likely to secure leadership buy鈥慽n, tap external vendors for automation, and climb faster toward proactive operations.

Of course, GenAI sits on the cusp of this changing that trajectory. More than half (57%) of respondents say their tax departments are implementing new technology this year, including GenAI-driven tools. And those departments that are, mainly are using it for research, summarization, and document drafting, rather than more complex integrated tax analytics. However, without a reliable tax data spine 鈥� clean, centralized, and accessible data 鈥� even the smartest model can鈥檛 deliver true automation or insight.

Still, as the report outlines, there are actions that tax department leaders can take now to boost their department鈥檚 tech prowess, including:

• • Prioritize 蹿补蝉迟鈥慠翱滨 automations, such as indirect鈥憈ax determination, e鈥慽nvoicing compliance, tax鈥憄rovision close tasks, and certificate management. These are proven areas in which automation immediately cuts cycle times and penalty exposure
  • Pair early GenAI pilots with structured data. For example, start with narrow copilots for research or variance explanation, but feed them curated internal data to evolve beyond guesswork and toward data-driven decisions
  • Borrow capacity intentionally and partner with third鈥憄arty automation specialists for discrete projects using a build鈥憃perate鈥憈ransfer model. This way, internal teams inherit sustainable, well鈥慸ocumented workflows rather than black鈥慴ox solutions.

Waiting for a full replacement of the organization鈥檚 enterprise resource planning system or a perfect end鈥憈o鈥慹nd tech stack actually can trap departments in perpetual backlog. Incremental wins, particularly those tied directly to penalty reduction or labor savings, can build the momentum and political capital needed to make the case for proper resourcing for larger transformations.

The recent 2025 State of the Corporate Tax Department report reveals a powerful connection between resource allocation and tax department performance: Under-resourcing perpetuates penalties and reactive workflows that can only be broken by shifting to proactive systems and automation.

For tax department leaders, the imperative is clear 鈥� invest in prevention, reclaim strategic time, and modernize incrementally, because true progress comes not from doing more, but from choosing fewer priorities and executing on those select ones with excellence.

You can download听a full copy of the , from the 成人VR视频 Institute and Tax Executives Institute, here

Key insights:

• • • AI use falls on a spectrum 鈥� Appropriate AI use hinges on which trial function it touches upon and how much it influences outcomes.

    • AI uses must align with duties 鈥� Administrative and preparatory uses should be aligned with lawyers’ duty of competence, with outputs being checked and used within existing ethical rules.

    • Context and timing control admissibility 鈥� Courts should assess tools on a case鈥慴y鈥慶ase basis, weighing procedural stage, validation and error rates, expertise, and safeguards.

The integration of AI in the legal system is a complex and multifaceted issue, defying simplistic categorizations of right or wrong. Indeed, the application of AI in court is not a binary concept but rather one that exists on a spectrum. The appropriateness of AI use depends on two critical variables: i) which portion of the trial process is being impacted; and ii) the degree of impact that the AI usage has on the outcome.

What matters is not whether AI appears in a case, but which aspect of the trial proceeding the AI in question touches 鈥� research, drafting, evidence review, jury selection 鈥� and how deeply it may influence outcomes. A document-review algorithm that flags potentially relevant discovery operates at a vastly different point on this spectrum than an AI system that drafts legal arguments or predicts case outcomes.

Low鈥慽mpact assistance on routine tasks may be not only permissible but prudent, while high鈥慽mpact automation in fact鈥慺inding or credibility assessments can quickly cross ethical or legal lines. Understanding this spectrum 鈥� and where a specific use case falls along it 鈥� is essential for maintaining ethical standards, preserving the integrity of our judicial system, and serving clients competently in an era in which technology is reshaping every corner of legal practice. For professionals navigating this terrain, it is important to consider where, how much, and with what guardrails AI is utilized.

Administrative applications and professional competence

Administrative applications of AI have gained widespread acceptance within the legal community. The Honorable Erica Yew of the Santa Clara County Superior Court observes that many preliminary research platforms now incorporate AI-enhanced features as standard functionality. These features have become so seamlessly integrated into legal practice that their use is not only appropriate but often expected, requiring little deliberation or justification from practitioners.

Dr. Maura R. Grossman, JD, PhD, a Research Professor in the School of Computer Science at the University of Waterloo, dives deeper into this conversation by discussing the use of AI to provide summaries and chronologies as a part of case preparation. She contends that while it still requires being checked by human lawyers, it is an appropriate use of AI.

Further, the deployment of AI tools in administrative contexts aligns directly with attorneys’ fundamental duty of competence. Judge Yew articulates this connection with clarity, noting that AI should be viewed through the same lens as previous technological innovations. “When looking at rules for appropriate AI, it is akin to the rules for social media or even stationary at their inception 鈥� they are all tools,鈥� explains Judge Yew. 鈥淲e need to make sure we know how to use them and use them within the rules already set for lawyers and judges.”

This perspective underscores a critical principle: AI represents an evolution in legal tool use rather than a departure from established professional standards. Just as attorneys were expected to master word processors and legal databases in previous decades, today’s competent practitioners must understand how to leverage AI effectively while adhering to existing ethical frameworks. The emphasis, naturally, remains on validity, reliability, efficiency, fairness, and compliance with professional responsibilities 鈥� all objectives that AI, when properly employed, can significantly advance. That is at the heart of the discussion around appropriate use of AI in legal settings.

Evaluating the impact: A spectrum of appropriateness

While AI has demonstrated clear value in streamlining administrative functions and preliminary case management 鈥� indeed, many practitioners increasingly expect its judicious application in these contexts 鈥� the deployment of AI avatars in judicial proceedings demands scrutiny. In fact, this appropriateness of such technology usage exists along a spectrum, contingent upon both the intended application and the procedural stage at which it is employed.

Two recent cases illuminate the boundaries of this spectrum. In , a court authorized the use of an AI-generated avatar 鈥� in this case, an AI-generated video version of a deceased victim 鈥� during the victim-impact statement portion of sentencing proceedings. Conversely, a Appellate Court categorically rejected the use of an AI avatar for oral argument presentation, deeming it fundamentally inappropriate for that forum under the circumstances presented.

While multiple variables distinguish these cases, a critical differentiator emerges: The procedural juncture at which the avatar would function. In these cases, this temporal dimension 鈥� when in the judicial process that AI intervention occurs 鈥� proves instrumental in determining whether such technology enhances or undermines the integrity of the legal proceedings.

The gray area in practice

A Florida criminal case saw a judge use AI-enabled virtual reality (VR) goggles to review evidence 鈥� an unprecedented move that highlights the challenges of integrating advanced technology into courtrooms. Supporters say immersive tools such as the use of VR can clarify crime scenes and improve fact-finding; critics counter that AI reconstruction may be inaccurate, biased, and unduly shape memory.

Again, the core issue is context. Admissibility and weight cannot be resolved by blanket rules. Courts must assess the specific technology, its validation and error rates, the expertise behind the reconstruction, and its safeguards against manipulation. Only rigorous, case-by-case scrutiny can balance innovation with the justice system’s bedrock commitment to fairness.

Indeed, this case-by-case framework becomes all the more essential when we consider how profoundly AI has transformed the nature of evidence itself. The Florida VR case exemplifies a broader epistemological challenge facing modern courts: technology no longer simply captures reality, rather it reconstructs, interprets, and in some instances, generates it. Where traditional evidentiary rules presumed a clear distinction between genuine documentation and fabrication, AI-enabled tools occupy an ambiguous middle ground that resists categorical treatment.

It is precisely this collapse of binary certainty that scholars like Dr. Grossman have identified as the defining evidentiary dilemma of our era, one that demands not merely procedural adjustments but a fundamental reconceptualization of how courts evaluate truth.

Dr. Grossman notes that this shows a critical shift in evidentiary standards for the digital age. Traditionally, photographic and video evidence was evaluated through a binary lens 鈥� either authentic or inauthentic. Today, however, AI-generated content has fundamentally altered this calculus because content can be altered in different ways, e.g., simple noise removal versus substantive changes.

Truth now exists on a spectrum, Dr. Grossman observes, now requiring courts to navigate unprecedented gradations of authenticity when determining admissibility.

Into the future of courts

As AI continues its inexorable integration into legal practice, the profession must resist the temptation of categorical acceptance or rejection, instead embracing a nuanced, context-sensitive approach that evaluates each application against the twin metrics of where in the procedural stage AI is used and what is its impact on the finder of fact鈥檚 decision.

The future of justice depends not on whether we permit AI in our courtrooms, but on our collective wisdom in distinguishing between AI-driven tools that enhance human judgment and those that threaten to supplant it. This critical distinction demands ongoing vigilance, rigorous validation, and an unwavering commitment to the foundational principles of fairness and accuracy that have long anchored our legal system.

You can find out more about the appropriate use of AI in legal proceedings in the 成人VR视频 Institute鈥檚 AI in Courts Resource Center

3 key takeaways

• • • Law firms are adopting AI to stay competitive 鈥� While AI adoption remains robust, law firms鈥� biggest challenges are around data, metrics, pricing, and client management.

    • AI investment may be driven by fear 鈥� Law firms fear being outpaced by clients鈥� in-house legal teams, which are often ahead in AI adoption, and this is prompting law firms to invest more in AI tools and solutions.

    • Many priorities are vying for attention 鈥� Key priorities for successful AI integration include training lawyers to use new technology, creating pricing teams to set profitable rates, and focusing on metrics that demonstrate clear value.

WASHINGTON, DC 鈥� One of the most impactful issues in the legal industry today 鈥� to hear many of the law firm leaders attending the 成人VR视频 Institute鈥檚 24th Annual Law Firm COO & CFO Forum tell it 鈥� isn鈥檛 AI. Rather, it鈥檚 locating and cleansing proprietary data so the firm can properly use AI, identifying what the firm can do with AI, developing the AI metrics to measure performance, and then determining how to price AI-driven legal work in a way that shows clients real value.

Those, to hear attendees, panelists, and speakers tell it, are the real issues.

Jump to 鈫�

The 24^th Annual Law Firm COO & CFO Forum Executive Summary

鈥淎I isn鈥檛 the hard part,鈥� one panelist said. 鈥淟aw firms just have to understand what they鈥檝e done in the past, then see what needs to change in order to give clients what they say they want.鈥�

Sounds easy, right?

Others at the annual event weren鈥檛 so sure. 鈥淢y greatest fear is that somewhere there鈥檚 a lawyer right now typing into ChatGPT: 鈥�Please show me how to price you for clients鈥� 鈥� and that鈥檚 concerning because lawyers need to be looking to firm leadership for how to price legal work differently in this environment.鈥�

Still in the early years of AI

Fears aside, the debate at the annual Forum around whether AI 鈥� and more specifically generative AI (GenAI) and agentic AI 鈥� will have an impact on law firm operations is pretty much over, and most lawyers know that. Now, the debate has morphed into questions about the level of impact, how to use AI to stay ahead of the competition (and that includes clients鈥� in-house legal teams), and how law firms are going to remain profitable as charging by the hour continues to move slowly off the table.

In one of the many Forum panels that discussed AI and its impact on law firm operations, several panelists suggested that some law firms may have been scared into using AI before they were ready and before they actually knew what problems they wanted AI to solve. 鈥淲e are in the early times of this technology, even though it seems to be moving very fast,鈥� one panelist said. 鈥淓veryone is just trying to figure out what AI can do for them.鈥�

And while process automation seems like a no-brainer, it鈥檚 important to give thought to that also. Ryan Alshak, CEO of Laurel, an automation specialist company, explained that law firms need to automate those tasks that will allow lawyers to be more effective. “AI should automate the laundry and the dishes, not the science and the arts,鈥� Alshak said. 鈥淔or lawyers that means automating the business of law, not the practice of law.鈥�

Indeed, several panelists said they remembered that it wasn鈥檛 that long ago that more than a few clients didn鈥檛 want their outside legal providers using AI. Now, in many cases, corporate legal teams are already running ahead of law firms in AI use 鈥� a fact that is not going unnoticed by law firm leaders. Indeed, one law firm executive related how the firm developed a use case for AI around proactively analyzing markets and their clients鈥� positions in them after one lawyer came back from a meeting with a client in which the clients鈥� in-house legal team had already used AI to better understand its own market position. 鈥淭he lawyer was worried,鈥� the executive said. 鈥淗e knew if things didn’t change, the firm was going to lose the work.鈥�

Questions begetting questions

In fact, several panelists said it was this fear of being eclipsed by their own clients that is pushing more law firms to expand their investment in AI-driven tools and solutions. Of course, as they said, that is the easy part.

As many panels at the Forum discussed, the decision to even dip a toe into the AI pool leads to a plethora of ensuing questions, from best ways of training lawyers to use the new tools to figuring out exactly what problems you want AI to help you solve. Then, there are also the inevitable questions of how to price legal work that takes your team much less time to do, how to measure your performance in a demonstrable way, and how to manage heightened and often-changing client expectations around speed, execution, and value.

鈥淓very client wants to talk about AI, but only in the sense of how their outside law firms are going to use it to the client鈥檚 benefit,鈥� noted another panelist.

Not surprisingly, there often seemed to be as many different tech-driven priorities as there were attendees at the event. One firm executive contended that AI-guided metrics were the key 鈥� that a firm鈥檚 ability to deliver on measurements that can demonstrate clear value was critical to future success. Another said it was pricing, because that was the area that GenAI was primarily going to up-end. To address that, he explained, more firms will need to create pricing teams that leverage AI and data to determine how to set rates and create profitable business models for firms. Yet another said training lawyers to properly use this new technology was the factor upon which all else depended.

Overall, it became clear that the final brew was a heady mix of all of it 鈥� and much of it depended on a firm鈥檚 lawyers themselves. 鈥淭here鈥檚 a technological aspect to all of this, certainly,鈥� one law firm leader noted. 鈥淏ut there鈥檚 also a human psychology aspect as well.鈥�

In short, legal professionals have to be ready for the change that鈥檚 coming. However, that also means understanding that for law firms to move successfully into the future, they will need a business model that works both for their clients and for them.

“Law firms aren’t asking themselves,听鈥�What’s in it for us? What’s best for the firm?鈥�鈥� noted Laurel鈥檚 Alshak. And that may be where the next big questions lie for the legal industry.

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