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 highlights:

• • • Employer-driven curriculum design 鈥� The director of the accounting program at The University of Central Florida (UCF) consulted with 16 leading accounting practitioners to learn what AI skills employers need today from graduates.

    • Critical thinking milestones for students 鈥� UCF developed an AI competency framework delivered through a fictional theme park case study that was woven across tax, cost accounting, and financial accounting coursework.

    • Human skills as a competitive advantage 鈥� Recognizing that AI cannot replicate certain capabilities, UCF鈥檚 Dixon School introduced six to eight professional skills workshops (per academic year) covering topics such as relationship management, project management, agentic AI, and critical thinking to ensure that graduates bring irreplaceable human value to the profession.

The accounting profession is at an inflection point as the level of generative AI (GenAI) already in the workflow continues to reshape how audits are conducted, data is analyzed, and decisions are made. Already, one-third of tax firms are already using GenAI in their work, with 14% specifically using听, according to the 成人VR视频 Institute鈥檚 recent听2026 AI in Professional Services Report.

For accounting educators, the question is becoming how fast and how boldly are they responding to marketplace needs by embedding advanced AI technology into their curriculum?

Building a curriculum the marketplace needs

Most schools benchmark themselves against peer schools, but , Director of the Dixon School of Accounting at the University of Central Florida (UCF) is starting with practitioners and employers to uncover what curriculum innovation is necessary to prepare his future graduates. In fact, when Dr. Thibodeau arrived at UCF two years ago, he made a deliberate decision to be innovative in his approach to inserting GenAI into the curriculum.

Instead of starting with the faculty, however, he consulted with employers who are hiring his graduates. Over a series of a few months, he led a team of 15 professors and lecturers who leveraged formal research interviews with 16 accounting practitioners, who were responsible for recruiting and hiring for their organizations. Dr. Thibodeau鈥檚 goal was to understand what employers are demanding for new accounting graduates.

What he heard led to creative changes within the curriculum. Through this consultation process, he learned employers need undergraduates in accounting who can think critically听through听AI output, interrogate it, challenge it, and ultimately exercise independent professional judgment about it.

From those conversations, Dr. Thibodeau and his team developed an AI competency framework which includes a “critical thinking milestone staircase” approach to measure progressive levels.

For example, Dr. Thibodeau says that one of the milestones is effective prompting. While this skill is a table-stakes capability, it is the foundation to learning how to query AI purposefully to get useful output.

Next 鈥� and perhaps the most critical skill 鈥� is the ability to transition from AI user to AI evaluator. At this stage, students learn to interrogate AI output, cross-reference it against authoritative sources, and recognize when a fluent-sounding answer is wrong. As Thibodeau notes, hallucinations are becoming less frequent as GenAI technology improves, but the risks of uncritically accepting AI output in a professional setting remain unacceptably high.

The next milestone is strategic GenAI problem-solving, which involves knowing what AI tools are best for which specific tasks and how to deploy GenAI within a larger professional workflow. “What’s going to give them the expertise to be that exceptional human-in-the-loop is to operate independently of the GenAI tool,” Dr. Thibodeau adds.

The delivery vehicle for this framework is a comprehensive case study that was built around a fictional theme park that spans across tax, cost accounting, and financial accounting coursework.

Building faculty support for curriculum innovation

The project required assembling a team of faculty members who were motivated by Dr. Thibideau鈥檚 vision to both insert innovation within the curriculum while offering meaningful impact and scholarship opportunities for his colleagues. To gain buy-in from his colleagues, Dr. Thibodeau emphasized their legacy with the chance to demonstrate with personal satisfaction that this creative approach will prepare students for the accounting profession鈥檚 future.

For faculty with scholarship requirements, he emphasized that this project could produce publications in top educational journals. Now, that vision is paying off. Of the five papers his colleagues produced, one has been accepted and the others are in various stages of the review process at the Journal of Accounting Education. There also will be six presentations from colleagues at the American Accounting Association’s Global Connect meeting this summer. As a byproduct, every faculty member involved also immersed themselves and improved their own skills in GenAI along the way.

Lessons for accounting programs

Dr. Thibodeau is candid about the current difficulty in assessing growth in critical thinking skills. In fact, the accounting department at UCF is experimenting and learning as they go. For example, an early attempt to use AI to grade students’ qualitative reasoning responses did not work well, and the current approach of using outcome-based indicators as a placeholder is imperfect, he acknowledges.

In spite of this, Dr. Thibodeau offers strong guidance for other accounting professors, which includes:

Keep studying how to teach developmental skills and evaluate judgment 鈥� Dr. Thibideau knows that this is a universal challenge at the moment at every university and for every organization that depends on the apprenticeship model. The routine tasks that built junior-level judgment organically are increasingly being absorbed by AI, and no one has figured out with certainty how to replace that developmental experience.

Require a CPA pathway 鈥� To close the gap on the technical side, the Dixon School has embedded a CPA review course directly into the master鈥檚 in accounting curriculum across all semesters to ensure students graduate with both the technical depth needed to challenge AI output and a clear path to passing the CPA exam.

Offer opportunities to develop skills that AI cannot replicate 鈥� On the human skills side, the school now runs six to eight professional skills workshops, which include relationship management, project management, agentic AI, and critical thinking. Indeed, these human skills were deemed necessary by the research done by the Dixon鈥檚 schools accounting practitioners, chiefly because AI cannot replicate these skills.

For other accounting programs watching from the sidelines, Dr. Thibodeau’s model offers a clear lesson on how to remain relevant in the age of AI. Indeed, proximity to practice is not optional. The schools that will produce the most sought-after graduates in the AI era will be the ones engaged in continuous, structured dialogue with the employers who hire them.

Further, curriculum innovation at this pace requires an institutional culture change that treats change as an opportunity rather than a threat. The curriculum must improve with it because continuous evolution is a baseline requirement. The schools that understand this now will produce the professionals who can best shape the AI-enabled future of the accounting profession.

You can find out more about how tax firms are managing their AI technology here

Key insights:

• • • AI adoption has become an urgent business imperative 鈥� Enabling corporate functions are under pressure from leadership, business stakeholders, and employees to demonstrate tangible AI-driven value.

    • Slow AI adoption creates risk 鈥� Many professionals are frustrated by limited access to high-quality AI tools, which contributes to increased employee turnover and growing use of unauthorized shadow AI

    • Success depends on coordinated transformation 鈥� Organizations need a deliberate AI strategy rather than scattered experimentation to help guide responsible AI adoption across the organization.

Today, internal corporate enabling functions 鈥� such as legal, tax, global trade, compliance, and risk 鈥� find themselves at a crossroads as they face mounting pressures from three critical fronts: i) internal stakeholders that are demanding faster, more informed decisions; ii) finance departments that are expecting AI-driven efficiency and cost control; and iii) a professional workforce eager for tools that enhance the value of the work they do.

The message from the C-Suite is clear: AI must deliver tangible results now, according to the recent 成人VR视频听2026 Future of Professionals Report.

To help internal corporate function leaders manage this pressure and move forward with confidence into an AI-enabled future, 成人VR视频 has published a new action paper, Future of Professionals Report 2026: Actionable insights for corporate leaders, drawing on insights from hundreds of internal corporate professionals.

Facing down the triple pressures

The urgency that corporate function leaders are facing is underscored by those three areas of pressure. For example, almost half of professionals surveyed in enabling functions say they are either already experiencing the financial consequences of lagging AI adoption or are expecting to within a year. Many enabling functions have long been expected to absorb growing workloads without proportional increases in resources. Now, AI is increasingly viewed as a way to expand capacity and improve efficiency, making delaying its adoption a potential source of budgetary and competitive risk.

You can download your copy of the听2026 Future of Professionals Report听here

Stakeholder pressure is equally intense. As many business units accelerate their own AI deployments, they expect the organization鈥檚 other enabling functions to keep pace. If these functions become bottlenecks, they risk being sidelined or being perceived as obstacles rather than strategic partners. Indeed, more than half of corporate professionals say they are facing significant pressure from stakeholders to act faster on AI, with in-house legal teams feeling this most acutely.

Yet the pressure coming from the workforce may be the most alarming. The action paper shows that fully 30% of professionals say they are considering leaving their organizations within two years if the gap between the AI-driven value they expect and what is made available to them isn鈥檛 addressed. Access to professional-grade AI tools has become a key factor in job decisions, yet nearly 6-in-10 professionals say they lack access. This gap contributes to both retention challenges and the rise of unauthorized AI use, increasing compliance and governance risks.

Choosing the right path

Faced with the reality of these pressures, corporate function leaders must choose a strategic path for AI adoption. The action paper outlines three primary trajectories:

• • • Using AI to elevate by shifting human effort to high-value, judgment-based work.
    • Using AI to scale by leveraging AI to handle increased workloads without increasing headcount while optimizing for efficiency.
    • Using AI to reimagine by rebuilding workflows around AI鈥檚 capabilities, such as implementing shared data infrastructure and real-time dashboards.

However, knowing the path is not the same as walking it. The action paper also highlights a potential execution gap, in which a lack of coordination and shared accountability across functions derails any real progress. This is a particular problem for enabling corporate functions because many departments often operate in silos, using different AI tools and standards, which leads to fragmentation and operational bottlenecks.

The solution, as the paper outlines, lies in building a shared framework for AI governance and accountability, with fiduciary functions like legal, tax, and compliance taking the lead. Some critical recommendations outlined in the paper include advocating for professional-grade AI tools, planning for an evolutionary journey through AI adoption, and leading an organization-wide conversation about AI governance and standards.

Finally, the paper encourages corporate leadership teams to step back from daily pressures and engage in structured exercises to define a shared vision for AI within the organization. By developing a long-term roadmap that considers processes, data, technology, people, and risk, corporate leaders can ensure AI adoption delivers both immediate value and sustainable competitive advantage for the future.

You can read a full copy of the听Future of Professionals Report 2026: Actionable insights for corporate leaders paper here

Key highlights:

• • • Build on internal momentum rather than top-down mandates 鈥� Dean Kalb backed faculty who were already experimenting with AI, embedding shared learning outcomes into the legal writing program first before expanding to other courses.

    • Empower students to shape the school’s AI policy 鈥� Dean Kalb formed a 15-person student advisory group that surveyed one-third of the student body and produced AI principles that directly influenced school policy.

    • Create opportunities to get students collaborating with faculty 鈥� Efforts by Dean Kalb uncovered shared concerns of faculty and students, underscoring that students often know AI tools better than faculty and creating a co-learning opportunity in the classroom.

In her first six months as dean at the University of San Francisco (USF) School of Law, Johanna Kalb heard the same message from alumni across sectors: Those students entering law school today would step into a profession that looks meaningfully different from the one that exists now.

So, in her first move to translate that urgency into institutional change, Dean Kalb got behind those faculty members who had already started building toward that future.

Start with what is already in motion and invite others in

Dean Kalb started with the efforts that Profs. Nicole Phillips and Megan Hutchinson had already been doing by conducting their own experiments in their classrooms and building their own tools.

Dean Kalb鈥檚 first step mattered as a strategic choice. Rather than convening a task force or commissioning a study, she identified the faculty who had credibility with their peers and gave them resources and institutional backing. In this way, USF was able to embed shared AI learning outcomes across its legal research, writing, and analysis program in the second semester of the 2024-鈥�25 academic year.

The decision to focus on this program was deliberate because it built upon existing internal momentum and fit into the course鈥檚 existing goals. The structure of the legal research and writing program 鈥� with faculty having autonomy while supporting each other 鈥� also made the integration work by providing natural support.

Expand through optional workshops before adding mandates

Over the following summer in 2025, Profs. Phillips and Hutchinson ran optional hands-on workshops for the broader faculty. “Faculty learn from other faculty,” explains Dean Kalb. 鈥淭hey don’t want a vendor to come in and sell them. They’re not interested in having somebody from central administration try to tell them how they can teach better. But listening to a colleague who really understands the work that they do is very helpful.”

Indeed, some faculty showed up, were excited by the possibilities, and began integrating AI learning outcomes into their elective courses. This voluntary uptake created a visible proof of concept before any additional requirements or mandates were introduced.

Alongside the workshops, Dean Kalb also expanded AI learning outcomes into two required courses on evidence and professional responsibility. The professional responsibility inclusion was straightforward given the ethical dimensions of AI use in legal practice. One colleague, Prof. Tiffany Li, had already been building those outcomes into her section and was willing to share her approach with other faculty members who were teaching the same course.

Give students a formal role in shaping the direction

When USF rolled out access to the AI platform Claude across students and faculty, the response was more complicated than Dean Kalb anticipated. Feedback from students at USF 鈥� a Catholic Jesuit institution with a strong social justice identity 鈥� raised questions about AI鈥檚 social, environmental, and democratic impacts.

Dean Kalb intentionally chose to use the students鈥� feedback to involve them. With the help of another alum, who has deep experience in evaluating and implementing emerging technologies, Dean Kalb convened a student group to develop a set of draft principles for AI use at USF Law. The students conducted structured interviews with faculty, staff, and students, resulting in the creation of a survey in which approximately one-third of the student body participated. The student group drew on these results to draft a series of AI principles and presented them to faculty, staff, and other students. Ultimately, the principles were adopted by the faculty.

What came out of that process has already begun to shape the law school鈥檚 AI practices in concrete ways. For example, a faculty technology advisory committee with student representation has been formed to implement the principles to ensure transparency and ongoing oversight. The school also has begun exploring ways to engage with AI that reflect and enhance its social justice mission.

We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.

The more significant outcome, Dean Kalb says, was the discovery of shared concerns among faculty and students that AI would erode critical thinking rather than develop it. “That was probably the most helpful part of the whole process,” she says. “We now have a shared sense of where the community is and what our concerns are. That allows us to speak in a common language as we talk about why and how we’re doing this.”

Commit to sharing in the learning

Dean Kalb鈥檚 suggestion for her peers and faculty is to integrate AI tools into their own lives, which would allow them to better 听听听听听keep pace with technology that is moving faster than any curriculum committee can match. 鈥淚t鈥檚 hard to regulate and teach these tools in the abstract,鈥� she explains. 鈥淚鈥檝e found that playing around with them in my personal life 鈥� where the stakes are low 鈥� has helped me come up with ideas for their use at work, and that in turn, means that I notice their evolution.鈥�

For a profession built on expertise and the authority that comes with it, this mindset requires a particular kind of intellectual honesty. Some students are beginning to arrive at law school with more familiarity with AI tools than their professors, Dean Kalb adds, and this may offer an opportunity to shift the classroom dynamic toward co-creation, in which faculty and students are building knowledge together rather than transmitting it in one direction.

This change in perspective can, turn the stress of 鈥渒eeping up鈥� into the more enjoyable experience of collaboration, she says.

You can find out more about the impact of AI on legal education here

Key findings:

• • • Workloads grow, while staffing stays flat 鈥� Many government legal department professionals say their work keeps increasing while staffing remains stagnant; and many are turning to AI tools to improve capacity.

    • AI adoption is surging 鈥� Over the past year, AI adoption among government legal departments has spread rapidly, with federal and state agencies leading the way.

    • Unfortunately, AI oversight hasn鈥檛 surged 鈥� Many legal departments report that their AI governance is lagging behind adoption, with 20% of agencies having no AI use policy in place at all.

Government legal departments are facing an all-too-familiar problem: more work, more complexity, and the same number of staff to do the job, according to the 成人VR视频 Institute鈥檚 2026 Government Legal Department Report, which captures the insights from 200 government legal department professionals at varying levels.

Jump to 鈫�

2026 Government Legal Department Report

Threaded through these insights, some clear trends emerged. For example, technology 鈥� especially AI and other advanced tools 鈥� is increasingly serving as an extension of staff, expanding agencies鈥� capacity to manage rising workflow demands.

Increasing pressures across all levels

More than one-third of respondents report that their workload increased by more than 10% in the past year, with many handling between 21 and 50 legal matters per week. At the same time, workloads are becoming more complex, with more than one-third of respondents saying that more than half of the legal issues they face are complex, which is particularly notable at the state and federal levels.

Staffing shortages, a top concern in recent years, continue to persist. Three-quarters of respondents say their agencies experienced staffing shortages over the past two years, and almost two-thirds say they anticipate shortages into 2027.

Indeed, despite an increase in complexity and workload, attorney staffing levels have stayed the same for almost 40% of agencies, the report shows. And at the federal and state level, departments were more likely to have experienced a reduction of more than 10% of their staff.

AI adoption skyrockets, making governance more necessary than ever

More than one-quarter of respondents say their agency or department is now using AI tools, up from a meager 5% last year, with this increase taking hold at the federal and state level much more quickly. Among the different groups of respondents, one-third of federal and state government legal professionals report using AI tools compared to just 19% of those at county and city departments. Resistance to AI is diminishing, too; however, more than one-third of county and city legal departments still report having no plans to use AI.

Optimism toward AI is rising alongside implementation, the report shows. More individuals at the federal and state level feel optimistic than pessimistic about AI technology, which is an inversion of last year鈥檚 sentiment. Among county and city legal professionals, pessimism still remains more common. Among all respondents, confidential data exposure remains the top evaluation criterion when assessing these advanced tools.

The report underscores that this all points to a need for the establishment of strong governance models before adoption. Nearly two-thirds of government agencies and departments have an AI use policy in place or are developing one, respondents say. However, 1-in-5 departments and agencies are still without an AI use policy, risking unofficial use of prohibited AI tools.

Those agencies hesitant to implement AI technology are encouraged to view AI technology as a way to increase staff capacity amid flat staffing, rising workloads, and growing matter complexity. AI tools can help reduce strain on employees, contributing to better-managed workloads while reducing employee burnout. When appropriately vetted, however, AI technologies can reduce administrative burdens, increase legal research efficiency, and help those organizations facing trying to manage more work with the same staffing levels.

An actionable path forward

As the report makes clear, AI is no longer a future challenge; rather, it鈥檚 a present reality in a rising percentage of government legal departments. Indeed, the report outlines ways departments and agencies can move forward in this space, by beginning with lower-risk foundational tools like legal research and case management systems; and then investing time in developing thoughtful AI use policies and evaluation protocols. With responsible staff training and a thoughtful evaluation process, AI technologies can protect the valuable time and work-life balance of government legal professionals.

Increasing workloads are not optional for government legal departments, but how department leaders empower their staff to manage these workloads is becoming the differentiator.

You can download

a full copy of the 成人VR视频 Institute’s “2026 Government Legal Department Report” by filling out the form below:

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 highlights:

• • • AI can help remove interpersonal friction during feedback conversations 鈥� Attorneys have taken naturally to soliciting substantive critique from AI that covers tone, argument strength, and clarity in ways they might hesitate to request from a colleague or supervisor.

    • The role of feedback-giver remains irreplaceable 鈥� The developmental core of feedback still requires someone who knows both the work and the person receiving the feedback, even if AI is used as a sounding board.

    • Feedback culture can be accelerated using human-AI mentorship 鈥� AI makes the conditions for receiving feedback easier and can spark shared human conversations about the work rather than replacing those conversations.

It鈥檚 one of those things upon which most people on any legal team would agree 鈥� feedback matters. It鈥檚 essential to professional development, to mentorship, and to the long-term growth of attorneys at every level. And yet, for all the emphasis placed on its importance, feedback remains one of the more nuanced and inconsistently experienced dimensions of working in the legal profession.

At my firm, Seyfarth Shaw, in particular, there has long been a deliberate focus on building high-performing teams and developing legal talent in a way that is both structured and intentional. We have designed systems that measure and reinforce feedback because we believe it is central to how attorneys grow and how high-performing teams consistently deliver exceptional client service.

Within the legal profession, however, there are dynamics and circumstances that can often make feedback more complex to navigate in practice.

At its core, a lot of feedback still relies on the comfort level of both supervising and junior attorneys to proactively provide it and seek it out, and that is often easier said than done. Time pressures, the structure of legal teams, and the challenge of balancing candor with constructiveness all contribute to making this an often-difficult task.

One of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them 鈥� not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact.

Attorneys are trained, as an actual skill set, to question everything and that can naturally extend to how feedback is processed, depending on how it is delivered and by whom. The amount of effort, intelligence, and judgment that goes into legal work is significant, so when that work is challenged, it can feel personal. And on the receiving end, actively seeking out substantive feedback is not a muscle that gets consistently developed in most educational settings leading up to working within a law firm.

The behavioral shift

This is the backdrop against which something genuinely interesting started happening when generative AI entered the picture. Almost immediately, one of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them 鈥� not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact. Traditionally, this is the kind of feedback many of these same professionals might hesitate to request directly from a colleague or supervisor.

What struck me was how quickly and naturally asking this of AI became a default behavior. There was an almost instinctive willingness to let AI review and analyze work product in a way that felt qualitatively different from how feedback had traditionally been experienced in interpersonal settings.

Landmark, meta-analysis research on found that feedback interventions actually decreased performance roughly one-third of the time, particularly when that feedback shifts attention to the self and triggers anxiety rather than a renewed focus on the work. More identify three triggers that cause people to reject feedback, including the relationship trigger, in which the reaction is not to the substance but rather to the person delivering it.

And 鈥檚 Dr. Larry Richard鈥檚 adds a profession-specific dimension: Lawyers tend to score lower on resilience, defined as how one reacts to criticism or rejection, while also ranking high in skepticism, an instinct to question assertions rather than accept them at face value. The combination can make feedback both more essential and, at times, more complex to deliver effectively.

Using AI neutralizes many of these dynamics 鈥� there is far less perceived pressure, no interpersonal dynamic to navigate, and no relationship to manage in the moment. We are constantly giving AI feedback about what it did right, what it did wrong, and how to improve. Indeed, we often are already flexing that muscle without thinking twice about it.

What the paradox reveals

This is the part to which I keep coming back. The problem was never that lawyers cannot handle feedback; if that were true, they would not be seeking it so readily from AI. The opportunity lies more in the conditions under which feedback is delivered and received. AI did not make people more open to feedback, rather it removed some of the perceived barriers that can accompany feedback in traditional settings.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better.

However, removing that friction is not the same as providing what lawyers actually need to grow. AI can tell you that your argument has a structural gap, but it cannot tell you why that gap matters in the context of a particular client relationship, a judge鈥檚 known preferences, or the broader strategy of a case. It cannot replace the judgment that comes from a supervising attorney explaining not just what to change, but why it matters to change it, and how to think about it differently next time. The developmental core of feedback still requires a person who knows the work, knows you, and is invested in your growth.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better. It has identified behaviors 鈥� such as seeking input early, iterating quickly, engaging with critique 鈥� that firms like ours have long been working to encourage, and made such behaviors easier to access in the flow of work.

Yet, there is also something more subtle happening. With AI, attorneys retain a clear sense of autonomy over the feedback itself. They can take it or leave it, focus on it, or set it aside without any interpersonal consequence.

AI creates a different dynamic: It functions more as a sounding board, essentially another set of eyes on the work. That makes it easier to engage with feedback in a more exploratory way by incorporating what resonates, questioning what is unclear, and testing ideas before bringing them back into a human conversation. In that sense, AI can help build the muscle not just of receiving feedback, but of engaging with it more thoughtfully 鈥� including developing the confidence to ask the 鈥渨hy鈥� that is often where the real learning happens.

At Seyfarth, this is where we see a meaningful opportunity to build on an already strong foundation. Our focus has long been on creating a culture in which feedback is expected, measured, and part of how work improves. What AI allows us to do is take that a step further, making feedback more continuous, more immediate, and easier to engage with as part of the workflow.

In the next part of this series, we will look at what we call SEYmultaneous Advancement, our way of intentionally bringing AI into the feedback process 鈥� not as a replacement for human interaction but as a progression 鈥� and in true AI form, iterating with the human-in-the-loop along the way.

You can find out more about

Key highlights:

• • • The limits of the current system and good intentions 鈥� While the justice gap is not the fault of legal educators, their good intentions alone cannot close a systemic gap that requires new models of training and delivery designed for the long term.

    • A healthcare model for legal services is needed 鈥� Just as the healthcare industry relies on physicians, nurses, and physician assistants, the justice system needs a wider spectrum of trained and regulated legal providers; and American law schools are best positioned to educate, license, and oversee them.

    • States prove the model works 鈥� Alaska, Utah, and Arizona have already developed programs that train and certify non-lawyer legal service providers to help individuals navigate courts and address common legal issues, offering a replicable framework for those states willing to open regulatory doors.

Our nation鈥檚 healthcare system has wisely evolved past being one built on doctors alone. Yet in the legal industry, access to services remains largely tethered to a lawyer-only model that leaves millions of people unable to secure the help they need. Every day, tenants face eviction without representation, parents navigate custody disputes alone, and workers struggle to secure employment benefits or resolve workplace disputes because they cannot pay for legal counsel.

Legal professionals need to work together to create a broader, smarter, and more efficient legal workforce that can meet the public鈥檚 legal needs while maintaining the United States鈥� current legal standards of excellence. American law schools are best positioned to lead this effort; however, they will need to partner with regulators to educate, license, and oversee new categories of legal service providers who, like nurses and physicians鈥� assistants, can help expand the public鈥檚 access to critical support.

Preserving excellence while expanding access

American legal education has long been the global gold standard, producing leaders in law, politics, and business. Its rigorous curriculum, emphasis on critical thinking, and commitment to developing practical problem-solving skills have established a framework that many systems around the world aspire to emulate.

While meaningful innovations have taken place in legal education over the years, many are best characterized as refinements to the existing model rather than significant reforms. For example, curricular options today are more likely to include a wider variety of subject areas and teaching methods, however, most US legal education is still delivered through an in-person, full-time, three-year post-graduate Juris Doctor (JD) degree. While the overall quality of American legal education is exceptional, it is not filling our nation鈥檚 need for justice work.

The consequences are increasingly difficult to ignore. Low-income Americans receive no or insufficient legal help for 92% of their substantial civil legal problems, according to the Legal Services Corp.鈥檚 report. As a result, in many court systems, self-represented litigants have become the norm rather than the exception, whether the legal challenge involves housing, consumer debt, or family stability.

This is not the fault of legal educators, who often go above and beyond to help bridge the gap through the provision of free legal services and other efforts. Even so, it is the responsibility of legal educators to assist in designing and supporting new models of training and legal delivery to systemically narrow the gap for the long term.

Innovation beyond fine-tuning

Addressing this persistent and growing issue will require more than fine tuning. Instead, to meet the demands of a society increasingly characterized by inequality, social division, and complex interdisciplinary problems requires change that will better prepare our justice system for the future.

To get there, legal educators may have to sacrifice one part of what has long defined them: homogeneity. While a degree from a more elite law school is certainly rewarded in the entry-level employment market, the legal education provided at most of the accredited law schools in the US is more alike than different.

For law schools to help close the justice gap, increasing institutional pluralism is essential. Law schools can and should differentiate themselves by developing tailored solutions to address specific justice challenges within their reach. For example, Medical-Legal Partnership Clinics at and help low-income clients address legal issues that can impact their health outcomes. And students at the University of Arkansas School of Law provide assistance to small businesses, nonprofits, and rural municipalities that often cannot afford legal counsel though the university鈥檚 Community and Rural Enterprise Development Clinic.

To be sure, law schools cannot and should not do this alone. Law school deans have rightly encouraged legal education鈥檚 accreditation process to improve regulatory flexibility and promote responsible change. As a result, many schools are developing high-quality online programs that offer both access and excellence. These programs may expand the pool of lawyers over time, but they remain largely focused on JD education rather than the broader workforce that will be needed to improve the public鈥檚 legal health.

A framework for responsible expansion

To enhance access to justice, the legal profession needs to move beyond 鈥渆ducating lawyers鈥� alone and expand into teaching law more broadly. The traditional JD degree will continue to be vital to our legal system; but just as healthcare relies on physicians, nurses, physician assistants and other licensed professionals, the justice system needs a wider spectrum of trained and regulated providers.

To get there, states must open their doors to a wider range of legal services providers. Unfortunately, many states 鈥� often for political reasons 鈥� continue to resist allowing limited-service legal providers to handle routine but still important legal needs.

Models for this approach already exist. , , and each have developed programs that train and certify non-lawyer legal service providers to help individuals navigate courts, understand their rights, and address common legal issues involving housing, family law, public benefits, and debt.

If state courts and legislators are serious about closing the justice gap, they should begin by opening their regulatory doors to these alternative legal providers, while providing responsible licensing and oversight mechanisms in collaboration with law schools in their state. If those doors are open, law schools can and will step through. Many law schools already have innovative master鈥檚 degree programs that are aimed at law-adjacent fields such as government contracts, human resources, compliance, and more. These non-lawyer educational programs can easily be tailored for alternative legal providers.

Keeping legal education in the hands of American law schools will properly balance access and excellence, ensuring the public continues to be served by qualified practitioners. Law schools have the skilled faculty, ethical underpinnings, and institutional infrastructure that鈥檚 needed to train and oversee the next generation of justice workers.

A robust justice system needs a full spectrum of professionals to meet society鈥檚 legal needs, much as our healthcare system relies on a range of trained providers. Until we build such a structure, the justice gap will remain exactly where it sits today, to the detriment of many citizens.

You can find more about the听challenges facing law schools and legal education here

Key insights:

• • • The history of discovery is a history of matching tools to tasks 鈥� From paper review to keyword search to machine learning to generative AI, each innovation succeeded by assigning the right level of capability to the right kind of problem.

    • Adoption has outpaced understanding 鈥� AI discovery tools are now widely used, but the statistical literacy needed to defend their use in court has not kept pace.

    • Courts reward preparation, not adoption 鈥� Judges generally don’t weigh in on which tool a law firm uses; rather, they weigh in when counsel cannot explain or defend that choice.

Document review has always been organized around a basic question: Who, or what, is best suited to handle a given volume and type of material? In the paper era, that question was answered with people. Large cases were staffed with tiers of reviewers, working through boxes of documents. The model was labor-intensive but logical. More documents required more reviewers, and the work was distributed according to experience level, with junior reviewers handling first-pass relevance calls and more experienced attorneys handling privilege determinations and quality control.

The widespread shift to electronic discovery in the early 2000s introduced keyword search as an intermediate tool. Search terms allowed reviewers to narrow enormous document populations before human eyes even glanced at them, but the approach had limits as well. A search for “ice cream”, for example, would miss a document that said “gelato,” and a poorly constructed term list could either bury reviewers in false positives or miss documents entirely.

The next major shift came with the introduction of supervised machine learning in the late 2000s and early 2010s. This was a different kind of tool entirely. Rather than searching for fixed terms, the system learned from examples. A reviewer would mark documents as relevant or not relevant, and the system would apply that pattern across the rest of the collection, prioritizing documents most likely to matter. This was the first point at which legal discovery began to resemble a partnership between human judgment and statistical inference, rather than a purely manual or purely mechanical process.

Bringing GenAI in

The most recent shift, beginning around 2022, introduced large language models and generative AI (GenAI) into the same workflow. These tools do not simply sort or rank documents 鈥� they can summarize them, answer questions about them in natural language, and construct chronologies or timelines across a collection.

Each of these innovations did not in fact replace the one before it so much as they added a new layer of capability. Indeed, the central challenge in every era has been the same: The important determination is knowing which tool, or which level of human or machine capability, is appropriate for a given task.

For more on this, check out , featuring Dr. Maura Grossman

This is where the analogy to legal staffing becomes useful. A first-year associate and a senior attorney are not interchangeable, because they are suited to different kinds of judgment calls. The same logic increasingly applies to AI tools. A general-purpose AI model is not the same as a fiduciary-grade system built and validated for a specific legal task. Choosing a narrowly designed, purpose-built tool over a general one is the AI equivalent of choosing an senior attorney over a law school graduate for a task that requires accountability and demonstrated reliability, not just general competence.

The literacy gap that follows adoption

As these tools have become more capable, the legal profession’s ability to evaluate them has not necessarily kept pace. Evaluating AI discovery tools, both before they are adopted and after they generate output, requires such as recall, precision, confidence intervals, and margin of error. In fact, these are not concepts most lawyers were trained to work with, and many rely heavily on vendors to supply and interpret these metrics rather than developing the capacity to do so themselves. The risk is a quiet one: figures or citations that should raise concern can go unexamined simply because the people reviewing them do not know what to look for.

This gap matters because it shapes how discovery disputes are litigated. If one party argues that another’s recall rate is too low 鈥� meaning that a significant share of relevant documents was not produced 鈥� both sides need a working understanding of how that figure was calculated and what it means in order to argue the point credibly. The legal and technical questions in such disputes cannot be fully separated.

What courts actually expect

Discovery is , with judges generally staying out of methodology decisions entirely. Judicial involvement in AI-driven discovery is therefore a signal, not a routine occurrence. In fact, courts usually step in under two circumstances: i) a dispute over whether a chosen method is adequate; or ii) a timing problem severe enough to threaten the case schedule. Both situations typically indicate that something has already broken down. A low recall rate that prompts a motion or a delay serious enough to draw judicial questions, for example, suggests a deeper flaw in how the review was designed or executed and is sometimes serious enough that it can call an entire production into question rather than just the documents in dispute.

The common thread in these failures is governance, not technology. A capable AI tool used without a clear validation process, defined oversight roles, or documented standards for measuring output is no more reliable than an undertrained reviewer left unsupervised. Most of the disputes that escalate to a judge can often be traced back to a preventable gap: whether no one verified the tool’s metrics, no one understood what the numbers meant, or no one assigned responsibility for catching errors before production.

Those law firms that build proper AI governance into their discovery process, have clear protocols, defined accountability, and have a working understanding of how their tools are validated, are far less likely to need a judge to resolve what should have been caught internally.

The history of document review, at its core, is a history of matching capability to task, whether that capability comes from a person or a machine. As AI tools take on more of the discovery process, the skill that increasingly distinguishes effective use from risky exposure is not technical operation, but the ability to select the right tool for the task, understand what its outputs mean, and recognize those instances in which human review remains essential.

To learn more about how courts should approach AI and other advanced technology, check out the 成人VR视频 Institute’s

Key insights:

• • • Successful AI implementation requires a solid foundation 鈥� Strategy, leadership, and the impact on operations and individual users are key elements to any successful implementation.

    • AI success is a skills strategy, not a technology strategy 鈥� AI creates a whole new set of skills that are required for both legal department attorneys and department leadership.

    • AI changes how legal work is conducted 鈥� If implemented correctly, AI not only improves the end work product, but it also changes how lawyers perform their jobs.

Corporate legal departments are already experiencing the benefits of AI, including improved productivity, and reduced costs and errors, the 成人VR视频 Institute鈥檚 recent shows. So it鈥檚 not surprising that AI is increasingly becoming a strategic priority for general counsel (GCs).

The report cautioned, however, that success with AI is not a given. AI is not a silver bullet which guarantees improvements across the department. Instead, AI adoption and implementation must be carefully planned in order to realize those benefits.

Crucially, successful AI implementation is not simply about the technology; rather, it鈥檚 a reflection of the department itself and often can signal whether the department has the right elements in place to enable that success.

AI enhances successful legal departments 鈥� it does not create them

AI implementation is like any other law department strategy 鈥� it does not live on its own but instead advances as a direct result of everything that has come before it, including the work of the department鈥檚 attorneys and professionals, its daily operations and processes, and the GCs who are guiding the overall vision.

Overall, it鈥檚 about having a solid foundation upon which to build AI adoption and implementation.

The Pyramid of AI Success

AI may be one of the most impactful and transformational technologies to come on the scene in recent years, but it鈥檚 important to remember that it is still simply one tool among many. And its ultimate success will be determined not only by its capabilities, but by how it integrates with and augments the work that corporate legal department attorneys perform daily.

The technology itself does not perform the work 鈥� it enables more efficient work. This means that the rise of AI creates a whole new set of necessary skills for both legal department attorneys and department leadership.

With that in mind, GCs should focus on a few key areas to improve their department鈥檚 chances of AI success. The essential steps can be viewed as a pyramid 鈥� every step that you take builds, each upon another, creating a solid foundation. Establishing a top-level AI strategy means setting the tone from leadership, which then permeates down through operations and ultimately transforms how individual users work every day.

• • Learning 鈥� Most departments have a basic AI understanding and a culture to encourage change, but they often do not have the depth of understanding to move from AI literacy to AI fluency. Be sure to determine where your team is on this learning curve.
  • Empowerment 鈥� Empowering your professionals is crucial to drive experimentation and identify new use cases. Ask yourself, does my team feel encouraged to explore new ways of working and empowered to make changes?
  • Ownership 鈥� The legal team should feel they have significant input into how AI will be used in the department and throughout the organization. AI can be a major transition, and team members should feel that they can freely share ideas, concerns, and insights.
  • Accountability 鈥� Team members with personal goals that are linked to AI adoption are more likely to become top learners and regular users, our research shows, and this leads to greater overall benefits for the department.
  • Usage 鈥� Regular use drives adoption, so you should build AI into your team鈥檚 daily habits, monitor how many legal team members have tried AI, and how many are using it regularly.
  • Expectations 鈥� Balance encouraging uptake with clear expectations around adoption. Offering open encouragement along with access to tools and training to build momentum can be key first steps. As team members become more proficient, set formal expectations around AI usage. Be clear that when targets are set, usage will be tracked and individuals will be held accountable. Then, follow up with low- or no-usage individuals to determine causes, such as difficulty with training.

For GCs, today鈥檚 top challenge is how the department can develop needed AI skills in a way that will best augment how lawyers work. If implemented correctly, AI will not only improve the end work product, but it will also better enable lawyers to perform the work they do best.

AI success with outside counsel

The same principles of strategy and leadership that contribute to AI success within the department also extend to working with outside counsel. Currently, more than half of corporate counsel say they believe their outside law firms should be using AI, according to the report; however, two-thirds also say they do not know how their outside firms are approaching their use of AI.

This creates a communication gap, in which some GCs attribute to hesitance or caution. 鈥淲e do not ask and they are shy to provide answers because they are already under a lot of pressure because their rates are so high,鈥� reports one GC.

About three-quarters of corporate counsel also say they expect their outside law firms to take the lead in AI conversations between the department and the firm. However, that does not mean that GCs should simply accept a lack of conversation if firms are not forthcoming. Those GCs that want their outside firms to embrace AI should be open and transparent, conveying that they believe AI can assist firms with most work tasks, while placing a strong emphasis on output verification and the authority of attorney expertise. Indeed, GCs need to understand how their outside firms are using AI, especially how and when it is being applied, how it鈥檚 being supervised, and, perhaps most importantly, how it impacts fees.

Without detailed and regular discussions, GCs could develop a blind spot in this area. 鈥淐onversation has been only high level,鈥� another GC explains. 鈥淲e generally know what AI they are using but not how they are using it.鈥� What鈥檚 surprising, the GC adds, is that 鈥渢he billing has remained the same as it did before 鈥� so either they are not using AI tools efficiently, or they are just doing double work.鈥�

You can download a fully copy of the , from the 成人VR视频 Institute here