Key takeaways:

• • • Corporate legal AI strategies should start with business goals, not just efficiency 鈥� While many corporate law departments first adopt AI for internally-focused use cases, the bigger opportunity is to align AI with broader business priorities such as revenue growth, risk reduction, and improved operational performance.

    • GCs should measure AI success by business impact 鈥� Metrics such as time saved and tool usage help, but stronger AI metrics connect legal work to business results. In contract review, for example, success may be reflected in improved win rates, reduced revenue leakage, faster deal completion, or dollars of risk avoided.

    • A strong legal AI strategy should produce multiple forms of business value at once 鈥� The most effective approaches do not focus on a single benefit such as cost savings. Rather, they aim to improve service delivery, strengthen operations, support growth, and reduce risk across the business.

Over the past several years, corporate law departments have begun to rapidly adopt AI tools, often spurred on by company-wide AI initiatives. In fact, in just the past year alone, department-wide AI adoption has risen to nearly half (47%) of all departments, according to respondents surveyed for 成人VR视频 Institute (TRI) research.

However, it鈥檚 not enough to simply adopt technology. For AI to truly make an impact, it needs to be integrated strategically. In taking this strategic approach, however, GCs and other legal department leaders are still in the early stages.

According to findings from TRI鈥檚 2026 State of the Corporate Law Department Report, more GCs are focused on technology than ever before. When asked their top strategic priorities over the next year, 28% answered that technology was a top priority, double the portion that prioritized technology just one year ago. And out of those mentions of technology, a vast majority specifically referenced AI as a primary area of focus.

Historically, many legal departments have thought about AI from an internal efficiency standpoint, leveraging it to perform their work quicker and cheaper. Increasingly, however, C-Suites are looking to their legal departments to provide more effective business counsel and connect legal analysis to business outcomes 鈥� and, not surprisingly, they鈥檙e expecting AI to play a role in that shift.

So how can GCs effectively make AI a priority not only for the legal department but also for the entire business? It starts with broadening the potential impact of AI processes.

From unlocking to deploying capacity

Still less than four years since the public release of generative AI (GenAI) tools through ChatGPT, many corporate legal departments are still in the early days of rolling out the technology. As a result, most GenAI use cases still tend to focus on low-hanging fruit such as document summarization and review, contract drafting and review, research, and more.

This is understandable from an individual use case standpoint. The problem is, when these use cases are translated to the leadership level for overall strategic guidance, many GCs remain focused on how to maximize the gains from that low-hanging fruit. According to TRI research, less than 20% of corporate law departments measure return-on-investment from AI at all, meaning many departments are using AI tools without any sort of guiding measurement around what success should look like. And even among those departments that are measuring AI success, most of the metrics they use center around internal department usage or department cost savings from the tool.

Those measurements are more helpful than no tracking at all, to be sure. They focus on how AI is unlocking capacity for the legal department and look for ways that attorneys can perform their work more efficiently than before. Indeed, the majority of legal departments that have invested in AI tools are currently at this point.

However, there is an additional step that legal departments need to take in order to full take advantage of the strategic value of AI. And that is connecting AI鈥檚 use to that of larger business goals by deploying the capacity it has unlocked. This requires thinking about AI less in terms of how it will impact the legal department, and more in terms of how it will impact those that the legal department serves.

For example, take a common AI use case such as contract review. Currently, the most common measurement around contract review technology is speed, such as how quickly the legal department can help a contract go from start to signature. Maximizing that value can improve the efficiency of the department, to be sure. But C-Suite partners aren鈥檛 necessarily looking for an efficient department as the end goal 鈥� they鈥檙e looking for business success.

As a result, some forward-thinking GCs are looking to connect AI usage directly with business goals or revenue. For contract review, that could mean demonstrating the impact on overall contract win rate, or whether close rates increased through use of AI. Or it could mean more successful revenue leakage protection; and it could even mean risk avoidance, measured in dollars of risk avoided. All of these can demonstrate value and be connected to the rest of the business.

Further, all of this requires close collaboration with other business units, both in terms of sharing metrics as well as understanding what success throughout the organization should mean to all parties. That said, GCs have told TRI for countless years that breaking out of a silo is a top priority for the legal department. In this case, AI implementation should be no different.

Wide areas of impact

As it currently stands, corporate law departments are seeing the most impact from AI in areas of efficiency and time saved. More than three-quarters of GCs who have talked with TRI say that AI is either currently benefiting the department鈥檚 efficiency and productivity, or that they鈥檙e expecting those benefits to occur within the next 12 months.

Connecting AI outcomes with business imperatives provides more areas of improvement, however. In this year鈥檚 State of Corporate Law Department Report and elsewhere, TRI breaks down the law department鈥檚 role into four key functions that we call the four spinning plates:

1. 1. 1. Provide effective legal services and operational excellence
      2. Offer efficient legal value within budget
      3. Enable business and strategic growth, and
      4. Protect the business鈥檚 assets and competitive advantage.

AI鈥檚 impact on efficient legal value is clear; but GCs are beginning to see that it can actually impact all four of those plates.

Those GCs looking to adopt AI as a strategic goal should be aware that said strategy should encompass more than simply internal efficiency. Not all of these benefits will be applicable to all departments, but all departments should be considering more than just one of these areas. An effective AI strategy should have multiple benefits in mind 鈥� and as such, it should take into account multiple business factors when measuring the success of the department鈥檚 AI strategy.

Entering into an AI strategy is a laudable goal for today鈥檚 GCs, but also not a light undertaking. When thinking about how AI will impact the department, leaders should take the next step beyond deploying capacity into unlocking capacity, helping attorneys not only work more efficiently but also make a bigger impact on the business at large.

You can download a full copy of the 成人VR视频 Institute鈥檚

Key insights:

• • • AI’s greatest strength in criminal justice is pattern recognition鈥� AI can process vast amounts of data quickly, helping law enforcement and legal professionals detect connections, reduce oversight gaps, and improve consistency across investigations and casework.

    • AI should strengthen justice, not substitute for human judgment鈥� Legal professionals are integral to evaluating AI-generated outputs, especially when decisions affect evidence, warrants, and individuals鈥� constitutional rights.

    • The most effective model is human/AI collaboration鈥� AI handles scale and speed, while judges, attorneys, and investigators provide context, accountability, and ethical reasoning needed to protect due process.

The law has always been about patterns 鈥� patterns of behavior, patterns of evidence, and patterns of justice. Now, courts and law enforcement can leverage a tool powerful enough to see those patterns at a scale at a speed no human mind could match: AI.

At its core, AI works by recognizing patterns. Rather than simply matching keywords, it learns from large amounts of existing text to understand meaning and context and uses that learning to make predictions about what comes next. In the context of law enforcement, that capability is nothing short of transformative.

These themes were front and center in a recent webinar, , from the听, a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI). The webinar brought together voices from across the justice system, and what emerged was a clear and consistent message: AI is a powerful ally in the pursuit of justice, but only when paired with the judgment, accountability, and constitutional grounding that human professionals can provide.

AI’s pattern recognition is a gamechanger

“AI is excellent,鈥� said Mark Cheatham, Chief of Police in Acworth, Georgia, during the webinar. 鈥淚t is better than anyone else in your office at recognizing patterns. No doubt about it. It is the smartest, most capable employee that you have.”

That kind of capability, applied to the demands of modern policing, investigation, and prosecution, is a genuine gamechanger. However, the promise of AI extends far beyond the patrol car or the precinct. Indeed, it cascades through the entire arc of justice 鈥� from the moment a crime is detected all the way through prosecution and adjudication.

Each step in that chain represents not just an operational and efficiency upgrade, but an opportunity to make the system more fair, more consistent, and more protective of the rights of everyone involved.

Webinar participants considered the practical implications. For example, AI can identify and mitigate human error in decision-making, promoting greater consistency and fairness in outcomes across cases. And by automating labor-intensive tasks such as reviewing body camera footage, AI frees prosecutors and defense attorneys to focus on other aspects of their work that demand professional judgment and legal expertise.

In legal education, the potential of AI is similarly recognized. Hon. Eric DuBois of the 9th Judicial Circuit Court in Florida emphasizes its role as a tool rather than a substitute. “I encourage the law students to use AI as a starting point,鈥� Judge DuBois explained. 鈥淏ut it’s not going to replace us. You’ve got to put the work in, you’ve got to put the effort in.”

AI can never replace the detective, the prosecutor, the judge, or the defense attorney; however, it can work alongside them, handling the volume and velocity of data that no human team could process alone.

Judge DuBois’ perspective aligns with broader judicial sentiment on the responsible integration of AI. In fact, one consistent theme across the webinar was the necessity of maintaining human oversight. The role of the legal professional remains central, participants stressed, because that ensures accuracy, accountability, and ethical judgment. The appropriate placement of human expertise within AI-assisted processes is essential to ensuring a fair and effective legal system.

That balance between leveraging AI and preserving human judgment is not just good practice, rather it鈥檚 a cornerstone of justice. While Chief Cheatham praises AI’s pattern recognition, he also cautions that it “will call in sick, frequently and unexpectedly.” In other words, AI is a powerful but imperfect tool, and those professionals who rely on it must always be prepared to intervene in those situations in which AI falls short. Moreover, the technology is improving extremely rapidly, and the models we are using today will likely be the worst models we ever use.

Naturally, that readiness is especially critical when individuals鈥� rights are on the line. 鈥淎 human cannot just rely on that machine,鈥� said Joyce King, Deputy State’s Attorney for Frederick County in Maryland. 鈥淵ou need a warrant to open that cyber tip separately, to get human eyes on that for confirmation, that we cannot rely on the machine.” Clearly, as the webinar explained, AI does not replace constitutional obligations; rather, it operates within them, and the professionals who use AI are still the guardians of due process.

The human/AI partnership is where justice is served

Bob Rhodes, Chief Technology Officer for 成人VR视频 Special Services (TRSS) echoed that sentiment with a principle that cuts across every application of AI in the justice system. “The number one thing鈥� is a human should always be in the loop to verify what the systems are giving them,” Rhodes said.

This is not a limitation of AI; instead, it鈥檚 the design of a system that works. AI identifies the patterns, and trained, experienced professionals evaluate them, act on them, and are accountable for them.

That partnership is where the real opportunity lives. AI can never replace the detective, the prosecutor, the judge, or the defense attorney. However, it can work alongside them, handling the volume and velocity of data that no human team could process alone. So that means the humans in the room can focus on what they do best: applying judgment, upholding the law, and protecting an individual鈥檚 rights.

For judicial and law enforcement professionals, this is the moment to lean in. The patterns are there, the technology to read them is here, and the opportunity to use both in service of rights 鈥� not against them 鈥� has never been greater.

You can find out more about the webinars from the AI Policy Consortium here

Key takeaways:

• • • Agentic AI poised for adoption uptick 鈥� Agentic AI is following GenAI’s rapid adoption in the legal industry, with less than 20% of firms currently implementing agentic systems but half planning or considering adoption in the near future, according to a new report.

    • Adoption depends on human oversight answers 鈥� Legal professionals are generally optimistic about agentic AI’s potential, but successful adoption depends on explicit guidance about human oversight and the lawyer’s role in maintaining ethical standards.

    • Time to retool AI education? 鈥� Agentic AI’s increased autonomy introduces new oversight and ethical challenges for law firms, making targeted education and clear guidance essential to understanding the differences from GenAI.

Over the past several years, law firms and corporate legal departments have turned towards generative AI en masse. At the beginning of 2024, just 14% of all law firms and legal departments featured an enterprise-wide GenAI tool. Just two years later, that number had already risen to 43% of all firms and departments, according to the 2026 AI in Professional Services Report, from the 成人VR视频 Institute (TRI). For large law firms or legal departments, those percentages 鈥� not surprisingly 鈥� are beginning to approach 100%.

With GenAI adoption now this widespread, legal industry leaders are now turning their attention to two primary initiatives. One, of course, is how to get the most out of the AI tools they already have 鈥� a task that is proving a bit elusive. Currently, less than 20% of lawyers say their organizations measure AI鈥檚 return-on-investment, and most corporate lawyers say they have no idea how their outside law firms are approaching AI. Thus, instituting not just AI tools, but also an AI strategy is the second top priority for law firms and corporate legal departments in 2026 and beyond.

However, even as the legal industry reaches a tipping point in adopting GenAI tools, technology innovation still continues unabated. Agentic AI has emerged as the next wave of innovation that could change how lawyers work on a daily basis, offering a way to autonomously complete multi-step tasks. For example, agentic AI systems are already being built for the legal industry that independently researches a regulation or law, drafts a document based on the finding, identifies pitfalls, and revises the document, with stops for human guidance only instituted as desired.

According to the AI in Professional Services Report, the legal industry is already making headway towards implementing agentic AI systems. For agentic AI to truly take hold in legal, however, lawyers still require more education around not only how it differs from the GenAI systems they already have in place, but also when and where human intervention needs to occur within an agentic system.

The early stages of agentic AI

Examining current agentic AI adoption for the legal industry almost takes one back in time 鈥� two years, to be exact. Following the public release of GenAI in late-2022, many legal industry organizations spent 2023 evaluating and experimenting with AI systems, usually with a small working group of interested guinea pigs. As a result, only 14% of survey respondents said their law firms or corporate legal departments were engaged in organization-wide GenAI rollouts at the start of 2024. However, more than half of respondents said their organizations expected to be rolling out large-scale GenAI systems over the next 1 to 3 years. The intervening two years since then have proved that prediction to be largely true.

Agentic AI usage in the first half of 2026 looks largely similar to GenAI in 2024. The legal industry started to experiment with agentic AI at the beginning of 2025, with an eye towards actual implementation in 2026 and beyond (particularly as legal software providers began to integrate agentic systems into their own products). As such, less than 20% of recent survey respondents say their organization is engaged in widespread agentic AI adoption, but with about half of respondents said their organization is either planning to use or considering whether to use agentic AI in the near future.

By and large, lawyers feel positive about the agentic AI movement. When asked about their sentiment towards agentic AI, 51% of legal industry respondents said they felt excited or hopeful, while just 19% said they felt concerned or fearful. Further, about half (47%) said they actively believe agentic AI should be used for legal work, while 22% felt it should not, with the remainder saying they were unsure. These figures largely track with the sentiments expressed about GenAI in 2024, which have only grown over time from about 50% positive two years ago to two-thirds of all legal professionals feeling positive currently.

This all lends further credence to a rise in agentic AI usage similar to what law firms and corporate legal departments experienced with GenAI over the course of 2024 and 2025. Indeed, when asked when they expect agentic AI to be a central part of their workflow, few have baked agentic systems into their daily work currently, but a majority of legal industry respondents expect it to be central within the next 3 to 5 years.

The unique barriers of agentic AI adoption

Agentic AI does differ from GenAI in one crucial area that may limit its growth potential within the legal industry, however 鈥� autonomy. By and large, GenAI systems operate on a back-and-forth basis: Users provide the tool a prompt, receive its output, and then iterate back-and-forth from there. Agentic AI is intended to be more automated by design, only requiring human input at pre-determined points in the process. And that makes some lawyers understandably nervous.

When asked why they might feel hesitant about using agentic AI for legal tasks, the most common answer was a general fear of the unknown, but the second most common answer dealt with the need for careful monitoring and oversight. In fact, some respondents said they were excited about GenAI, but more cautious about agentic AI鈥檚 potential.

鈥淎gentic AI, while exciting, to me removes oversight a step too far,鈥� said one such lawyer from a US law firm. 鈥淚 like the idea of prompting and reviewing a result. It is something else to have a machine have so much autonomy in the actual doing of a thing and potentially acting on my behalf without that very concrete review.鈥�

Agentic AI usage in the first half of 2026 looks largely similar to GenAI in 2024.

An assistant GC at a US company also pointed to potential privacy and security concerns, adding: 鈥淭he fact that agentic AI operates in a much more autonomous way, with a lack of control from the user, means there are many unknowns that are hidden beneath the process.鈥�

For law firm and corporate legal department leaders looking to potentially implement agentic AI systems into their practice, this means re-thinking what AI education and training will mean moving forward. Beyond that, however, legal AI educators also will need to make sure to pinpoint and perhaps over-explain those specific instances in which human oversight needs to occur in agentic systems. More autonomous does not mean fully autonomous, and particularly for lawyers with ethical duties to their work product, lawyer oversight will in fact be a necessary part of any agentic system.

For law firm or legal department leaders, that means that finding the right balance between efficient workflows and human intervention will be key to agentic AI adoption. And those organizations that can best communicate human-in-the-loop to their professionals up-front will be rewarded with more increased and reliable adoption.

Clearly, lawyers feel positively about the agentic AI future, after all. They just need it spelled out explicitly as to what the lawyer鈥檚 role will be in this new paradigm.

鈥淎gentic AI is powerful, but its moral compass must come from humans,鈥� one UK law firm barrister noted aptly. 鈥淟awyers are trained to safeguard fairness, rights, and the rule of law 鈥� principles that should guide how AI is designed, governed, and deployed. Hope lies in our ability to shape AI through these values for fairer values for society as a whole.鈥�

You can download a full copy of the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report听here

Key takeaways:

• • • Anchoring distorts judgment before you begin 鈥� Research shows a first draft shapes subsequent decisions; and an AI draft is the most seductive anchor imaginable, because it looks exactly like something a lawyer would write.

    • The First Draft Trap inverts legal training 鈥� The Socratic method builds the habit of holding multiple possibilities in tension before committing; but an AI first draft collapses that space before the real thinking begins.

    • The fix is to ask for the map, not the draft 鈥� Requesting multiple strategic framings before writing keeps judgment where it belongs and uses AI to expand possibilities rather than foreclose them.

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Welcome back to听The AI Law Professor. Last month, I examined why promised efficiency gains often become a cycle of work intensification. This month, I want to address a subtler challenge. I call it the First Draft Trap and understanding it may change how you reach for AI the next time a new matter lands on your desk

We have all heard the pitch: Staring at a blank page? Just prompt the AI. In seconds you have a working draft: structured, coherent, and surprisingly competent. The blank page problem, that ancient enemy of productivity, thus has been vanquished.

Except the blank page itself was never just an obstacle; rather, it was a space of possibility. For lawyers, it was the space in which the most important part of their work actually happens. Now, with AI in the mix, that may be changing.

Welcome to the First Draft Trap.

Simply put, the First Draft Trap is this: The moment you accept an AI-generated draft as your starting point, you have already made the most consequential decision of the entire project 鈥� most importantly, you made it by not making it. You let the machine choose your direction, your framing, and your theory. Everything that follows is editing; and editing, no matter how rigorous, is not the same as thinking.

The cognitive hijack

There is solid psychology behind why this happens. Daniel Kahneman and Amos Tversky demonstrated in their landmark 1974 paper, , that once people are exposed to an idea, this first impression distorts their subsequent judgments and becomes a mental anchor. In their experiments, subjects who watched a roulette wheel spin to a random number still let that number influence their estimates of completely unrelated quantities. The anchor held even when people knew it was meaningless.

Please join Tom Martin at the on April 28鈥�29. It鈥檚 virtual and completely free 鈥� two days of keynotes, panels, and workshops on AI and the legal profession

An AI first draft is the most seductive anchor imaginable. It is not random 鈥� it is plausible, and it is well-organized. It sounds like something a lawyer would write. And that is precisely what makes it dangerous. You know intellectually that it is just one of many possible approaches to addressing the matter, but the anchor holds anyway.

That is the First Draft Trap at the cognitive level. The AI draft is not just one option you happen to prefer. It is a filter that prevents you from seeing the other options that were available to you, the roads you never even noticed that you did not take.

Consider what this means for a profession built on the opposite instinct. From the first day of law school, lawyers are trained to resist the obvious answer and to think like a lawyer. The Socratic method exists for exactly this reason. A good professor hears your confident response and asks: What else? What if the facts were different? What is the argument on the other side? The goal is not to arrive at an answer, per se. It is to build the mental habit of holding multiple possibilities in tension before committing to any one of them.

The First Draft Trap is the anti-Socratic method. It delivers a confident answer before you have even formulated the question properly 鈥� and instead of interrogating it, you polish it.

The value of the blank page

Think about what a senior partner actually does when a junior associate brings them a memo. The partner鈥檚 value is not better writing; rather, it is peripheral vision: The ability to see what the memo does not address, the argument not considered, or the framing that would land differently with this particular judge or this particular jury. That capacity to see beyond the document in front of them is why clients pay senior partners premium rates. And it is precisely the muscle that atrophies when your default workflow begins with the prompt generate a draft.

The AI draft is not just one option you happen to prefer. It is a filter that prevents you from seeing the other options that were available to you, the roads you never even noticed that you did not take.

The two-system framework offered by Kahneman and Tversky gives us a clean way to describe what is going wrong. System 1 is fast, intuitive, and pattern-matching; while System 2 is slow, deliberate, and analytical. The practice of law, at its best, is a System 2 discipline. We, as lawyers, are trained to override gut reactions, challenge assumptions, and think through consequences before acting.

In this way, the AI first draft feels like a System 2 output. It is structured, footnoted, and methodical. However, your decision to accept it as a starting point is pure System 1 鈥� a fast, intuitive grab at the nearest plausible answer. You have used a sophisticated tool to bypass the sophisticated thinking the tool was supposed to support. That uncomfortable period of ambiguity, of not knowing which path is best, is where the real lawyering lives.

What to do instead

None of this means stop using AI. It means stop using AI to skip the hard part that matters.

Before you ever ask for a draft, ask for the map. Describe the matter or document you are working on, then ask the AI for three fundamentally different strategic framings for the problem. For each framing, request the strongest argument in its favor and its most serious vulnerability. Then ask which framing best fits the client鈥檚 goals, the audience, or the procedural posture. Close with a clear instruction: Do not write a draft yet.

That last instruction is the key. It keeps you in the driver鈥檚 seat during the phase that matters most. You are using AI to expand the possibilities before you prune them, not after. And, most importantly, it gives you the opportunity to think for yourself about other important possibilities and add them in.

In the terms used by Kahneman and Tversky, use AI to fuel System 2, not to hand the controls to System 1. Let the machine generate options, and you exercise judgment.

For lawyers, the ability to see what is not there is the whole game.

Do not let the first draft blind you to it.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and author of the forthcoming听. He is 鈥淭he AI Law Professor鈥� and writes this eponymous column for the 成人VR视频 Institute.

Key insights:

• • • A significant visibility gap persists between legal departments and the C鈥慡uite 鈥� Most general counsel believe their legal department contributes strategically, yet senior executives often fail to see or understand that value.

    • Strong internal relationship鈥慴uilding is critical (and often underdeveloped) 鈥� This capability enables legal teams to spot risks earlier, stay embedded in decision鈥憁aking, and make their work more visible across the business.

    • Closing the gap requires communicating legal鈥檚 value and increasing true AI fluency 鈥� For legal teams to be seen as proactive, strategic partners rather than task executors, communication and strong AI fluency are essential.

General counsel (GCs) have spent years doing more with less, tightening their legal spend, and aligning the law department鈥檚 priorities with the wider business. And yet, despite all of this effort, a striking visibility gap persists. While 86% of GCs believe their department is a significant contributor to overall organizational objectives, only 17% of the C-Suite agrees, according to the , from the 成人VR视频 Institute, which was based on more than 2,300 interviews with corporate general counsel. Meanwhile, 42% of C-Suite executives say the legal function contributes little or not at all to company performance.

The challenge for GCs is whether their staff have the skills and capabilities to make their work visible, relevant, and understood by the business at large. To address this perception gap in 2026, every GC needs to prioritize building richer internal relationships with business leads, moving from task-based to outcome-focused messaging, and improving the team鈥檚 collective AI fluency.

Empower teams to build internal relationships

Nearly half of all GCs surveyed for the report cited staffing and resource constraints as the top barrier to delivering additional value, a concern that has remained stubbornly consistent for years. Beyond headcount, the report underscores that the deeper challenge facing legal departments is relational.

Internal relationship-building is one of the most critical and underrated people skills in a legal department’s collective skill set. Indeed, 68% of GCs rate internal dialogue as their most valuable source of information about emerging risks. In fact, the most successful GCs use a deliberate combination of formal and informal methods to build connections with the internal business units that they serve.

You can learn more about how to assess your legal department鈥檚 strategic positioning with the听成人VR视频 Institute鈥檚 Value Alignment toolkit, here

Some run structured weekly face-to-face sessions with business departments, complete with schedules, plans, and frameworks. Others rely on walking the halls, open-door policies, and ad-hoc conversations that keep the corporate law department visible and accessible on a human level.

The report offers a five-dimensional framework to help GCs audit where, with whom, and how often legal is in dialogue with other parts of the business.

Use communication tactics that focus on business outcomes

Even when legal departments are doing excellent work, they often describe it in the wrong language. Many in-house lawyers categorize their contributions in task-based terms 鈥� such as 鈥淲e support M&A鈥� or 鈥淲e analyze contracts鈥� 鈥� rather than in value-creating terms.

Some in-house legal leaders have progressed to stakeholder-level framing, such as, 鈥淲e protect the company from competitive threats鈥� or 鈥淲e support new business opportunities.鈥� Still, neither of these levels truly communicates value to a C-Suite audience, the report shows.

To effectively align the law department’s priorities with business goals, in-house attorneys need to develop the skill of communicating through a business lens. For example, one GC states that the primary goal of the law department is to “find the fastest and most compliant way for the sales department to sell products.” This response reframes the legal function鈥檚 activities as much more business fluent and value-added.

Legal teams are not always good at touting their accomplishments, however, and this is a challenge when a lot of the work can be categorized as invisible. For example, when protecting the company is done right, threats are eliminated before they occur and no one notices. When efficiency is unlocked through process improvement, the C-Suite only sees the outcome if someone connects the dots explicitly. This is why surfacing invisible value is now a business imperative for corporate law departments.

Advancing from AI literacy to AI fluency

The most significant skills challenge facing legal departments in 2026 is how to best use AI strategically. Mentions of AI as a strategic priority among GCs have doubled in the past year, according to the report. In fact, almost half of all GCs now reference AI in their survey interviews. Yet the report draws a sharp distinction between being AI literate and being AI fluent, with most departments being the former but not the latter.

To close that gap, the report recommends a six-layer model covering learning, empowerment, ownership, accountability, usage, and expectations.

At its core, the model asks GCs to start with open encouragement and access to AI tools to build momentum, then shift toward more formal expectations around adoption to make AI use a daily habit.

You can download a full copy of the 成人VR视频 Institute鈥檚 here

Key takeaways:

• • • Strategic collaboration is becoming a defining strength for the region 鈥� Latin American organizations are realizing that progress in AI accelerates when they combine forces by linking industry expertise, academic talent, and public鈥憇ector support.

    • AI initiatives rooted in real local challenges are gaining global relevance 鈥� By developing solutions grounded in the region鈥檚 own structural needs, whether in infrastructure, finance, agriculture, education, or mobility, many LatAm firms are producing technologies that are both highly impactful and naturally scalable.

    • Demonstrating clear outcomes is becoming fundamental 鈥� Organizations that show concrete operational improvements, measurable efficiencies, or stronger customer outcomes are strengthening their position with investors and partners.

In recent years, Latin America has experienced significant growth in investments related to AI, accounting for . This is strikingly low given that the region makes up around 6.6% of global GDP, highlighting the region’s opportunities to scale AI initiatives even further. Although there are notable differences among countries, Mexico and Brazil 鈥� the two largest LatAm economies 鈥� stand out for their volume of AI projects and funding, followed by other nations such as Chile, Colombia, and Argentina.

By recognizing the region鈥檚 strengths 鈥� which include cost-effective operations, access to data, clean energy, and public support 鈥� the region鈥檚 businesses can better position themselves and design strategies to draw in international investors that may be increasingly seeking promising locations for AI development.

Lessons from LatAm鈥檚 AI success stories

Latin America has produced remarkable AI success stories that can serve as models to build confidence among investors. These cases 鈥� involving companies that attracted substantial investment and achieved growth 鈥� demonstrate valuable best practices that range from technological innovation to working with governments and corporations. Some of these best practices include:

Building strategic alliances

The journey of innovation rarely unfolds in isolation. At times, the presence of large, established companies, whether local industry leaders or multinationals, has served as a catalyst for AI projects. The experience of that specializes in AI-powered agricultural irrigation, proves it. Now, Kilimo is partnering with EdgeConneX, a data center company based in the United States, on a community .

Academia, too, can be woven into this narrative. Collaborations with research centers or universities offer scientific credibility and connect ventures with emerging talent. In Mexico, AI startups often originate within university settings 鈥� such as computer vision projects from the National Autonomous University of Mexico (UNAM), for instance 鈥� and maintain agreements that sustain ongoing innovation and technical progress even with modest resources. And academic validations, whether in published papers or conference accolades, tend to resonate with foreign investors. Indeed, the emergence of this ecosystem that features early corporate clients and academic mentors frequently lends a distinctive appeal for those seeking investment.

Focusing on local problems with global impact

Within Latin America, certain issues prove especially relevant in situations in which AI solutions intersect with sectors renowned for regional strengths, such as fintech and financial inclusion, agrotech optimizing agriculture, and foodtech drawing on local ingredients. The experience of Chilean food startup NotCo 鈥� in which and subsequently exported 鈥� suggests how innovations rooted in local context may generate broader attention.

By addressing needs in urban transport, education, mining and related areas, local LatAm companies can provide access to homegrown data and users, which can further refine technology and open pathways for investors into similar emerging markets. When AI solutions respond to genuine pain points rather than mere novelty, momentum often builds more quickly, and the model finds validation among that evaluate investments.

Showing results and AI ROI early on

Questions linger for many executives . Evidence of clear metrics like cost savings, sales growth, or error reduction can prove persuasive, especially when complemented by success stories from local clients.

Recent studies show that companies ; and such figures tend to reassure those considering investment by illustrating tangible improvements. Testimonials or independent validations, such as a university study, can further illuminate achievements.

The act of quantifying impact 鈥� whether in efficiency, revenue, or other relevant KPIs 鈥� has a way of transforming perceptions from uncertainty toward clarity.

Leveraging government incentives and collaborations

Many Latin American nations have put forth support programs for AI and tech projects, such as non-repayable funds, soft loans, and tax benefits for innovation illustrated in , , , or the .

Public financing, when present, often acts as a stamp of validation for private investors. For example, this trust extended to Brazilian startups receiving Finep support for AI health projects, which in turn can shift perceptions for foreign ventures capitals. Engagement in government pilots, such as smart city initiatives or solutions for ministries, provides valuable exposure. In such contexts, public-private partnerships and incentives seem to act as quiet levers for growth and legitimacy.

Seeking smart and diversified financing

Financial strategies in Latin America have been shaped by the interplay of local and foreign capital. Local funds often bring insights and patience, while foreign funds may offer larger investments and global scaling experience. Ownership dilution sometimes accompanies the arrival of strategic investors, whose networks can prove invaluable, such as . Programs like 500 Startups, Y Combinator, MassChallenge, and international competitions have ushered LatAm AI startups such as Heru, Rappi, Bitso, and Clip into new rounds of capital following increased exposure.

Efficiency in capital management, which can be demonstrated with lean burn rates and milestone achievement with limited resources, signals an ability to execute within the realities of LatAm, which may enhance the appeal for future investments. The cultivation of relationships and responsible stewardship of capital frequently matters as much as the funds themselves, suggesting that the value of mentorship, contacts, and reputation is often intertwined with deepening financial support.

Unlocking AI Investment

By applying these principles, Latin American companies have achieved a better position to attract AI investments to their projects and help position the region as a viable destination for technology capital. These recent experiences show that when a LatAm company combines innovation, talent, and strategy 鈥� while communicating its story well 鈥� it can win over global and local investors alike. Each of the best practices noted above is based on real lessons: international alliances (NotCo with US funds), leveraging incentives (Brazilian companies funded by Finep), talent formation (Santander and Microsoft programs), focus on ROI (successful use cases that convince boards), and more.

Latin America has challenges but also unique advantages. Companies that manage to navigate this environment intelligently will increase their chances of securing the financing needed to innovate and grow. By doing so, they will contribute to a virtuous circle in which each new success attracts more investment to the region and opens doors for the next generation of LatAm AI ventures.

You can find more about the challenges and opportunities in the Latin American region here

Key takeaways:

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

    • The profession can鈥檛 agree on what constitutes “legal judgment鈥� 鈥� Unlike other professions, there is no agreed-upon definition of legal judgment or clear standards for when AI should be used.

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

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

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

notes that calls for 鈥�鈥� to accelerate the development of legal judgment early in lawyers鈥� careers.鈥�

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

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

Three pillars for an AI-era lawyer formation system

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

Pillar 1: Integrate work experience into legal education

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

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

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

Pillar 2: Decompose legal judgment into teachable micro-skills

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

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

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

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

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

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

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

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

The challenge of who pays

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

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

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

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

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

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

You can learn more about the impact of AI on professional services organizations at TRI鈥檚 upcoming 2026 Future of AI & Technology Forum here

Key takeaways:

• • • Efficiency is a means, not an end 鈥� Gains from AI only count when you can show what they enabled: better advice, stronger protection, smarter business support.

    • Narrow measurement invites cuts 鈥� Legal departments that measure AI value only through cost savings are telling C-Suites that legal costs less, thereby inviting budget and headcount reductions.

    • Measure across all four plates 鈥� A framework that captures effectiveness, risk, and enablement alongside efficiency is what shifts perception of the legal department from cost center to strategic asset.

Your legal department has invested in AI tools, adoption is growing, your team is saving time on routine work and, by most accounts, work operations are running faster. Then your CFO asks a simple question: What has AI delivered for the legal department?

If your answer centers on hours saved and cost reduced, you are not alone. However, you may be leaving your most important value story untold. And in a climate in which legal departments are under more scrutiny than ever to demonstrate the full return on their AI investment, that gap matters.

This is the fourth and final part of our series on the 鈥淔our Spinning Plates鈥� model, which frames the GC’s evolving responsibilities as:

1. 1. 1. delivering effective advice
      2. operating efficiently
      3. protecting the business, and
      4. enabling strategic ambitions.

This article focuses on the Efficient plate and specifically on the risk of letting it do too much of the talking.

The Efficient plate under pressure

For a GC, making the best use of what are often limited resources is a constant pressure. The Efficient plate sits alongside, not above, the other three plates and must be kept always spinning. Right now, however, for many in-house legal teams the Efficient plate is receiving disproportionate attention, and for understandable reasons.

AI adoption in corporate legal departments is accelerating quickly. According to the 成人VR视频 Institute’s AI in Professional Services Report 2026, nearly half (47%) of corporate legal respondents surveyed said their department has already integrated generative AI (GenAI) into their work 鈥� more than double the figure from the previous year. A further 18% reported that they鈥檙e already using agentic AI, with more than half expecting agentic AI to be central to their workflow within the next two years.

GCs are genuinely excited about what this makes possible. As one GC said in the survey that underpinned the AI in Professional Services Report: “It presents the promise of getting out of low-value work and into higher-value work that supports the business.鈥� Another described their vision of a legal department that is “boldly digital-first, relentlessly innovative, and tightly woven into business priorities.”

Clearly, the opportunity is real, but so is the risk of measuring it badly.

The measurement trap

Our 2026 research found that only one-quarter of legal departments are currently measuring the ROI of their AI tools. That alone is striking given the pace of adoption but the follow-up finding is where the real problem lies 鈥� of those departments that are measuring ROI, 80% are tracking it in terms of internal cost savings.

Reducing external spend, automating high-volume processes, and bringing more work in-house are all legitimate efficiency gains and worth reporting, of course. However, when cost reduction becomes the only story being told, two things can happen. Your C-Suite learns to associate your department’s value with how little it costs, a frame that is very difficult to escape once it鈥檚 established. And the wider value that efficiency enables in terms of sharper risk identification, faster business support, and higher-quality advice goes unmeasured and therefore unrecognized.

听If your metrics only capture time saved and cost reduced, and not what that freed-up capacity actually delivered, you are measuring the means and ignoring the end.

Think about what GCs themselves say they want from AI. As several GCs said in the survey, they鈥檙e hoping AI will provide them with “better output on more meaningful tasks,” “proactive, strategic insight,” and “getting out of low-value work.” These are not efficient outcomes, per se; rather, they are effectiveness, protection, and enablement outcomes, made possible by improved efficiency.

So, if your metrics only capture the input (time saved, cost reduced) and not what that freed-up capacity actually delivered, you are measuring the means and ignoring the end. This is the efficiency trap 鈥� measuring the plate so narrowly that it starts to work against you.

Reframing how you measure efficiency

Measuring efficiency well does not mean measuring it more. It means measuring it differently, and always in relation to the business you support. A few principles worth applying include:

Present spend in a business context 鈥� Legal spend as a percentage of company revenue tells a more credible story than a raw cost figure. It scales with the business and can be benchmarked meaningfully against peers.

Show what technology investment actually delivered 鈥� Time saved through automation is a useful starting point, but the stronger case is what the team did with that time. Tracking the shift from routine to strategic work over a period of time is a far more compelling ROI story.

Connect efficiency gains to business outcomes 鈥� An efficiency gain that enabled a faster product launch, prevented a compliance risk, or improved stakeholder satisfaction has a value that no cost metric will capture. Build those connections explicitly into how you report the value of the legal department to the C-Suite.

New resources to help

To support GCs in getting this right, the 成人VR视频 Institute has added two new resources to its Value Alignment Toolkit that directly address this measurement gap.

The Metrics Library brings together more than 100 metrics organized across all four spinning plates. It is a practical starting point for GCs to browse, select, and adapt to the specific goals of their departments, making it easier to build a measurement framework that reflects everything departments do, not just the part that appears in a budget line.

The AI Success Metrics guide addresses the AI measurement gap head-on with a best practice guide and a hands-on worksheet designed specifically for legal departments navigating AI adoption and asking: How do we actually know whether this is working? It looks beyond cost savings to capture the fuller picture of AI value including quality, capacity, strategic contribution, and risk.

Getting the balance right

In today鈥檚 environment, every GC needs to consider their answer when their C-Suite asks what the legal department delivers. Are your department鈥檚 metrics giving them the full answer or just the part that’s easiest to count?

Efficiency is not the enemy of strategic value. A department that runs well, uses its resources wisely, and embraces technology thoughtfully can in turn create the conditions for everything else the business needs from its legal function. However, that case only lands if your metrics measure across all four plates, not just one.

You can explore the new Metrics Library and AI Success Metrics guide, along with the full 成人VR视频 Institute鈥檚 Value Alignment toolkit听here

Key takeaways:

• • • Employees face contradictory messages or none at all 鈥� Nearly 40% of professionals surveyed report receiving conflicting directives about AI usage from clients and leadership, while half report no client conversations about AI have occurred at all.

    • Workers lack feedback on whether their AI efforts matter 鈥� Professionals who are experimenting with AI tools without knowing if their efforts are valued are left uncertain about whether investing time in developing AI skills is worth it.

    • Job displacement fears are rising 鈥� While employees remain cautiously optimistic about AI usage in their workplace, concerns about job displacement have doubled over the past year.

As generative AI (GenAI) tools flood into legal and accounting workplaces, organizations are deploying powerful technology without giving their employees clear directions on how to use it. Worse, some have received no guidance.

New research that underpinned the recent 2026 AI in Professional Services Report听from the 成人VR视频 Institute (TRI), reveals a disconnect between AI availability and organizational guidance, which is creating confusion that may undermine both employee experience and the technology鈥檚 potential value. (The report鈥檚 data was gathered from surveys of more than 1,500 legal, tax, accounting, and compliance professionals across 26 countries.)

Employees navigate inconsistent AI policies or none at all

Approximately 40% of the professionals surveyed said they received contradictory guidance from clients and leadership about AI tool usage, with directives both encouraging and discouraging their use on projects and in RFPs. This ambivalence is slowing down decision-making at the front lines 鈥� a place in which AI could deliver the most value.

Equally concerning is the fact that half of professionals indicated that no conversations with clients about AI tool usage have taken place yet. And when discussions do occur, concerns about data protection and accuracy are the main topics.

This confusion extends to external relationships as well. More than two-thirds of corporate and government clients remain unaware of whether their outside professional service providers are even utilizing GenAI. And the majority of clients have provided no direction whatsoever to their outside law firms concerning AI use, respondents said.

Organizations often ignore what employees need to know

Perhaps most revealing is how organizations are measuring 鈥� or failing to measure 鈥� whether their AI investments are paying off. Almost half of respondents said their organizations are not measuring return on investment (ROI) at all. Among the minority (18%) of respondents that said their organizations do track ROI, the metrics they use tell a story about organizational priorities. That fact that internal cost savings and employee usage rates lead the list suggests a focus on efficiency over innovation or quality improvements.

This measurement vacuum has consequences for employee experience. Without clear success metrics, employees lack feedback on whether their AI experimentation is valued, discouraged, or even noticed. The absence of ROI frameworks also makes it hard to justify training investments or dedicated time that allows employees to develop AI fluency.

AI usage doubles while support systems fall behind

AI usage among professional service organizations has nearly doubled over the past year, and professionals are increasingly integrating these tools into their workflows, the report shows. Yet organizational infrastructure that could support this adoption surge lags badly. Most professionals said they expect GenAI to become central to their work within the next two years 鈥� but that may be happening without roadmaps from their employers.

In addition, notable barriers in employees鈥� usage of AI remain. When asked what barriers could prevent their organization from more widely adopting GenAI and agentic AI, almost 80% of professionals cited concerns over inaccurate responses. Other concerns included worries over data security, privacy, and ethical use. Most of these suggest an ongoing lack of trust in GenAI.

The tool landscape adds another layer of complexity. Publicly available tools dominate current usage, with more than half of respondents (57%) citing their use, while proprietary or industry-specific solutions remain largely in the consideration phase. This suggests employees are often self-provisioning AI tools rather than working within enterprise-supported ecosystems. This potentially opens organizations to increased risk exposure because of security gaps, compliance risks, and inconsistent quality.

Employees鈥� job displacement fears increasing

Despite these challenges, employee sentiment toward AI remains cautiously optimistic. More than half (57%) of respondents said they are either hopeful or excited about the future of GenAI in their industry. Clearly, employees see AI’s potential to enhance their efficiency, automate routine tasks, and free up their time for higher-value work.

At the same time, hesitation and concern among employees are rising, particularly around accuracy, job displacement fears, and the unknown implications of autonomous AI systems. Notably, concerns about job displacement have doubled over the past year, and this trend demands organizational attention and transparent communication about a workforce strategy to combat this concern.

What organizations need to do now

Organizational leaders who are serious about positive employee AI experiences need to step up their efforts to provide guidance to employees and gain the ROI that AI promises. Specific steps they can take include:

• • • Draft clear and consistent guidance 鈥� Create explicit policies for employees about in which instances AI use is encouraged, required, or prohibited. This includes client communication protocols, data-handling requirements, and escalation procedures in those situations in which AI outputs seem questionable.
    • Develop and implement meaningful ROI metrics 鈥� Organizations must move beyond usage rates and cost savings as key success measurements. Tracking data points that capture quality improvements, time redeployed to strategic work, and client feedback on AI-enhanced deliverables present a more comprehensive picture. Also, leaders need to share these metrics transparently in order to give employees an understanding about organizational priorities.
    • Invest in structured learning 鈥� The survey shows professionals are experimenting with dozens of different tools from ChatGPT to specialized legal tech platforms. Organizations should curate recommended toolsets, provide hands-on training, and create communities of practice in which employees can share effective prompts and use cases with other users.

Our data shows that the employee experience around AI adoption reveals a workforce that is hopeful but hungry for direction and concerned about job impacts. Leaders who implement these actions effectively are more likely to unlock the strategic value that AI promises while building the trust and competence needed for their organizations and its employees to thrive in an automated future.

You can download a full copy of the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report听here

Key highlights:

• • • Lawyers excel at semantic legal work while AI excels in syntactic tasks 鈥� Syntactic work (document generation, pattern recognition) is where AI excels, but semantic work involving exercising independent judgment, reflecting on consequences, and fulfilling fiduciary duties remains uniquely human.

    • Fiduciary duty as the core of legal relevance 鈥� What distinguishes lawyers isn’t just听whatthey do, but听how and why听they do it. The fiduciary relationship demands human understanding of context, balances competing interests, recognizes unstated concerns, and exercises discretion.

    • 5 hours to deepen or diminish 鈥� The five hours lawyers are expected to gain each week by using AI can either accelerate professional obsolescence or deepen lawyers鈥� relevance, depending on what they do with it.

This is the first of a two-part blog series that looks at how lawyers can keep their skills relevant in the age of AI

Lawyers expect to gain a full five hours per week of worktime due to the efficiency derived from AI use, according to the 成人VR视频 2025 Future of Professionals Report. Yet the fear of job loss among lawyers is rising, as those viewing AI as a threat or somewhat of a threat grew from to almost two-thirds (65%) of those surveyed, according to the 成人VR视频 Institute鈥檚 2026 AI in Professional Services Report.

Many in the legal profession are asking how lawyers are uniquely valuable at a time when machines can process legal information faster and cheaper. The answer lies in understanding the difference between what AI does in processing legal information and what humans do in exercising legal judgment, says , Founding Director of the .

Defining 2 levels of legal work

Understanding what makes lawyers particularly听meaningful听in this current AI moment requires distinguishing between two different levels of legal work in an environment in which AI-enabled information systems are compressing humanity and legal judgment into data points and draining away the storytelling and moral nuance that ground both. According to Lee, these different levels involve the syntactic and the semantic:

• • • Syntactic 鈥� Lawyers process information, generate documents, and recognize patterns at the syntactic level, meaning those tasks in which AI excels and delivers promised efficiency gains. 鈥淭he danger is that we will use this efficiency merely to generate more syntactic volume,鈥� Lee explains, adding that this will result in faster processing of more documents at greater speeds. 鈥淚f we do that, we will have automated ourselves out of a profession.鈥�
    • Semantic 鈥� The semantic aspect of lawyering highlights the irreducible skills of the legal practice, which include exercising independent legal judgment, reflecting on consequences, demonstrating care for clients, and fulfilling fiduciary duties.

This distinction between the semantic level is inherent within the practice of law definition, Lee says, pointing out that many jurisdictions distinguish between “providing legal information” (not practicing law) and “exercising independent legal judgment” (the essence of legal practice).

He also rightly contends that the existential risk facing lawyers is not in AI completing legal tasks, but rather the temptation to reduce lawyers鈥� role to verifying machine output and processing legal information. Conflating these two concepts is a challenge for the legal profession and requires increasing the appreciation for the craft of legal reasoning and judgment.

Making this more difficult is that the current information age complicates this picture by challenging society’s assumptions about reality, consciousness, and the moral meaning of human life 鈥� all at an exponential rate, Lee says. Similarly, AI and information systems threaten to reduce everything, including human beings and law itself, to processable data by stripping away the narratives and meanings that define humanity, he adds.

Semantic qualities of legal judgment

The question of what makes lawyers especially relevant in the AI era is mainly answered in how and why they do what they do, rather than in what they do. For example, Lee points to skills around executing their fiduciary duty and ensuring legitimacy and meaning as key characteristics of lawyers鈥� semantic qualities.

Fiduciary duty 鈥� When a client seeks legal counsel, it鈥檚 legal judgment 鈥� not information processing 鈥� that the client wants. Lawyers, as part of their fiduciary duty to their clients, demonstrate human and legal understanding of the unique context of each case and the consequences of various legal paths forward. This bond of trust between attorney and client demands reflection, consideration, care, and proper purpose.

The fiduciary duty of the lawyer to the client requires balancing competing interests, recognizing unstated concerns, and exercising discretion in ways that honor both the letter and spirit of the law. At the heart of this balance is legal reasoning and professional judgment, which often involves navigating the critical gap between legal rules as written and their meaningful application to human circumstances.

Legitimacy and meaning 鈥� Beyond the fiduciary of care exercised in individual client relationships, lawyers serve a broader purpose in their role to safeguard law’s connection to the narratives of justice and human dignity that legitimize its authority. Indeed, lawyers maintain the connection between law and its humanistic foundations, so that the narratives that give legal authority its legitimacy depend on this connection. 鈥淭he artwork that one associates with the law (in law schools and courtrooms) connects actions and legal judgment of attorneys to the mythic meaning of justice, equality, and the rule of law,鈥� Lee explains.

How to deepen appreciation for the special relevance of lawyers

The five hours that lawyers said they expect to gain each week through AI-driven efficiency represents a choice point for the profession. These hours can either accelerate lawyers鈥� obsolescence or deepen their relevance. To ensure the latter, Lee advises lawyers and legal institutions to examine ways to put those hours to good use by, for example:

Collaborating on apprenticeships 鈥� Bar associations, practicing lawyers, legal service providers, and law schools should consider apprenticeship models that teach professional norms and values through mentorship that allow law students to learn the craft of legal reasoning through guided practice.

Recommitting more fully to legal service 鈥� Law firms and in-house counsel must reclaim humanistic awareness as central to their professional identity. The efficiency gains from AI should be reinvested into semantic work, which include counseling clients, exercising moral judgment, and fulfilling fiduciary duties with greater care and reflection.

Improving legal education 鈥� Law schools must return to the humanistic formation of lawyers, echoing the vision of the pre-2007 , before economic pressures reduced legal education to producing commercially exploitable graduates. In addition, AI ethics must be integrated systemically across the curriculum into doctrinal courses rather than being confined to elective courses.

Looking ahead

The five hours gained through AI represent a defining choice for the legal profession. The special relevance of lawyers in the AI age lies precisely in the human components and semantics aspects of lawyering.

In the concluding part of this blog series, we look at how the legal profession needs to rethink how it trains lawyers in order to prevent AI from eroding legal judgment skills