Key insights:

• • • AI can improve officer safety听鈥� By helping them prepare for high-risk situations and make better decisions under pressure, advanced technology can enhance officer safety.

    • AI can increase operational efficiency听鈥� AI can reduce administrative burdens and improve efficiency overall, allowing officers to spend more time on police work.

    • Responsible implementation is essential听鈥� To ensure AI strengthens public trust while protecting civil liberties, proper guardrails and oversight need to be enacted.

Each year, during , we pause to honor the brave men and women in law enforcement who have made the ultimate sacrifice in service to their communities. As we pay tribute to those we have lost, we are reminded of the inherent dangers officers face every day.

In recognition of the importance of reflection and advancement, it is imperative that we examine the responsible application of emerging technologies, especially AI, to enhance officer safety, support their objectives, and reinforce overall public safety.

AI is already being integrated into public safety systems in meaningful, measurable ways. When guided by strong ethical principles, transparency, and commitment to community trust, AI can serve as a force multiplier and a protective partner for members of law enforcement. The goal is not to replace officers, of course, but to equip them with better tools, that allow them to reduce risk and return home safely after every shift.

Improving situational awareness and operational readiness

One of the most immediate benefits of AI in law enforcement is its ability to enhance situational awareness. When officers respond to a call, the first minutes on scene are often the most critical 鈥� and the most dangerous. AI can help reduce uncertainty by providing rapid access to relevant information.

For example, AI-powered systems can analyze incident data, criminal records, and community reports to give officers a clearer picture of what to expect when they arrive on scene. This includes identifying patterns of violence, recognizing repeat offenders, or flagging locations that may have a history of high-risk activity. Such insights allow for better preparation, smarter deployment, and more informed decision-making under pressure.

Additionally, AI can assist in public records and open-source searches, pulling critical data from comprehensive databases, the internet, and connected devices in seconds rather than hours. This immediate access to information enables faster, more effective responses. In short, AI can save valuable time when seconds count.

Streamlining administrative work to focus on the mission

Law enforcement officers spend a significant portion of their time on administrative duties, such as writing incident reports and managing court schedules and citations. These tasks, while necessary, take officers away from community engagement and proactive policing.

AI can help reduce this administrative burden by automating routine documentation. Natural language processing tools can draft reports based on officer input, ensuring consistency and freeing up time for frontline duties. Similarly, AI-driven scheduling systems can optimize shift assignments, account for court appearances, and manage on-call rotations. This AI-enabled administrative assistance goes a long way in ensuring that staffing levels are appropriate and that officers are not overburdened.

When guided by strong ethical principles, transparency, and commitment to community trust, AI can serve as a force multiplier and a protective partner for members of law enforcement.

By reducing the administrative load, AI allows officers to focus on what they do best 鈥� serving and protecting their communities. This not only improves job satisfaction among officers themselves but also increases operational efficiency and public safety outcomes.

Building guardrails for responsible AI use

As with any powerful advanced technology, the integration of AI into law enforcement must be guided by clear policies, oversight, and accountability. The goal is not to deploy AI indiscriminately, but rather to ensure its use enhances safety without compromising civil liberties or public trust.

This requires proactive collaboration between technologists, law enforcement agencies, policymakers, and the communities they serve. Standards must be developed for data privacy, algorithmic transparency, and bias mitigation. AI-enabled systems should undergo rigorous testing and independent review before deployment. Further, officers must be trained not only on how to use these tools, but also on the limitations and ethical implications of using these tools as well.

Finally, public trust is essential. Members of the community need to know that AI is being used to protect their safety and that of law enforcement 鈥� it is not a tool to surveil them without cause. Communicating transparently how the AI systems are designed, what data they use, and how decisions are made will be key to maintaining legitimacy and trust with the public.

A future of safer streets and stronger trust

The integration of AI into law enforcement is not about replacing human judgment 鈥� rather, it鈥檚 about augmenting officers鈥� judgment. When used responsibly, AI can reduce risk, improve preparedness, and support officers in carrying out their duties more safely and effectively.

In the years ahead, we can expect to see broader adoption of drone first responders, real-time language translation tools, and predictive systems that further help enhance officer and community safety measures. However, technology alone is not the answer. Success will depend on how thoughtfully these tools are implemented, how well citizens鈥� rights are safeguarded, and how deeply communities are involved in the process.

This week, as we honor those officers who have fallen in the line of duty, let us also commit to doing everything we can to protect those who serve today. AI, when applied with care, can be a powerful ally in their mission, keeping officers safe, allowing them to make better decisions, and together, building stronger, safer communities for all.

The data provided to you may not be used as a factor in establishing a consumer鈥檚 eligibility for credit, insurance, employment, or for any other purpose authorized under the Fair Credit Reporting Act.

You can find more on the challenges facing law enforcement here

Key insights:

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

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

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

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

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

A new model for learning

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

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

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

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

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

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

Leadership in an AI age

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

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

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

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

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

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

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

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

The new rules of professional growth

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

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

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

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

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

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

Key insights:

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

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

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

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

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

Significant discussion about legal judgment

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

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

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

Development of business judgment is lacking

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

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

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

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

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

Why both views need to be addressed

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

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

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

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

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

Key highlights:

• • • Technology satisfaction is in freefall and needs human-side investments 鈥� Satisfaction with tax technology dropped sharply to just 34%, from 56%, in a single year, even as many tax departments continued investing in tools.

    • Training cutbacks are accelerating, as the AI era deepens 鈥� Only 50% of tax departments provided technology training in 2025, down from 59% the prior year.

    • Hiring changes reveal a false choice between tax expertise and tech fluency 鈥� After years of prioritizing tech and IT hires (which were 57% of new roles in 2024), tax departments sharply reversed course, with 62% of new hires in 2025 emphasizing tax expertise over technology skills.

After years of investing in tax technology, corporate tax departments have yet to see peak efficiency because of underdevelopment in workforce training and a growing mismatch between what advanced AI tools can do and what employees can handle. In fact, the , from the 成人VR视频 Institute and Tax Executives Institute, reveals that satisfaction with tax technology has plummeted to just 34%, from 56%, in a single year.

That leaves many tax departments struggle with a widening frustration gap between what they want to achieve and what their current tools will allow. The key to closing this chasm is for heads of tax to reinvest in the human-side of their technology capabilities.

Tech competency remains a challenge

Only 9% of tax professionals rate their colleagues as very competent with technology, according to the report. The majority (60%) said they consider their teams merely somewhat competent, while nearly one-third admit their departments lack technological competence altogether.

What makes this especially alarming is that larger companies with more resources are almost three times more likely to report competency gaps, with 39% of professionals saying this, compared to just 15% at smaller firms. Indeed, these larger organizations are the ones that have invested most heavily in sophisticated tech stacks and should theoretically have the most capable users.

Perhaps a reason for this competence gap is the failure to invest in consistent technology training and knowledge-sharing among peers. Despite being one of the most cost-effective performance levers available, only 50% of corporate tax professionals surveyed said their departments provided technology training in 2025. This is down from 59% the previous year.

This training deficit has consequences because most corporate tax departments remain stuck in the reactive or chaotic phase of technological maturity. Meanwhile, AI timelines are compressing rapidly. In fact, 39% of tax professionals said they now expect AI to be central to their workflow within 1 to 2 years, up from the 31% who thought it would take that long just last year.

Pendulum swing in hiring

The 2026 Corporate Tax Technology Report also reveals a dramatic reversal in hiring priorities that deserves careful attention. In 2024, 57% of new tax department roles were dedicated to tech/IT expertise, with only 24% prioritizing tax knowledge. By 2025, the script had completely flipped, with 62% of new hires now emphasizing tax expertise.

At smaller companies (those with revenue of less than $1 billion), the swing is even more extreme. In fact, 100% of new hires are now those with tax expertise rather than technology specialists.

This pendulum swing likely reflects a correction after years of heavy tech/IT hiring combined with greater technological maturity that subsequently requires less technology expertise. At the same time, however, the solution is not one or the other; rather, hiring for both makes the most sense. In fact, the data supports this as hybrid tax/tech roles are on the rise, according to the report.

4 actions corporate tax leaders should take now

While the data makes the problem of this frustration gap clear, the more pressing issue is what tax leaders can do about it right now. Four concrete actions stand out:

1. Make training non-negotiable 鈥� If corporate tax leaders are investing in technology but not in developing their people’s ability to use it effectively, they are wasting money. Make formal training 鈥� along with mentoring and peer knowledge-sharing 鈥� a performance requirement.

2. Hire for the future 鈥� The pendulum swing back to tax expertise is understandable, but it鈥檚 essential that heads of corporate tax departments do not overcorrect. Prioritize candidates who demonstrate both deep tax knowledge and technological fluency or invest in upskilling current staff with explicit development paths to build in the missing capability.

3. Track what matters 鈥� Two-thirds of tax departments now measure time savings and efficiency gains, while 55% track accuracy improvements. In addition, it is important to track where your corporate tax department staff are struggling with tools and where additional training or process optimization could unlock value.

4. Prepare for the AI acceleration 鈥� With 39% of corporate tax professionals expecting AI to be central to their work within the next 1 to 2 years and another 15% expecting it within a year, corporate tax executives must start experimenting with AI for technical research, compliance automation, and document analysis to build the team’s comfort and competency through hands-on experience.

The bottom line

The frustration gap among corporate tax professionals highlights the mismatch between advanced technological capability and the human capacity to leverage it. As one survey respondent described: “Technology is extremely important to reduce manual processes and help reduce errors. I don’t see a path for any tax department to not lean into technology.鈥�

However, leaning into technology without investing equally in your people is a recipe for disillusionment. The 56% dissatisfaction rate with current tech stacks underscores the frustration in the human-technology relationship and the perception that the technology tools are not solving users鈥� problems very well.

Those corporate tax departments that will thrive in the AI era will be the ones that invested in building technological competence, hired for hybrid capabilities, and created cultures of continuous learning. The technology maturity curve and the talent maturity curve must ascend together.

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

Key insights:

• • • Conflict of interest doesn’t start with bad intent 鈥� Often, conflict of interest starts with tenure, trust, and relationships that slowly blur the line between good judgment and personal interest.

    • The real exposure isn’t the fraud itself 鈥� The real damage from conflict of interest can be years of skewed vendor decisions, above-market pricing, and lost competitive ground.

    • Companies shouldn鈥檛 treat conflict of interest as a disclosure problem 鈥� Companies would do well to remember that often conflict of interest is really a data and systems problem.

His access logs were clean, so it took weeks to find out what actually happened. He had been borrowing colleagues’ IT logins, who had handed them over without much thought, even though they knew it broke policy. They just didn’t think it mattered. He used those logins to steer million-dollar contracts to selected vendors who were paying him kickbacks.

The company鈥檚 conflict of interest policy existed, and people had signed it. Yet, nobody checked whether anyone followed it. And this scheme wasn’t even caught internally. Fortunately, someone outside found it.

This gap between knowing something is wrong and believing it matters 鈥� that鈥檚 where conflict of interest lives.

The financial exposure goes well beyond the kickback itself

The kickback that was paid to an insider is not the real cost to the company. The real cost is what happens while nobody is looking. As a result of this fraud, this company didn鈥檛 even know they were experiencing years of sourcing decisions that were shaped by hidden interests, vendors who never got a fair shot, and pricing that stayed above market price because the person managing the relationship had a reason to keep it there.

Throughout many industries, the numbers back this up. The from the Association of Certified Fraud Examiners (ACFE) found corruption in almost half (48%) of all fraud cases. Median loss for corruption schemes was around $200,000, and the average scheme run for about 12 months before anyone catches on. Not surprisingly, 87% of conflict-of-interest fraud perpetrators had no prior criminal record. Indeed, they were trusted employees, not career criminals.

What makes this worse is that most organizations have no reliable way to catch it. Across industry guidance, compliance publications, and professional forums, a consistent picture emerges: The majority of organizations rely entirely on disclosure forms and self-reporting to manage conflicts of interest. Leading compliance expert, Rebecca Walker has publicly admitted that 鈥� and even though the tools exist, almost nobody is using them.

The statistics, however, only capture what gets caught. The psychology of how it starts is harder to measure 鈥� and more important to understand. Conflict of interest rarely begins with a plan to steal. Rather, it starts with tenure, trust, and relationships that make someone hard to replace. Over time, the line between good judgment and personal interest doesn’t get crossed, it just disappears.

Taking a more structured approach

Most companies rely on disclosure forms, ethics training, and a code of conduct. They want to tell people what a conflict looks like, ask them to report it, and assume they will. Too often, they won’t.

Disclosure forms ask employees to self-report behavior they often don’t recognize as problematic, and those who do recognize it worry they’ll be investigated or treated unfairly themselves. They’ve watched junior staff held to strict standards while senior leaders get a pass. Unfortunately, that teaches everyone the same lesson: Stay quiet. When 85% of companies with a code of conduct still have fraud at this scale, the problem is not what people know, rather it鈥檚 how the program is built.

These failures point to three specific gaps in how most organizations approach conflict of interest: i) how they gather information; ii) how they monitor risk; and iii) how they receive reports. A structured framework 鈥� one based on concepts of design, detect, and deploy 鈥� can address each one of these gaps directly, with each component being measurable in financial terms.

Design: Are you collecting facts or asking people to confess?

Take a look at how you approach employees around conflict-of-interest issues. Are you seeking information or just generally hoping the employee admits wrongdoing, even inadvertently. A better approach could be to ask specific questions: How long has the employee worked with this vendor? Can the employee award contracts to them? Does the employee have any ownership stake in a company on the approved vendor list?

Let the employee give the facts and then let the system make the call. When you separate sharing information from being judged for it, people actually share and you get better data. And better data means better procurement decisions. That is not a compliance win 鈥� that鈥檚 a business win.

Detect: Are you looking for conflicts or hoping someone speaks up?

Run your vendor list against your employee records and flag matching addresses, phone numbers, and bank accounts. Check public registries for shared directors between your staff and your suppliers. Look at who has been awarding contracts in the same role for years without rotating, and managers who keep hiring from former employers.

Any company with an ERP system and an HR database can run these checks quarterly. And ACFE data underscores the value in taking the proactive approach: On average, companies using automated transaction monitoring catch fraud within six months and lose about $83,000; and companies that wait for law enforcement to alert them to the fraud take 24 months and lose $675,000.

Deploy: Is your hotline a business tool or a poster on a wall?

Tips catch 43% of all fraud 鈥� more than audits, management reviews, and law enforcement combined. Companies with hotlines lose $100,000 in median fraud; but companies without them lose $200,000. A working tips hotline can cut your losses in half.

However, most hotlines are not functioning as intended. They exist on paper without the visibility, trust, or independence required to generate reliable reports. For example, a senior executive was steering contracts to his own associates. And even though a company hotline existed, the executive actually sat on the committee that received the reports. The tool was built to catch misconduct and was working properly, yet it was controlled by the person committing the fraud. The matter had to be escalated outside normal channels, and the senior executive was eventually fired for cause.

Almost half (46%) of employees who report misconduct face retaliation, according to the , from the nonprofit Ethics and Compliance Initiative. When that is the outcome, silence becomes the rational choice. If you want your hotline to work, promote it every quarter. Show people what was reported and what happened because of it. Make sure no single person can block or read a report before it reaches the right people. Being that proactive around your hotline will give employees proof that the system protects them.

Is it worth the investment?

Of course, the question is not whether your company has a conflict-of-interest policy, it most likely does. Rather, the question is whether you would know if someone were breaking it right now.

Companies that design better fact-gathering, detect through monitoring, and deploy trusted reporting can do more than catch fraud early. They can buy from better vendors, compete on fairer pricing, protect their board from liability, and build a culture in which raising a red flag is seen as protecting the business.

If the honest answer is that you would not know if someone was violating your company鈥檚 conflict of interest policy, then business case for being more proactive has already been made.

You can find more about how companies can best manage business fraud here

Key takeaways:

• • • Prompt engineering pays off 鈥� Tax professionals who master clear, contextualized AI instructions see immediate gains in output quality and speed.

    • AI doesn鈥檛 replace professional responsibility 鈥� Every output that carries your name requires your verification and your judgment.

    • Link learning to a real problem 鈥� The most effective way to build needed skills is to focus on your current workflow, not to chase every new tool as it emerges.

For tax professionals, technical excellence used to be enough. Know the code, understand the cases, apply the rules correctly 鈥� that was the job, and it was sufficient. It isn’t anymore. Not because the technical knowledge matters less, but because the professionals competing for the same work increasingly bring other talents to the table, such as the ability to do in an hour what used to take a day; to provide insights from data that would have taken a week to compile manually; and to deliver polished, well-reasoned analysis at a pace that wasn’t possible five years ago.

This rarified capability doesn’t come from intelligence or experience alone; rather, it comes from skills 鈥� specific, learnable, practical skills.

The data bears this out. Improving efficiency through technology has been the top strategic priority for firms for three consecutive years, with 44% of firm leaders citing it as their primary focus, according to the 成人VR视频 Institute鈥檚 . Indeed, 47% of tax professionals surveyed said investing in AI should now be a top priority 鈥� and yet, 18% of firms still use no automation at all.

This gap between intention and capability is real, and it sits squarely with the individual tax professional.

The skills most needed by today鈥檚 tax professionals

To help close this gap and improve tax professionals鈥� overall work value, there are several specific skills that demand attention, including:

Prompt engineering: The skill nobody takes seriously until they see what it does

The name doesn’t help 鈥� but set that aside, because the underlying skill is straightforward: giving your AI tools clear, precise, well-contextualized instructions that produce outputs that are worth using.

Most people start badly when approaching a blank AI screen. They type something vague, get something generic, and conclude the tool isn’t useful. That conclusion is wrong, because it was the instructions given, the prompt, that was the problem. Specify the entity type, jurisdiction, tax year, audience, and format. Then tell the tool what you need and why. The difference in output quality is not marginal.

Of course, it鈥檚 important to remember that AI will tell you things that are wrong with complete confidence. It will cite an amended provision, apply a rule from the wrong jurisdiction, or construct a plausible analysis on a flawed premise 鈥� all without flagging any of it. The professional responsibility to catch it remains entirely upon the user. That’s not a flaw in the tool; it’s a reminder that expertise isn’t being replaced here 鈥� it’s being put to better use.

Data literacy: The capability gap most tax professionals don’t know they have

Tax work is data work. Today, what has changed is the expectations around the volume and complexity that professionals are now required to handle, interpret, and present, often with fewer resources than a decade ago.

Advanced spreadsheet proficiency is the starting point, and the emphasis on advanced is deliberate. The features that most professionals have never explored are precisely the ones that separate those who spend three hours processing data from those who spend 20 minutes. The ability to build visual dashboards that communicate tax data clearly 鈥� effective tax rates, provision variances, deferred movements, and more 鈥� is increasingly an expectation in corporate environments rather than a differentiator. For those professionals who handle large datasets or complex scenario modeling, even a foundational understanding of represents a significant capability uplift.

The Tax Professionals Report found that 57% of firm leaders cited getting better use out of existing technology as their top investment priority 鈥� more than those planning to buy new systems. The problem, in other words, isn’t the tools; it鈥檚 having the skills and the understanding to use them.

Workflow automation: Reclaiming time from work that shouldn’t exist

Look at any tax workflow closely and you’ll find steps that are repetitive, rule-based, and time-consuming 鈥� not because they require a tax professional鈥檚 skilled judgment, but because nobody has stopped to ask whether these routine tasks could be done differently.

Again, the harder part of improving your skill set as a tax professional isn’t learning the tools; rather, it’s developing the habit of process analysis, a way of thinking that will allow you (among other things) to distinguish between steps that require genuine expertise and steps that are simply consuming time.

AI judgment: Knowing what to trust and what to verify

This is the skill that determines whether AI makes you more effective or creates problems you didn’t anticipate. This means validating outputs against primary sources before they reach a client. It means recognizing that AI reflects training data that may be outdated or jurisdiction-specific in ways that aren’t readily apparent in the output. And it means knowing when a task is too nuanced or too high stakes for AI to handle reliably.

Professional responsibility does not transfer to the tool itself. If an AI-generated analysis carries your name, it is your analysis.

Communicating and staying current

As routine tax compliance work becomes more automated, the premium on communication rises sharply. The Tax Professionals Report found that three-quarters of clients now strongly desire advisory services beyond tax preparation from their outside tax professional 鈥� yet most tax firms still derive their greatest profits from simple tax return preparation.

Those professionals who can close that gap are those who can translate technical work into clear, confident guidance that their clients can act on.

Going forward, the tools will keep changing. Identify the problem in your current workflow that costs the most time, find the skill that addresses it, and build from there. The professionals who will define the next decade will combine this deep technical knowledge with the ability to work faster, more clearly, and more adaptively than those who came before them. That combination is not yet common, but it鈥檚 also not out of reach.

For more on how tax professionals are navigating technological change, visit the or download the full 2025 State of Tax Professionals Report

Key highlights:

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

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

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

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

Planning around 3 law firm business models in the AI era

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

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

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

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

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

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

Aligning the talent model to the future business model

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

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

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

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

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

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

Boyko underscores a similar approach to skills. 鈥淩ight now, the skills needed to be a good lawyer are no longer those rote skills that AI can automate,鈥� she explains. 鈥淚nstead, they are the people skills, the operational skills, and the client skills.鈥�

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

You can learn more about the challenges facing legal talent 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 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