Key insights:

• • • The duty of competence now includes technology 鈥� Rules first written for legal knowledge and diligence are being read to cover the tools lawyers use, not just the arguments they make.

    • The rules didn’t anticipate AI, but they still apply 鈥� Formal opinions and updated guidance make clear that generative and agentic AI fall squarely within lawyers鈥� existing ethical obligations.

    • What reaches the court carries the highest stakes 鈥� A lawyer’s technology missteps are most consequential, and most visible, the moment they show up in a filing, an exhibit, or an argument made to a judge.

Every attorney holds a duty of competence, and that duty has always included legal knowledge and sound judgment. Today, however, it now extends to technology as well. Often called 鈥�technical competence鈥�, today鈥檚 duty of competence requires understanding the benefits and risks of the tools used in practice, not just the law itself.

The American Bar Association’s Model Rules of Professional Conduct describe an attorney’s duty of competence as the obligation to provide representation using the (Rule 1.1). Comment 8 of the rule states explicitly, in part: 鈥淭o maintain the requisite knowledge and skill, a lawyer should keep abreast of changes in the law and its practice, including the benefits and risks associated with relevant technology鈥� .鈥�

Indeed, competence matters well beyond a lawyer’s own file. Every filing, brief, and exhibit is, in effect, a lawyer’s communication to the court, and the court relies on the presumption that what it receives has been prepared with care. When a lawyer misunderstands or misuses technology, whether that means citing an AI hallucination as precedent or mishandling e-discovery output, the failure lands directly on the judge’s desk.

Rules of professional conduct are written broad and durable, so they do not need constant revision. Even so, few could have anticipated the pace of technological change now reaching the legal field.

A lawyer cannot simply plead ignorance when technology goes wrong, and failing to understand the tools being used can lead to ethics complaints, sanctions, or malpractice claims. Perhaps even more damaging, it can just as easily undermine a judge’s confidence in everything else that lawyer submits.

Rules built to last meet a technology no one predicted

Each state and territory maintains their , but most draw heavily on the ABA’s Model Rules as a foundation. Precedent is also shaped by disciplinary proceedings and case law, including a growing number of cases in which judges have sanctioned lawyers for submitting AI-generated content that was never checked for accuracy.

Rules of professional conduct are written broad and durable, so they do not need constant revision. Even so, few could have anticipated the pace of technological change now reaching the legal field. E-discovery was an early example, already raising real questions about what a lawyer could certify to a court in good faith. The more significant shift today is generative AI (GenAI) and agentic AI, capable of producing legal analysis or even taking action with minimal human oversight, often producing material that looks polished and authoritative right up until a judge or opposing counsel checks it.

Recognizing this shift, the ABA issued in July 2024 on the use of GenAI tools in legal practice, confirming that the duty of competence applies squarely to AI. The Formal Opinion requires lawyers to understand these tools well enough to use them responsibly, supervise their output, and avoid overreliance on unverified results 鈥� a standard that matters most the moment a document is filed, or an argument is made in open court.

5 ways to stay ahead of the curve

Given how quickly legal technology is evolving, and how little margin for error exists once something reaches a judge, there are five practical ways that lawyers can maintain their technical competence, including:

1. 1. 1. Pursue CLEs and structured education 鈥� Continuing legal education courses focused on AI, e-discovery, and cybersecurity offer a reliable, credentialed way to stay current. Many state bars now offer, and some require, CLE credits specifically on legal technology, exposing lawyers to real-world case studies that include those involving AI missteps that have drawn judicial sanctions.
      2. Build personal familiarity through hands-on use 鈥� Reading about a tool is no substitute for using it. Treating AI as a “thought partner,” for drafting, brainstorming, or spotting issues, helps professionals develop an intuitive feel for what these tools do well and in which ways they fall short, long before a document ever reaches a court. This low-stakes experimentation on matters that don鈥檛 impact client confidentiality or sensitive data can help build practical AI fluency.
      3. Practice in sandboxes and controlled environments 鈥� Before relying on a new platform in an active matter, test it in a sandbox first. Many vendors and firms now offer walled-off spaces to explore a tool’s features and failure points without exposing real client data.
      4. Review organizational guidelines 鈥� Firms, courts, and bar associations increasingly publish their own AI use policies that cover permitted tools, disclosure requirements, and data security. A growing number of courts also require lawyers to certify or disclose any AI use in filings, making familiarity with local rules as important as firm policy.
      5. Choose the right tools for the task 鈥� Free consumer-grade AI tools may suit general research, but should not be used in matters involving privileged or sensitive information, let alone a court filing. Premium and fiduciary-grade platforms, built with legal-specific safeguards around data handling and auditability, are better suited for substantive casework. Matching the tool to the sensitivity of the task, rather than convenience, is critical.

Used together, these five habits can help lawyers build a durable foundation in AI literacy. While technology will keep evolving, of course, any lawyer who has internalized this approach will be far less likely to be caught off guard once their work is tested in front of a judge.

Why this is about more than just the rules

Maintaining technical competence is not simply about avoiding disciplinary consequence. It is about preserving what the legal system is meant to provide. When lawyers understand the technology they use, including AI, they can apply it to research and review in ways that benefit clients and courts alike.

Just as importantly, technical competence protects the public’s trust in the legal profession, and something more immediate: a judge’s ability to rely on what lawyers put in front of them. In the end, technical competence is not a burden imposed by the rules; rather, it鈥檚 what allows the relationship between lawyers and the court, and the profession itself, to keep working.

For more on AI in the courts, check out the 听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI)

Key insights:

• • • Judges can lead by example in responsible AI adoption 鈥� By engaging thoughtfully with AI and using it to enhance (and not replace) human judgment, judges can set an important standard for accountability, verification, and ethical use across the legal system.

    • AI excels at streamlining preparation, not decision-making 鈥� AI is most valuable for reducing the burden of preparatory tasks, such as summarizing briefs, organizing records, identifying questions, and clarifying technical or statutory language. It should not substitute for judicial reasoning, discretion, or decision-making.

    • Verification and guardrails are non-negotiable 鈥� Responsible AI use requires vetted tools, strong privacy protections, precise prompting, and independent verification of all AI-generated content. The integrity of judicial work depends on maintaining these disciplined processes.

There is a leadership opportunity sitting inside every courtroom in America, and most judges haven’t claimed it yet.

AI is already shaping the legal system, in how attorneys research, how clerks draft, and how litigants prepare their cases. In , the latest webinar from the听听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥斕鼵hief Justice Matthew Fader of Maryland observed that the 鈥渢echnology is just becoming ubiquitous.鈥�

鈥淚t’s spreading faster than any technology I’ve encountered in my lifetime,鈥� Chief Justice Fader said. The reality, of course, is that AI will influence a wide spectrum of legal processes, including how judicial work gets done. The question is whether judges will be passive observers of that shift, or active, thoughtful participants in it.

The advantages in engaging with AI extend beyond efficiency 鈥� it is also about leadership. Judges who use AI responsibly, with clear guardrails, careful verification, and a firm commitment to human judgment, can model the standard that the legal profession needs from the bench.

The preparation problem AI can actually solve

The bulk of the work for a successful hearing or trial is done long before the judge enters the courtroom. This happens through careful review of records, briefs, and complex subject matter. Yet time constraints often limit thorough preparation, and this is where AI adds real value.

When used appropriately, AI can streamline early-stage work without compromising judicial rigor. Judges can upload briefs and receive structured summaries of each party’s arguments, providing a clear roadmap before deep analysis begins. Transcripts can be searched instantly for specific testimony, and lengthy records can be organized by topic, which reduces manual review.

For more technical cases, AI can generate concise primers on unfamiliar subjects, helping judges engage expert testimony within the proper context. AI also can draft targeted oral argument questions, flag hallucinated or incorrect citations in briefs, identify factual inconsistencies, and create procedural checklists for motions or default judgments. It can even simplify complex statutory language to enhance the clarity of proposed jury instructions.

As Justice Linda Kevins of New York observed, the more creatively AI is applied, the more use cases emerge. Judges who begin with one small task often discover a range of new efficiencies, which transforms preparation from a burden to a strategic advantage.

Responsible use is the point

What separates a judge using AI as a thoughtful professional from one using it carelessly is the discipline built around it. And that starts before the first prompt.

Any tool a judge uses should be vetted by the court’s IT department, its administrative office, or both. The terms of service matter, such as whether prompts are retained, whether data is used for model training, whether confidential information could be exposed. These are not technical details to delegate, rather they are the minimum a judge should understand before uploading anything.

Prompting with intention matters too. Instructing AI tools to always cite its sources, to flag uncertainty, and to present arguments neutrally are habits that will produce better output and reduce the risk of erroneous data. Asking for authority behind every claim, and explicitly telling the tool not to guess, should become standard practice. The right questions to ask before any task, as Dr. Maura Grossman of the University of Waterloo, described it, are: “What are you trying to accomplish? And what’s the best tool for that?”

And verification is non-negotiable. AI tools can confuse a dissent with a majority holding, misstate what a lower court decided, or misquote language.

One simple rule, offered by Justice Kevin summed it up: “Whatever it gives me, I have to verify.” Every case that AI identifies, every citation it provides, every factual summary it generates must be checked against primary sources.

This is not a burden that undermines the value of AI; indeed, it is the practice that makes AI valuable.

What AI cannot do

In AI use in courts, the boundaries matters as much as the capability of the tools.

“This is not a truth-finding tool,鈥� explained Chief Justice Fader. 鈥淭his is a truth-agnostic, predictive tool.” AI generates statistically likely output based on patterns in its training data. It does not reason, weigh competing values, or sense that something in a case doesn’t add up. It has no judgment, no empathy, no moral compass, and no capacity to recognize when the law should evolve because justice requires it.

Judicial discretion is not an inefficiency AI can optimize away 鈥� and this is the point. As Justice Kevins said in the webinar, AI is “only an assistant, an extra assistant.” The analysis of how the law applies to a specific set of facts, before specific parties, in a specific moment 鈥� that is unavoidably human work. The moment any judge asks AI to assess which party has the stronger argument, a vital task has been delegated to the AI that should not have been.

Leading from the bench

Judges who engage with AI carefully set a visible standard for everyone who appears before them. These judges are better equipped to recognize when a filing鈥檚 AI-generated content is inaccurate. They can set informed expectations for clerks and staff, and they can ask the right questions when AI-related issues surface in litigation 鈥� many of which are already surfacing regularly.

The goal for judges is to use AI 鈥渢o do their work more productively, but not to replace their judgment,鈥� said Chief Justice Fader. And that balance should guide responsible judicial use: Neither avoidance, which is increasingly unrealistic; nor uncritical adoption, which carries real risk 鈥� but instead a more disciplined approach than either of these extremes.

The prepared judge is not simply the one who has read everything before walking into a courtroom. It’s the one who has used every available tool wisely, verified rigorously, and exercised the human judgment that no algorithm can replicate. That standard, consistently applied, is a form of leadership for which the legal profession has been waiting.

For more on the impact of AI in courts, visit the听

Key findings:

• • • Law students understand risks and opportunities of AI use 鈥� Almost three-quarters (72%) of students surveyed say they see AI literacy as essential, while an even larger portion (74%) say they also recognize the risks of over-reliance.

    • Student AI adoption is already widespread 鈥� Almost 6 in 10 law students use AI several times per week for academic work, but much of this learning is happening through self-education rather than structured teaching.

    • AI guidance in law schools remains inconsistent 鈥� Close to a majority (48%) of students report that AI policies vary by professor, and almost one-third (32%) say that their schools do not give them the AI skills needed for their future career.

There is a significant and growing divide between how law students understand artificial intelligence and how legal institutions, such as law schools, are responding to it, according to a new 成人VR视频 Institute white paper.

Jump to 鈫�

2026 Law Student Pulse Survey

The 2026 Law Student Pulse Survey, based on responses from more than 1,800 law students that were collected in April 2026, challenges two assumptions that have long dominated institutional thinking. The first is that students are reckless adopters who use AI to bypass the hard cognitive work of legal education. The second is that students are passive and uninformed consumers of a technology they do not fully grasp. The data shows that neither characterization is accurate.

In reality, 72% of responding students identify AI literacy as an essential professional skill, while 74% simultaneously acknowledge that over-reliance on AI could undermine the development of their own core legal competencies. Holding both of these positions in tandem reflects a level of professional maturity that many institutions have yet to demonstrate in their own policies and curricula.

The survey also exposes a serious institutional gap. Nearly one-third of students report that their school does not provide the AI skills needed for their future legal careers. And nearly half indicate that AI policies vary by professor, leaving students without coherent and consistent institutional guidance on what responsible AI use actually looks like.

Far-reaching consequences

The consequences of this AI-understanding gap extend well beyond the classroom. Students are entering the workforce self-taught and inconsistently prepared, at a moment when legal employers are moving quickly to embed AI fluency into their hiring and development expectations. The profession is at risk of producing graduates who are sophisticated enough to recognize the stakes but underprepared to meet them.

The full white paper outlines specific, actionable recommendations for law schools, bar associations and accreditors, and legal employers to follow to better address this gap in AI understanding.

You can download

a full copy of the 成人VR视频 Institute’s “2026 Law Student Pulse Survey” by filling out the form below:

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 insights:

• • • Well-being is a professional necessity 鈥� Judges experience decision fatigue, emotional stress, and personal biases that can affect their rulings, making mental and physical well-being a judicial duty.

    • Community engagement builds better judgment 鈥� Staying connected to the communities they serve helps judges develop empathy, recognize bias, and deliver fairer decisions.

    • Diverse experience strengthens the judiciary 鈥� Varied backgrounds and ongoing education in areas like restorative justice make courts more responsive, inclusive, and publicly trusted.

Judges play a unique and essential role in society. They are tasked with interpreting the law, resolving disputes, and upholding justice 鈥� often under intense scrutiny and pressure. Their decisions shape lives, influence public policy, and reinforce the rule of law.

Indeed, judicial rulings may be the most visible part of the job, but they are not the only measure of a judge’s effectiveness 鈥� or of the judiciary’s overall health.

To truly understand and support a robust legal system, it is vital to look beyond the courtroom and examine the broader context in which judges operate. A judiciary that is fair, empathetic, and resilient depends not only on legal expertise, but also on balance, self-awareness, and active engagement with the communities it serves.

The weight of the robe & the value of connection

Despite the solemnity of the judicial office, judges also carry personal experiences, cognitive biases, and emotional responses. The weight of responsibility in adjudicating complex, often emotionally charged cases can lead to stress, burnout, and decision fatigue. that judicial decisions can be influenced by factors such as time of day, caseload volume, and even personal well-being.

When judges prioritize their own well-being through physical health, mental resilience, and time away from the bench, they are better equipped to render fair and consistent decisions. Judicial wellness is not a personal luxury; rather, it is a professional imperative.

Equally important is the role of community engagement. The law does not exist in a vacuum but is shaped by social norms, economic realities, and cultural shifts. Judges who remain isolated from the communities that are affected by their rulings risk losing touch with the lived experiences of the people before them.

Judicial rulings may be the most visible part of the job, but they are not the only measure of a judge’s effectiveness 鈥� or of the judiciary’s overall health.

Engagement with the public helps judges better understand how the law impacts and operates in people’s lives. It also builds the empathy and contextual awareness needed for interpreting statutes or imposing sentences.

For example, a judge who volunteers with youth programs or participates in community forums on public safety may develop a more nuanced understanding of cases involving juvenile offenders or policing practices. Similarly, a judge who attends local cultural events or listens to community leaders may be better positioned to recognize implicit biases or systemic inequities that may be inherent in the justice system.

Community involvement also strengthens public trust. When citizens see judges as accessible and engaged, rather than distant or aloof, confidence in the judiciary increases. And these ideas of transparency and connection are key to maintaining citizens鈥� trust in the courts.

These themes are explored more in depth in the 成人VR视频 Institute鈥檚 video series,听Beyond the Bench. For example, in the episode听,听Associate Justice Tanya R. Kennedy shares her experience educating youth, participating in civic organizations, and leading legal reform initiatives. The episode also highlights how service beyond judicial duties enhances judges鈥� decision-making and strengthens community ties.

Another episode of the series,听,听examines the personal and professional challenges faced by judges and attorneys alike. It features a candid interview with Judge Mark Pfiffer, who emphasizes the importance of mindfulness, peer support, and institutional policies that promote mental health and sustainable work practices.

A judiciary that reflects society

The same principle applies at the institutional level. A judiciary is strongest when it reflects the range of experiences and perspectives present in the society it serves.

Beyond individual judges, the judiciary can benefit from diversity and inclusion. A bench that reflects the full spectrum of society is more likely to deliver balanced and equitable justice. But diversity is not just about representation 鈥� it鈥檚 also about perspective.

Judges who have worked in public defense, civil rights advocacy, or rural legal services bring different insights to the bench than those who have spent their careers in corporate law or prosecution. These varied experiences enrich judicial deliberation and help ensure that decisions are informed by a broad understanding of justice.

Encouraging judges and court personnel to engage in lifelong learning, mentorship, and cross-sector collaboration further strengthens the judiciary. Programs that support judicial education on topics like implicit bias, trauma-informed practices, or restorative justice are essential to modern, responsive courts.

Improving judges鈥� well-being

The quality of justice depends not only on what happens in the courtroom, of course, but on what happens outside of it. Judges who maintain personal balance, engage with their communities, and remain open to diverse perspectives are better equipped to serve the public good.

Legal professionals, court administrators, and policymakers should support the kinds of initiatives that promote judicial wellness, community outreach, and professional development. By fostering a judiciary that looks beyond the bench, we ensure a justice system that is not only legally sound, but also humane, inclusive, and trusted.

In the end, judges and the justice they mete out are not defined by court rulings alone. It also depends on relationships, context, and public trust. Recognizing that reality is essential to preserving the well-being of the judiciary and the integrity of the law.

The听鈥�Beyond the Bench鈥澨齰ideo series is available on

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:

• • • Universities face pressure over pedagogy鈥� Academic institutions are adopting AI as a reputational marker that鈥檚 driven by market pressure rather than educational need, creating a risk for students who can work with AI but not independently of it.

    • Entry-level roles under threat鈥� AI is being deployed most heavily to automate the grunt work of entry-level positions in which foundational professional skills are traditionally built through struggle and feedback.

    • K-shaped cognitive economy emerging鈥� Experienced professionals with existing expertise are gaining efficiency from AI, while entry-level workers are losing access to skill-building experiences.

According to Harvard University’s Professional & Executive Development division, innovation is defined as a 鈥減rocess that guides businesses through developing products or services that deliver value to customers in new and novel ways.鈥� Along this journey, professional judgement in decision-making is used numerous times to determine next steps at key stages.

Notably, the word technology is nowhere to be found in this definition 鈥� an absence , Assistant Professor of Learning Technologies at the University of Minnesota, has long found revealing. Instead, innovation is framed as creative problem-solving, contextual intelligence, and the ability to work across perspectives. Interestingly, Dr. Heinsfeld adds, none of these require constant automation. In fact, many of them are undermined by it.

However, AI adoption has the real potential to automate away the very experiences that build these capabilities from university lecture halls to corporate offices. With notable data already suggesting that , the risk that the current approaches to AI use in universities and companies are engineering away innovation and professional judgement skills is real, notes , Group Leader in AI Research at Harvard and NTT Research.

Indeed, some observers view AI as the largest unregulated cognitive engineering experiment in human history. Yet, unlike medical drugs that require years of approval and testing, AI systems are reshaping how millions of students think, learn, and make decisions without a comparable approval process or a shared framework for discussing any potential 鈥渟ide effects,鈥� as Dr. Heinsfeld pointed out.

Most worrisome is that AI is being deployed most heavily to automate precisely the entry-level roles where foundational professional skills are built.

So, what happens when an entire generation of future employees learn to delegate judgment before they develop it? And what actions do universities and companies need to take now to avoid this reality?

Risks of universities adopting AI under pressure

For universities, AI 鈥渉as become a reputational marker, and not adopting AI is framed as institutional risk, regardless of whether an educational case has been made or not,鈥� says Dr. Heinsfeld, adding that this is being driven, in part, by market pressure rather than pedagogical need.

Already, companies can greatly influence universities as employers of new graduates; and as such, AI systems are currently being optimized for speed, agreeability, and accessibility to stimulate ongoing use. However, as Dr. Heinsfeld contends, as universities race to earn the label AI ready without a careful, cautious and detailed understanding of how it may impact students鈥� cognitive processes, they run the risk of damage to their reputations of pedagogical integrity.

In addition, the “data as truth” paradigm is a complicating factor, she says. Drawing on her research, Dr. Heinsfeld explains how data 鈥渋s often framed as the idea of being a single source of truth based on the assumption that when collected and analyzed, it can reveal objective, indisputable facts about the world.鈥� Indeed, this ubiquitous mindset across universities and corporations treats data 鈥� such as that used to train large and small language models 鈥� as objective and indisputable.

Yet this obscures critical decisions about what gets measured, whose perspectives are included, and what forms of knowledge are systematically excluded from AI systems. As Dr. Heinsfeld warns, when data becomes synonymous with truth, “knowledge is what is measurable and optimizable.鈥� This narrows professional judgment to efficiency metrics rather than the interpretive depth, ethical reasoning, and cultural context that are essential for sound decision-making.

Judgment gap widens in workforce downstream

Under the current AI adoption approach, students could leave universities able to work听with听AI but not independently听of听it, a distinction emphasized by Dr. Heinsfeld. Like calculators, AI works as a tool only when foundational skills for its use exist first. Without this, graduates enter the workforce with a critical judgment gap that compounds from their lives as students at college campuses to becoming employees working in corporations.

AI adoption has the real potential to automate away the very experiences that build these capabilities from university lecture halls to corporate offices.

Most worrisome is that AI is being deployed most heavily to automate precisely the entry-level roles where foundational professional skills are built, warns Dr. Tanaka. Indeed, this is exactly the type of grunt work that teaches judgment through struggle and feedback. Over time, overuse of AI will result in quality being sacrificed because critical evaluation skills have atrophied.

Looking into the future, Dr. Tanaka foresees a K-shaped economy of cognitive capacity. Experienced professionals with existing expertise and contextual judgment built through years of experience will gain increasing efficiency from AI. Entry-level workers, however, will lose access to the valuable experiences that build professional judgement. This gap widens between professionals who can independently accelerate their workflows using AI and those whose traditional tasks are merely displaced by it.

Intervention may be able to break the cycle

The pattern is not inevitable, as both Dr. Tanaka and Dr. Heinsfeld explain. Drawing on Dr. Heinsfeld鈥檚 emphasis on institutional agency, meaningful intervention will depend on conscious, intentional choices made at every level. Both experts share their guidance for how different organizations can manage this:

Academic institutions 鈥� Universities must first recognize that AI adoption is a decision rather than an inevitability and make educational need the North Star for decision-making around AI. In her analysis, Dr. Heinsfeld emphasizes that when vendors set defaults, they quietly redefine academic practice. Defaults shape what is made visible or invisible and what becomes normalized. In AI-driven environments, universities often lose control over how models are trained and updated, what data shapes outputs, how knowledge is filtered and ranked, and how student and faculty data circulate beyond institutional boundaries 鈥� especially if decision-making is left to vendors. As a result, the intellectual byproducts of teaching and learning increasingly become inputs into external systems that universities do not govern.

Private entities 鈥� For organizations, Dr. Tanaka calls for feedback loops and other mechanisms that will promote more open discussion about AI use without stigma. In addition, companies need to proactively redesign entry-level roles听to ensure these positions continue to cultivate judgment and foundational skills in an AI-driven environment. Likewise, Dr. Tanaka suggests that companies explicitly provide feedback about cognitive trade-offs to employees, fostering an understanding of possible skill entrophy.

Employees 鈥� Similarly, individuals working for organizations bear much of the responsibility for making sure critical thinking is enhanced by AI. Indeed, strategic decisions about when to use AI while seeking to preserve cognitive capacity and professional judgement are key.

Looking ahead

In today鈥檚 increasingly AI-driven environment, a new paradigm is needed to combat the current operating assumption that optimization from AI is the sole path to progress. And because the current trajectory sacrifices human development for efficiency, the need for universities and companies to choose a different path is urgent 鈥� while they still have the judgment capacity to do so.

You can find out more about how organizations are managing their talent and training issues here

Key points:

• • • The productivity promise is largely wrong 鈥� Emerging research shows that AI doesn鈥檛 reduce work 鈥� it intensifies it. Lawyers work faster, take on broader responsibilities, and extend their hours without recognizing the expansion. Further, because prompting AI feels like chatting rather than laboring, lawyers slip work into evenings and weekends without registering it as additional effort.

    • Self-reinforcing acceleration is the real risk 鈥� AI speeds tasks, which raises expectations, which increases reliance, which expands scope, ultimately creating a cycle that drives burnout in a profession already plagued by it.

    • Purposeful integration is the antidote 鈥� Legal organizations need to promote intentional governance structures that account for how people actually behave with AI, not how leadership imagines they will or should.

Welcome back to The AI Law Professor. Last month, I examined how AI is forcing us to rethink training for junior lawyers. This month, I examine a question that affects every lawyer: What happens when the efficiency gains we’ve been promised don’t materialize the way we expected? A recent study out of UC-Berkeley suggests the answer is more troubling than most law firm leaders realize.

If you鈥檝e attended a legal technology conference anytime over the past two years, you鈥檝e heard the pitch: Automate the mundane and elevate the meaningful.

A in the Harvard Business Review by UC-Berkeley researchers Aruna Ranganathan and Xingqi Maggie Ye suggests we should be more skeptical. They tracked how generative AI (GenAI) changed work habits over eight months at a 200-person technology company. Their findings were striking 鈥� AI tools didn鈥檛 reduce work; rather, they intensified it.

According to the study, the tech employees studied were shown to work faster, take on broader responsibilities, extend their hours into evenings and weekends, and multitask more aggressively 鈥� all without being asked to do so. The promise of liberation became a reality of acceleration and overwork.

For those of us in the legal profession, this should be a wake-up call.

Three forms of intensification

The researchers identified three patterns that will sound familiar to anyone watching lawyers adopt GenAI in their work processes.

Task expansion

Because AI fills knowledge gaps, professionals stepped into responsibilities that previously belonged to others. Product managers started writing code, and researchers took on engineering tasks. In legal contexts, the parallel is obvious. Associates use AI to attempt tasks once reserved for senior lawyers. Paralegals draft documents that previously required attorney oversight. Solo practitioners take on matters outside their core expertise because their AI tools make it feel manageable. The result isn鈥檛 less work distributed more efficiently, it鈥檚 more work concentrated in fewer hands, with less institutional knowledge guiding the output.

Blurred boundaries

AI blurred the boundaries between work and non-work. Because prompting an AI feels more like chatting than labor, lawyers (like the tech workers in the study) may slip work into lunch breaks, evenings, and commutes without registering it as additional effort. The conversational interface is seductive precisely because it doesn鈥檛 feel like work. It is work, however, and much more of it.

Pervasive multitasking

Workers managed multiple AI threads simultaneously, generating a sense of momentum that masked increasing cognitive load. For lawyers, this means running parallel research queries, drafting multiple documents at once, and constantly monitoring AI outputs, all while believing they鈥檙e saving time.

The productivity trap

The most important insight from the research is that these effects are self-reinforcing. AI accelerates tasks, which raises expectations for speed. Higher speed increases reliance on AI, and greater reliance expands the scope of what people attempt. And expanded scope generates even more work. Rinse and repeat.

Parkinson鈥檚 law: 鈥淲ork expands to fill the time available for its completion.鈥�

In a profession already plagued by burnout, this cycle should alarm us. The legal industry鈥檚 adoption of AI is being driven largely by the promise of doing the same work in less time. But if the Berkeley research is any guide, what actually happens is that we do more work in the same amount of time, or more work in more time, while telling ourselves we鈥檙e being more productive.

And because the extra effort feels voluntary, firm leadership may not see the problem until it manifests as errors, attrition, or ethical lapses. In law, the cost of impaired judgment isn鈥檛 just a missed deadline 鈥� it鈥檚 a client鈥檚 liberty, livelihood, or life savings.

From productivity to purposeful practice

The Berkeley researchers propose what they call an AI practice consisting of intentional norms and routines that structure how AI is used, including determining when to stop and how work should and should not expand. I鈥檇 go further. For legal organizations, purposeful AI integration requires more than workplace wellness norms. It requires a strategic framework that aligns AI capabilities with organizational mission, ethical obligations, and sustainable human performance.

This means, first off, being honest about what AI actually does to workloads rather than what we hope it will do. If your firm adopted AI expecting to reduce associate hours, audit whether that has actually happened, or whether associates are simply filling reclaimed time with more work.

Second, it means building governance structures that account for how people actually behave with these tools, rather than how leadership imagines they will. The Berkeley study found that workers expanded their workloads voluntarily, without management direction. Top-down AI policies that focus solely on permissible use will miss the intensification that could be happening in plain sight.

The most important insight from the research is that these effects are self-reinforcing. AI accelerates tasks, which raises expectations for speed. Higher speed increases reliance on AI, and greater reliance expands the scope of what people attempt. And expanded scope generates even more work.

Third, it means preserving space for the distinctly human work that AI cannot replicate, such as judgment, empathy, ethical reasoning, and the kind of creative problem-solving that emerges from genuine human dialogue 鈥� not from a conversation with a chatbot. The researchers also found that AI-enabled work became increasingly solitary and continuous, a dangerous trajectory.

The narrative that AI will free lawyers for higher-value work isn鈥檛 just optimistic. It鈥檚 a misunderstanding of how these tools interact with human psychology. AI doesn鈥檛 create leisure. It creates capacity 鈥� and without intentional structures, that capacity gets filled, not with strategic thinking, but with more of everything.

While it鈥檚 clear that AI will change the legal profession, the real challenge is whether law firms will integrate AI with purpose, shaping it to serve their values, their clients, and their professionals鈥� well-being. Or, whether they鈥檒l be allowing the technology to quietly shape us into something we didn鈥檛 intend to become.

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.

You can find more about听the use of AI and GenAI in the legal industry听here

You can read TRI鈥檚 latest feature,听The human side of AI: The growing risks of ubiquitous use of AI on talent here

It鈥檚 no exaggeration to say that AI is everywhere in our workplaces right now. It writes our emails, summarizes our meetings, generates slides, and even helps us think through problems. On the surface, this may sound like progress 鈥� and in many ways, it is.

However, our , The human side of AI: The growing risks of ubiquitous use of AI on talent by Natalie Runyon, Content Strategist for Sustainability and Human Rights Crimes for the 成人VR视频 Institute, makes a clear and timely point: When AI use becomes excessive and unchecked, it can quietly undermine the very people it鈥檚 meant to help.

One major consequence of cognitive decay is the weakening of the brain鈥檚 capacity to engage deeply, question systematically, and 鈥� somewhat ironically 鈥� resist the potential manipulation of AI.

As the article goes into in much greater detail, these harms caused by AI overuse can include a slow erosion of human connections, a loss of a professional鈥檚 sense of purpose, and a general sense of feeling overwhelmed in the workplace.

Of course, the solution isn鈥檛 to reject AI, it鈥檚 to use it better. To this end, the article makes a strong case for organizations to foster hybrid intelligence, a process by which human judgment and creativity work alongside AI capabilities.

In today鈥檚 workplace, AI can be a powerful advantage; however, that is only if organizational leaders can remember that technology should enhance the human experience, not replaces the parts of professional life that workers value.

To examine this and many more situations, the 成人VR视频 Institute (TRI) has launched that leverages our expert analysis and supporting data to tell some of the most compelling stories professional services today

Key findings:

• • • AI adoption accelerates across professional services听鈥� Organization-wide use of AI in professional services almost doubled to 40% in 2026, with most individual professionals now using GenAI tools, and many preparing for the next wave of tools such as agentic AI.

    • Strategic integration and measurement lag behind usage 鈥� While AI use is widespread, only 18% of respondents say their organization tracks ROI of AI tools, and even fewer measure AI’s impact on broader business goals such as client satisfaction or revenue generation.

    • Communication around AI use remains inconsistent听鈥� While most corporate departments want their outside firms to use AI on client matters, less than one-third are aware whether their firms are doing so. Meanwhile, firms report receiving conflicting instructions from clients about AI use, highlighting a need for clearer dialogue and shared strategy around AI adoption.

Over the past several years, AI usage within professional services industries has come into focus. As we enter 2026 in earnest, the early adoption phase of generative AI (GenAI) has come and gone. Today, most professionals have experimented with some form of GenAI, and many organizations integrated GenAI into their workflows 鈥� and now, a number are preparing for the next wave of technological innovation such as agentic AI.

Given this, the question for professionals and organizational leaders has now become: What will be AI鈥檚 long-term impact on my business?

Jump to 鈫�

2026 AI in Professional Services Report

To delve into this question further, the 成人VR视频 Institute has released its 2026 AI in Professional Services Report, which takes a broad view into the current usage and planning, sentiment towards, and business impact of AI for legal, tax & accounting, corporate functions, and government agencies. Taken from a survey of more than 1,500 respondents across 27 different countries, the report finds a professional services world that has embraced AI鈥檚 use but is continuing to evolve business strategy around its implementation.

For instance, the report shows that to 40% in 2026, compared to 22% in 2025 鈥� and for the first time, a majority of individual professionals reported using publicly-available tools such as ChatGPT. Additionally, a majority of respondents said they feel either excited or hopeful for GenAI鈥檚 prospects in their respective industries, and about two-thirds said they felt GenAI should be applied to their work in some manner.

At the same time, however, many are exploring GenAI tools without much guidance as to how that use will be quantified or measured. Only 18% of respondents said they knew their organization was tracking return-on-investment (ROI) of AI tools in some manner, roughly the same proportion as last year. And even among those tracking AI metrics, most are tracking mainly internally-focused, operational metrics; and only a small proportion analyzed AI鈥檚 impact on their organization鈥檚 larger business goals 鈥� such as client satisfaction, external revenue generation, and new business won.

This slow move to strategic thinking also impacts client-firm relationships. Although more than half of both corporate legal departments and corporate tax departments want their outside firms to use AI on client matters, less than one-third said they were aware whether their firms were doing so or not. From the firm standpoint, meanwhile, confusion reigns: 40% of firm respondents said they have received orders both to use AI on matters and not to use AI on matters from various clients.

Indeed, bout three-quarters of corporate respondents and firm respondents agreed that firms should be taking the lead in starting these conversations around proper AI use. Yet these discussions have not yet happened en masse. 鈥淔irms are reluctant 鈥� they claim it would compromise quality and fidelity,鈥� said one U.S.-based corporate chief legal officer. 鈥淚 think they are threatened by it.鈥�

All the while, technological innovation progresses ever quicker. This year鈥檚 version of the report measures agentic AI use for the first time, finding that already 15% of organizations have adopted some type of agentic AI tool. Perhaps more interesting, however, is that an additional 53% report their organizations are either actively planning for agentic AI tools or are considering whether to use them, indicating perhaps an even more rapid pace of adoption than we鈥檝e already seen with the speedy rise of GenAI.

Overall, the report makes it clear that most professionals do understand that change, driven by AI in the workplace, is undoubtedly here. Even compared with 2025, a higher proportion of professionals said they believe that AI will have a major impact on jobs, billing and revenue, and even the need for legal or tax & accounting professionals as a whole. The percentage of lawyers calling AI a major threat to the unauthorized practice of law rose to 50% in 2026 from 36% in 2025.

Further, this report paints the picture of a professional services world that has embraced AI, begun to see its impact, and realized that it will have broader business and industry implications than previously imagined. As a result, the time for professionals and organizations to begin planning in earnest for an AI future has already arrived.

As a corporate general counsel from Sweden noted: 鈥淲e cannot keep up with the modern-day corporations鈥� demands unless we also develop and adapt our way of working.鈥�

You can download

a full copy of the 成人VR视频 Institute’s 2026 AI in Professional Services Report听here