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 insights:

• • • Agentic AI acts autonomously, creating new accountability challenges 鈥� Agentic AI acts and makes decisions with minimal human intervention, and this shift changes everything about responsibility and oversight.

• • • With intentional design, the risks can be addressed confidently 鈥� Silent failures, accountability diffusion, and confidentiality breaches can only be mitigated through governance, testing, and rigorous human oversight.

• • • AI is changing legal work, not eliminating it 鈥� When agentic AI handles routine tasks, legal professionals can move their attention onto higher-value work and increased responsibilities.

It is no longer useful to treat all AI as a single category or a tool for a single use case. Generative AI (GenAI) has already begun reshaping legal work by drafting documents, researching precedents, and answering questions with remarkable speed. At its core, however, GenAI remains a responsive tool. Agentic AI, on the other hand, represents a distinct evolution. Rather than waiting for a prompt, agentic AI systems can plan workflows, carry them out autonomously, and make decisions along the way.

As technology and the judicial system become increasingly intertwined, it is essential to examine where these more advanced tools intersect and what that convergence means for legal institutions. Ankita Upadhyay, Senior Director of AI Enablement at 成人VR视频, recently shared her perspective during a recent webinar,听, presented by 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) 鈥斕齛nd offered valuable insight into the opportunities and responsibilities that accompany this shift.

One of the key notes to understand from the panel is that 鈥済enerative AI gives you an answer. Agentic AI takes an action 鈥� and that distinction changes everything about the accountability,” Upadhyay said, adding that the distinction is not merely technical. It must reshape how we think about professional responsibility and the integration of AI into institutions that are built on trust and accuracy.

The promise of efficiency and transformation at scale

The potential of agentic AI is already visible in courts across the country. In Palm Beach County, Fla., for example, court officials are using agentic AI to process incoming documents at unprecedented scale. When an attorney files a document, the system autonomously identifies the document type, classifies it, extracts data, and routes it appropriately. The county already has processed up to 5 million documents using this process, operating 20 hours a day, every day of the year.

The most important part isn’t just the volume, however, it’s what happened to the people.

The staff who spent time on routine document processing were not laid off; instead, they were reassigned. Clerk 1 positions were transitioned to Clerk 3 and Clerk 4 roles, and that meant greater responsibility, more complex decision-making, and increased compensation for those making that transition.

“The staff that was doing all the processing of documents has been reallocated to customer experience and more complex tasks,” explained Parik Chokski, Director of IT for Palm Beach, on the webinar. This development reflects a broader truth: AI is not taking jobs in the legal sector; rather, it鈥檚 changing what those jobs entail.

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As more repetitive work moves to AI, legal professionals move their attention toward the kind of work that demands their judgment, expertise, and accountability.

Risks are real, but not insurmountable

Yet the promise of agentic AI comes with genuine risks that differ from those posed by generative AI. Because agentic AI acts autonomously, for instance, failures can occur silently and invisibly, and sometimes repeatedly before detection.

成人VR视频鈥� Upadhyay identified three predominant risks for legal professionals and their organizations with agentic AI use:

1. Accountability diffusion 鈥� When an agentic AI system produces a document through a chain of autonomous decisions, it becomes difficult to determine where human judgment ended, and machine decision-making began. This ambiguity directly challenges professional conduct rules, which assume lawyers make every material decision. The result is an unclear line of responsibility and potential legal exposure for the lawyer.

2. Confidentiality at scale 鈥� Agentic AI systems operate across entire databases and multiple use cases simultaneously. A single misconfiguration of permissions can allow an AI agent to access privileged information to which it shouldn’t have access, potentially sharing sensitive client data across unintended matters. The danger lies in the fact that this often happens silently and repeatedly until discovered.

3. Irreversibility 鈥� Unlike GenAI, where a flawed draft often gets caught during review, agentic AI can send client communications, file documents, or update records based on faulty reasoning even before human oversight intervenes. The speed of action outpaces the speed of review, and thus, it creates a gap that traditional legal processes weren’t designed to address.

“The risk isn’t that AI gets it wrong,鈥� Upadhyay said. 鈥淭he problem is agentic AI systems, when it gets things wrong, it happens silently in a black box until you monitor it, and that’s the biggest challenge.”

Guardrails for responsible implementation

Given this, how do courts and legal organizations implement agentic AI thoughtfully? The webinar panelists, drawing on real-world implementations and NCSC research, emphasized several critical actions, including:

Establish clear governance 鈥� Begin with centralized registration of all agentic AI agents, conduct rigorous risk classification based on task impact, and start with low-risk workflows before advancing to high-stakes tasks. “Having a proper agentic AI governance is really important,” Palm Beach’s Chokski said.

Commit to rigorous testing 鈥� Extensive stress-testing in development and Q&A environments must precede any production deployment. Palm Beach’s implementation required weeks, if not months, of testing before going live 鈥� but that investment paid dividends in reliability and organizational confidence.

Design for transparency 鈥� Build workflows with built-in checks, balances, and fail-safes. Establish comprehensive logging that tracks what the AI agent does, what permissions it has, and what decisions it makes at each step. Monitor continuously for behavioral drift.

Maintain human oversight 鈥�”Trust but verify,” Chokski noted. Agentic AI is powerful and here to stay; but so are human professionals, and they must always retain oversight, the ability to intervene, and ultimate accountability for outcomes.

The conversation continues

The choice legal organizations face today is not whether agentic AI will exist, but how to engage with it responsibly.

Organizations that approach agentic AI with intentionality, clear frameworks, and commitment to human judgment will unlock its potential to expand capability, improve efficiency, and free legal professionals to do work that requires their expertise and accountability. Those that rush forward without guardrails risk silent failures that could undermine trust in both the technology and in the overall institution.

The path forward demands partnership: AI handles scale and speed, while humans provide judgment, accountability, and ethical reasoning. When those work two parts work together intentionally and with clear guardrails, that’s where justice is served.

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

Key insights:

• • • AI augments judicial judgment without replacing it 鈥� Used thoughtfully it clarifies reasoning and improves access.

    • Strict guardrails are needed 鈥� These can include structured prompts, anonymized data, and rule-based outputs helps interrupt bias and maintain integrity.

    • Judges should lead 鈥� They can do this through peer learning and education, which fosters responsible use while preserving public trust.

The integration of AI in the judiciary is gaining momentum, offering a promising solution to the growing caseloads, access-to-justice gaps, and public trust challenges faced by courts across the United States. And as the judiciary explores the potential of AI, a crucial conversation is emerging 鈥� one that highlights the importance of responsible and thoughtful adoption.

A recent webinar, , presented by the听 鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥� shed light on the experiences of early adopters of generative AI (GenAI) in the judiciary. In the webinar, Prof. Amy Cyphert of West Virginia University and U.S. Magistrate Judge Maritza Dominguez Braswell of the District of Colorado shared their insights from their own use of AI, emphasizing the need for a deliberate and informed approach.

The role of AI in judicial decision-making

A common fear is that AI will somehow take over the position of final arbiter in court proceedings. However, judges are not interested in having AI displace their judgment; rather, they see AI as a tool that augments and helps advance justice, not a tool that replaces decision-making or human judgment.

Judges also are not rushing into AI use. Instead, they are approaching it with a deep commitment to responsible use and a desire to increase, not decrease, public trust. “Everybody on that spectrum 鈥� from ‘I’m just learning’ to ‘I want to be a power user’ 鈥� says, ‘But I want to do it right,鈥�” says Judge Braswell.

AI can also help judges close communication gaps. By taking decisions that judges have already reasoned through and converting them into accessible explanations, AI can help all litigants clearly understand the relevant legal framework, rule, or process behind the decision. This is even more impactful in cases involving self-represented litigants.

Leveraging AI to enhance judicial communication

Judge Braswell understands this well. In every case with at least one self-represented litigant, she offers a plain language summary of her written decisions. Although she does not use AI to draft those, she does use AI to translate complex legal reasoning when delivering information from the bench.

鈥淚f I have 15 minutes for a hearing and want to explain to a self-represented litigant something complex, I use AI to help me translate legal jargon into plain and simple language,鈥� she explains. 鈥淚 want the self-represented litigant to understand what I鈥檓 doing and why I鈥檓 doing it 鈥� and AI helps me translate lawyer-speak into plain-speak, quickly.鈥�

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This capability is particularly valuable for judges who often struggle to find the time to connect with litigants. By leveraging AI, they can provide more personalized and informative interactions, ultimately enhancing litigants鈥� judicial experiences. In addition, some judges are using AI to create engaging content, such as avatars and videos on YouTube, to make themselves more relatable and accessible to the public; while others are using AI to help litigants navigate court processes, helping to demystify the system and reduce anxiety.

Guardrails for responsible AI use

Of course, Judge Braswell doesn’t use AI casually. She has strict policies and protocols in place, including segregation of work and personal accounts, prompt anonymization, and prohibiting her clerks from uploading sensitive information or delegating core functions and judgment to any AI tool. She also trains her chambers on high-risk and low-risk cases and emphasizes the importance of proper AI use through structured prompts, appropriate settings, standing instructions, and deliberate guardrails.

For example, Judge Braswell describes a dedicated project in which she uploaded her district’s local rules, the Federal Rules of Civil Procedure, and standing orders. She queries that project any time she needs to refresh on an applicable rule or procedure. She gave the AI tool clear instructions, such as: Don’t answer unless grounded in a rule. Cite the rule with every response. If you don’t know, say so.

While these types of practices do not make the tools risk-free, Judge Braswell notes, they do offer guardrails to help support, rather than undermine, judicial integrity.

Addressing risks and challenges

While , the deeper risks in AI use in the courts are bias, cognitive deskilling, and erosion of public trust. Judge Braswell warns that bias is harder to detect than any made-up case citation. “If you ask for a legal framework in an employment discrimination case, the system may pull more from defense-side articles because larger firms publish more content,鈥� she explains. 鈥淭he result is a subtle tilt in perspective.”

To counter this, she prompts her AI tools deliberately asking for diverse perspectives, asking the tool to gather contrary views, or telling the tool to answer only after asking follow-up questions that could identify user bias. Without this intentionality, bias can go undetected.

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On the webinar, Prof. Cyphert echoed concerns about the next generation. “I worry that younger lawyers may skip critical learning processes if they rely too heavily on AI for drafting or research,” Prof. Cyphert says. “Is there a cognitive benefit to writing that we’re losing?”

The path forward through education, experimentation & transparency

During the webinar, both speakers rejected mandatory disclosure rules as counterproductive.

“It creates a chilling effect,” Judge Braswell says. 鈥淎nd we need people to engage for learning purposes.鈥� Instead, she notes that she advocates for voluntary transparency 鈥� judges explaining their use of AI in ways that build public understanding and confidence.

Prof. Cyphert agrees. 鈥淵ou can’t assess risks and benefits if you don’t understand the technology,鈥� she says, adding that she encourages judges to attend webinars, read research, and talk to peers. Similarly, Judge Braswell co-founded the , a judge-only, peer-led forum for candid discussion that exists as a safe space to share challenges, test ideas, and learn together.

As the webinar notes, the future of justice isn’t just about whether courts and judges are using advanced AI technology, it’s about how that technology should be used 鈥� with care, purpose, and always with people at the center.

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

Key insights:

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

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

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

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

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

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

AI’s pattern recognition is a gamechanger

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

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

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

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

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

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

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

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

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

The human/AI partnership is where justice is served

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

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

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

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

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Key insights:

• • • AI brings improved efficiency 鈥� AI accelerates tasks like document review and research, freeing lawyers to pursue more high-value work for clients.

    • AI does the work of a team of lawyers 鈥� AI levels the playing field for small law firms and solo practitioners by providing additional capacity without adding headcount, thereby allowing fewer lawyer to do the work of many.

    • Yet, AI still needs guardrails 鈥� Lawyers must remain accountable, however, with human oversight and review to ensure that AI outputs are accurate and correct, thereby preserving nuance and professional judgment.

Already, AI is no longer a theoretical concept for legal professionals, nor is it a nice-to-have for law firms that are seeking to impress their clients with improved efficiency and cost savings. That means, the practical question now becomes how to adopt AI in ways that improve speed and capacity of lawyers without compromising accuracy, confidentiality, or professional judgment.

The strongest near-term value shows up where modern practice is most strained: high-volume inputs and relentless timelines. In that environment, AI can be most helpful as an accelerant for the first pass through large bodies of material.

This possibilities, opportunities, and challenges of using AI in this way were discussed by a panel of experts in a recent webinar, , from the听, a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI).

One panelist, Mark Francis, a partner at Holland & Knight, described one way that AI can be an enormous help. “Anything where we’re dealing with large volume of materials that need to be reviewed [such as] large sets of documents, large sets of legal research, large sets of discovery. Obviously, AI can be leveraged in all of those circumstances.” That framing is important because it anchors AI’s utility in a familiar workflow: review, triage, and synthesis at scale.

AI also has a role earlier in the workflow than many attorneys expect. In addition to sorting and summarizing, it can help generate starting structures. For lawyers drafting motions, client advisories, demand letters, contract markups, or internal investigations memos, the hardest step can be getting traction from a blank page. 鈥淚t’s really good at content or idea generation,鈥� Francis said, adding that lawyers can ask AI to 鈥済enerate some ideas for me on this topic, or generate an outline of a document to cover a particular issue.”

“AI is definitely going to benefit some of the small law firms who cannot actually afford the workforce. AI can be an extension when it comes to the automation.”

Of course, that does not mean letting an AI model decide what the law is; rather, it means using AI to produce an initial outline, identify possible issues to consider, or propose alternate ways to organize an argument. Then, the attorney should apply their own judgment to accept, reject, refine, and verify the AI鈥檚 output.

For legal teams, the ideal mindset is that AI can compress the time between intake and a workable first draft, whether that draft is a research plan, a deposition outline, a set of contract fallback positions, or a motion framework. However, speed is only valuable if it facilitates careful lawyering, not just taking shortcuts.

Efficiency that scales down, not just up

AI’s impact is not limited to large law firms with dedicated tech & innovation budgets. In fact, the benefits may be most transformative for smaller legal organizations that feel every hour of administrative drag and every unstaffed matter. Panelist Ashwini Jarral, a Strategic Advisor at IGIS, underscores how broad the current level of AI adoption already is. “AI is already being used in a lot of legal research, contract analysis, and in office operations,鈥� Jarral explained. 鈥淲hether that’s in a small law firm or a large law firm, everybody can benefit from that automation with this AI.”

For many practices, that list maps directly onto the work that consumes lawyers鈥� time without always adding commensurate value: repetitive research steps, first-pass contract review, intake and scheduling, matter administration, and other operational tasks.

Historically, scale favored organizations that could hire more associates, paralegals, and support staff to push volume through the pipeline. Now, AI offers a different form of leverage: additional capacity without adding headcount. “It is definitely going to also benefit some of the small law firms who cannot actually afford the workforce,鈥� Jarral said, adding that 鈥淎I can be an extension when it comes to the automation.” For a solo or small firm, that extension can show up as faster first-pass review of contracts, quicker summarization of records, more consistent intake workflows, and reduced time spent on repetitive back-office tasks.

At the same time, it is crucial to be clear-eyed about what is being automated. While AI can help deliver efficiency, it does not offer legal judgment itself. The legal profession still must decide, matter by matter, what level of review is required and what risks are acceptable.

“Lawyers are trained a certain way, and AI is never going to be trained that way. AI misses nuances. We’re always going to need lawyers; we’re always going to need the human in the loop.”

And that鈥檚 where implementation discipline becomes a strategic differentiator. Law firms that treat AI as a general-purpose shortcut tend to create risk; while firms that treat AI as a workflow component, with guardrails, review steps, and clear accountability, are more likely to capture value without compromising quality.

The non-negotiable: lawyers remain accountable

Any serious conversation about AI in legal practice must address these limits, panelists agreed. The Hon. Linda Kevins, a Justice on the Supreme Court in the 10^th Judicial District of New York (Suffolk County), offered the most direct articulation of the boundary line: “Lawyers are trained a certain way, and AI is never going to be trained that way. AI misses nuances. We’re always going to need lawyers; we’re always going to need the human in the loop.”

Indeed, legal work is saturated with nuance. The same set of facts can carry different weight depending on jurisdiction, judge, forum, procedural posture, and the client’s goals and risk tolerance. Even when the law is clear, the right action often is not. To strive for true justice requires judgment about timing, framing, business consequences, reputational risk, and settlement dynamics. Those are not merely inputs for an AI to process 鈥� they are human decisions that define legal representation.

As the webinar made clear, this is the point at which responsible use becomes practical, not abstract. If AI is used for research support, contract analysis, or document review, lawyers need an explicit approach for verification and oversight. The outputs may look polished and may sound confident; however, confidence is not accuracy, and professional responsibility does not shift to a vendor or an AI model. Human review is not a ceremonial or perfunctory step, nor is it a formality. Rather, it is the core control that protects clients and the court, and it is the inflection point that turns AI from a novelty into a defensible tool.

In practice, the human in the loop means deciding in which instances AI can assist and in what instances it cannot. It also means reserving an attorney鈥檚 time for the decisions that carry legal and ethical consequences and building repeatable habits that prevent teams from drifting into overreliance on AI, especially under deadline pressure.

The legal profession can capture real benefits from AI, including speed, scalability, and improved access, but only if it adopts the technology in a way that preserves what Justice Kevins highlighted: training, nuance, and human accountability.

You can find out more about how AI and other advanced technologies are impacting听best practices in courts and administration here

Key insights:

• • • Considering courts鈥� approach 鈥� Although many courts do not interact with litigants prior to filings, courts can explore how to help court staff discuss AI use with litigants.

    • Risk of generic AI tools 鈥� AI use in legal settings can’t be simply categorized as safe or risky; jurisdiction, timing, and procedure are vital factors, making generic AI tools unreliable for court-specific needs.

    • Specialty AI tools require testing 鈥� Purpose-built court AI tools offer a safer alternative for litigants, yet these require development and extensive testing.

Self-represented litigants have always pieced together legal help from whatever sources they can access. Now that AI is part of that mix, courts are working to help people use this advanced technology responsibly without implying an endorsement of any particular tool or even the use of AI.

Many litigants cannot afford an attorney; others may distrust the representation they have or may not know where to begin. In any case, people need a meaningful way to interact with the legal system. Used carefully and responsibly, AI can support access to justice by helping self-represented litigants understand their options, organize information, and draft documents, while still requiring litigants to verify their information and consult official court rules and resources.

These issues were discussed in a recent webinar, , hosted by . The panel explored the potential benefits of AI for access to justice and the operational challenges of integrating AI into public-facing guidance for litigants.

The problem with “Just ask AI”

Angela Tripp of the Legal Services Corporation noted that people handling legal matters on their own have long relied on a mix of resources, “some of which were designed for that purpose, and some of which were not.” AI is simply a new tool in that environment, she added. The primary challenge is that court processes are rule-based and time-sensitive; and a mistake can mean missing a deadline, submitting the wrong document, or misunderstanding a requirement that affects the case.

Access to justice also requires more than just access to information in general. Court users need information that is relevant, complete, accurate, and up to date. Generic AI systems, such as most public-facing tools, are trained on broad internet text may not reliably deliver that level of specificity for a particular court, case type, or stage of a proceeding. In these cases, jurisdiction, timing, and procedure all matter. Unfortunately, AI can omit key steps or emphasize the wrong issues, and self-represented litigants may not have the legal experience to recognize what is missing.

At the same time, AI offers several potential benefits to self-represented litigants. It can explain concepts in plain language, help users structure a narrative, and produce a first draft faster than many people can on their own. The challenge is aligning those strengths with the precision that court processes demand.

A strategic pivot: from teaching litigants to equipping staff

In the webinar, Stacey Marz, Administrative Director of the Alaska Court System, described her team鈥檚 early efforts to give self-represented litigants clear guidance about safer and riskier uses of AI, including examples of how to properly prompt generative AI queries.

The team tried to create traffic light categories that would simplify decision-making; however, they found this approach very challenging despite several draft efforts to create useful guidance. Indeed, AI use can shift from low-risk to high-risk depending on context, and it was hard to provide examples without sounding like the court was endorsing a tool or sending people down a path to which the court could not guarantee results.

The group ultimately shifted to a more practical approach 鈥� training the people who already help litigants. The new guidance targets public-facing staff such as clerks, librarians, and self-help center workers. Instead of teaching litigants how to prompt AI, it equips staff to have informed, consistent conversations when litigants bring AI-generated drafts or AI-based questions to the counter.

The framework emphasizes acknowledgment without endorsement. It suggests language such as:

“Many people are exploring AI tools right now. I’m happy to talk with you about how they may or may not fit with court requirements.”

From there, staff can explain why court filings require extra caution and direct users to court-specific resources.

This approach also assumes good faith. A flawed filing is often a sincere attempt to comply, and a litigant may not realize that an AI output is incomplete or incorrect.

Purpose-built tools take time

The webinar also discussed how courts also are exploring purpose-built AI tools, including judicial chatbots designed around court procedures and grounded in verified information. Done well, these tools can reduce common problems associated with generic AI systems, such as jurisdiction mismatch, outdated requirements, or fabricated or hallucinated citations.

However, building reliable court-facing AI demands significant time and testing. Marz shared Alaska’s experience, noting that what the team expected to take three months took more than a year because of extensive refinement and evaluation. The reason is straightforward: Court guidance must be highly accurate, and errors can materially harm someone’s legal interests. In fact, even after careful testing, Alaska still included cautionary language, recognizing that no system can guarantee perfect answers in every situation.

The path forward

Legal Services鈥� Tripp highlighted a central risk: Modern AI tools can be clear, confident, and easy to trust, which can lead people to over-rely on them. And courts have to recognize this balance. Courts are not trying to prevent AI use; rather, many are working toward realistic norms that treat AI as a drafting and organizing aid but require litigants to verify claims against official court sources and seek human support when possible.

Marz also emphasized that courts should generally assume filings reflect a litigant’s best effort, including in those cases in which AI contributed to confusion. The goal is education and correction rather than punishment, especially for people navigating complex processes without representation.

Some observers describe this moment as an early AOL phase of AI, akin to the very early days of the world wide web 鈥� widely used, evolving quickly, and uneven in its reliability. That reality makes clear guidance and consistent messaging more important, not less.

This shift among courts from teaching litigants to use AI to teaching court staff and other helpers how to talk to litigants about AI reflects a practical effort on the part of courts to reduce the risk of harm while expanding access to understandable information.

As is becoming clearer every day, AI can make legal processes feel more navigable by helping self-represented litigants draft, summarize, and prepare; and for courts to realize that value requires clear guardrails, court-specific verification, and careful implementation, especially when a missed detail can change the outcome of a case.

You can find out more about how AI and other advanced technologies are impacting听best practices in courts and administration听here

Key highlights:

• • • Risk varies by workflow and context 鈥� Practitioners should apply risk ratings based on workflow and context, such as low for productivity, moderate for research, moderate to high for drafting and public鈥慺acing tools, and high for decision-support.

    • Courts need their own developed benchmarks 鈥� Courts should develop and regularly review their own independent benchmarks and evaluation datasets instead of relying solely on vendor claims, because vendors may optimize systems for known tests.

    • Need for benchmarking to detect drift, degradation, and bias 鈥� Continuous, rigorous benchmarking of AI models is essential for courts and legal professionals to maintain confidence in these systems, since both the law and AI models change over time.

AI is not monolithic technology, and a risk-based assessment process is needed when using it. Indeed, courts and legal professionals must scale their scrutiny to match risk levels.

This approach 鈥� which balances innovation with accountability, along with other essential best practices 鈥� is detailed in a recent publication, , created as part of .

In a recent webinar, , one of the co-authors of the document, explained the purpose of the document: “The central aim of what we were thinking about in these best practices is to give courts and legal professionals a principle-based architecture when you’re thinking about the adoption of GenAI tools.”

Risk and human judgement serve as central elements

What is unique about this framework is that it categorizes risk based on key workflow actions of lawyering, for example:

• • • Productivity tools carry minimal to moderate risk
    • Research tools are assigned moderate risk
    • Drafting tools range from moderate to high risk
    • Public-facing tools carry moderate to high risk
    • Decision-support tools pose high risk

The framework holds that risk is dynamic rather than static, and there can be shifts in risk levels based on use cases. For example, a scheduling tool typically poses minimal risk; however, the same tool becomes high risk when used for urgent national security cases. And translation tools can shift from lower risk research support to high-risk decision-support depending on their use.

Similarly, when tools range from moderate risk to high risk, users need to be especially discerning in order to understand the underlying risks 鈥� and if the task should be delegated to AI at all.

“You can’t just rely on categories,鈥� explains from the IP High Court of Korea. 鈥淵ou need to understand the underlying risks and ask yourself: Would I delegate this task to another person? Am I comfortable delegating it publicly? If the answer is no, then you probably shouldn’t be delegating it to an AI either.”

In addition, clear red lines around when AI should never be used and classified as unacceptable risk exist for judicial use. “I believe the clear red line is automated final decisions or AI systems that assess a person’s credibility or determine fundamental rights involving incarceration, housing, family,” says Judge Kwon, adding that fundamental rights require human judgment.

“You can’t just rely on categories. You need to understand the underlying risks and ask yourself: Would I delegate this task to another person? Am I comfortable delegating it publicly? If the answer is no, then you probably shouldn’t be delegating it to an AI either.”

The extent of human judgment also has layers. , Shareholder at Greenberg Traurig, says he believes that AI for any legal use currently requires human oversight. 鈥淭he human supervision piece鈥� is utterly critical in the real world of practicing lawyers and law firms,鈥� Greenberg says. 鈥淵ou have to supervise the lawyers in the firm that are using the technology, including young lawyers.”

To help distinguish which type of human oversight is appropriate, the framework in the Key Considerations document defines two forms of such oversight: i) human in the loop, which means active human involvement in decisions; and ii) human on the loop, which means monitoring automated processes and intervening when needed.

What the difference between what each concept could look like in a court setting shows that a human in the loop is, for example, a law clerk using AI to do research on relevant case law and checking to make sure that the references are legally sound; and a human on the loop is a clerk monitoring an established robotic process to extract data for the case management system and spot-checking for accuracy.

Practical guidance for courts

In addition to judges considering the risk level of AI tools, Judge Kwon, Greenberg, and Carpenter, noted the importance of technical AI competence as part of lawyers鈥� and judges鈥� ethical duty, especially around verification, transparency, and independent benchmarks as part of accountability, as well as the need for understandable documentation to maintain public trust. To reinforce the latter point, , Director in Government Practice for 成人VR视频 Practical Law states: “It鈥檚 very vital, especially as we usher in the age of AI, that the public be informed as much as they can be about how that decision-making process is taking place.鈥�

In addition, Judge Kwon, Greenberg, and Carpenter highlighted additional guidance on the criticality of benchmarking, including:

• • • Court-developed benchmarks prevent overreliance on vendor data 鈥� Courts should develop their own benchmarks and independent evaluation datasets rather than relying entirely on vendor claims and review evaluation scenarios regularly. Vendors may optimize their systems for known tests, which leads to overfitting, in which a model learns patterns specific to its training data so well that it performs poorly on new, unseen data. This gives a misleading impression of reliability.
    • Ongoing rigorous benchmarking to detect model drift & degradation 鈥� To build confidence in AI models, courts and legal professionals must approach AI model evaluation with rigor and ongoing vigilance. Continuous benchmarking is essential, and it cannot be a one-time process because the law evolves constantly and precedents shift. In addition, AI models themselves update regularly, and courts need to monitor performance over time to detect AI degradation or bias drift.

Adopting a thoughtful, risk-informed approach to GenAI in legal practice and courts will help realize its benefits for efficiency and access to justice while protecting ethical obligations, due process, and public trust in the legal system.

You can find out more about how AI and other advanced technologies are impacting best practices in courts and administration here

• • • Establish strong governance and principles first听鈥� Before implementing AI, courts must create cross-functional oversight committees, define guiding principles that align stakeholders, and develop clear AI use policies with high-quality data governance.

    • Prioritize people-centered implementation听鈥� Successful AI adoption requires engaging stakeholders early as co-creators and conducting thorough resource assessments that account for total cost of ownership (including maintenance and compliance).

    • Commit to continuous monitoring and adaptation听鈥� AI implementation requires ongoing human oversight to monitor performance, prevent data and model drift, and systematically review governance structures and policies after each project to strengthen courts鈥� overall AI readiness for future initiatives.

AI has the clear potential to revolutionize courtroom workflows, but AI itself can carry unforeseen risks. Indeed, AI solutions are complex and opaque, with inherent randomness and risk, says , Senior AI Manager in the New Jersey courts.

To help courts leverage AI safely, the with support from the State Justice Institute convened 16 experts to create an , which was featured in a recent webinar by the . This guide provides practical advice and offers a three-level approach for courts adopting AI: strategic planning (level 1); thoughtful project implementation (level 2); and continuous adaptation (level 3). These three levels guide courts from establishing governance and principles to executing measurable, people-centered projects that enhance trust and further the course of justice.

Establishing governance, principles & policies

To unlock AI’s potential while mitigating hazards, courts must first establish a strong foundation through clear governance, guiding principles, and well-defined policies. More specifically, courts should:

Establish governance with a diverse group of voices 鈥� A cross-functional committee sets policy, oversight, and feedback loops. 鈥淎I governance is鈥� really the leadership structure for all of the court’s uses of AI,鈥� says the NCSC鈥檚 , adding that AI Governance Tool in the AI Readiness guide should be used to run a structured 12鈥憁onth plan that covers level 1 readiness steps end-to-end.

Define your operating philosophy before you start 鈥� Establishing guiding principles are not bureaucratic exercises but rather essential blueprints for successful and ethical AI integration.听Without them, courts risk misalignment among stakeholders, the development of systems that do not serve their intended purposes, and the possibility of costly failures. These principles provide a constant reference point, ensuring that as AI projects evolve, the court remains true to its core values and objectives.

Indeed, the overarching mindset that directs actions and choices as part of the governing principles should align stakeholders, manage expectations, and anchor future decisions. 鈥淭he leading cause of software failures historically has been misalignment among stakeholders and changing or poorly documented requirements,鈥� says , Assistant Professor of Computer Science at George Mason University, adding that the same is true for AI projects. 鈥淲ithout these guiding principles [for AI use], there’s the same risk for misalignment among stakeholders.鈥�

Another core tenet of any firm foundation is to set internal rules as part of an AI use policy that provides guardrails and clarity for staff during the transition. And because high-quality, well-governed data is fundamental, pay attention to the quality of the data. 鈥淥ne of the dirty secrets of data science is the data cleansing process,鈥� says Appavoo. 鈥淕arbage in, garbage out.鈥�

Finally, pick projects using workflow analysis and by identifying pain points; then use a scoring matrix to evaluate potential projects based on criteria such as impact and feasibility.

Implementing projects that focus on practicality

After foundational planning is complete, the next stage focuses on the practical implementation of AI projects through productive change management, resource assessment, and strategic procurement. Beyond initial deployment, substantial work occurs during this stage.

The most important element in this phase is that successful AI adoption hinges on a strategic, people-centric approach that carefully considers resources and risk. “When people are engaged early and meaningfully, they stop being subjects of change and start being co-creators and co-designers of it,鈥� explains , Assistant Professor of Art and Design at Northeastern University. 鈥淎nd that sense of ownership is one of the strongest predictors of adoption.”

Indeed, effective change management and prioritizing person-centered design are paramount. Often, this means actively engaging stakeholders, fostering open communication, and providing comprehensive training and support throughout the project lifecycle.

The most important element… is that successful AI adoption hinges on a strategic, people-centric approach that carefully considers resources and risk.

Perhaps the most challenging action in this phase is that courts start moving beyond immediate costs and benefits to better understand the full financial and operational implications of AI projects. This requires an accurate assessment of both tangible and intangible costs, along with clearly defining success metrics.

“What’s really tricky about that is some of those costs are very obvious and simple,鈥� says Dr. Miller. 鈥淪ome of them are very squishy and hard to estimate, and the same goes for the benefits.”

In fact, at this stage there are common pitfalls around cost, according to , Chief of Innovation and Emerging Technologies for Maricopa County, Arizona. “Courts sometimes focus only on the upfront purchase price, or the development budget, and they ignore the updates, the retraining, the legal compliance 鈥� and that can multiply the total cost of ownership.”

Further, courts need to consider their own capabilities, the practicality of their AI solution, its long-term sustainability, and potential risks such as transparency and vendor dependency. If the decision is to buy a product off the shelf, the procurement process and vetting vendors will be key. “If we don’t clarify who’s responsible when the system makes a mistake, we expose ourselves to reputational and legal risk,” Judy notes.

Continuous improvement and preparing for the next AI initiative

After implementing an AI project, the journey does not end. Indeed, it evolves the critical importance of incorporating those lessons learned back into court operations through post-project review.

“It is not about getting in the game when it comes to AI, it is about staying in the game,鈥� says Appavoo. 鈥淭he complexity is actually after you productionize a solution 鈥� that is what we see.鈥� You have to have a human in the loop, stay on top of things in terms of observability, constantly monitor the performance, constantly check the data or the model are not drifting, or the business context is changing, Appavoo explains.

To help put this into practice, the AI readiness guide has comprehensive feedback checklists courts can use to systematically review the foundational AI program elements for ongoing adaptation. More specifically, the post-project review process should examine whether governance structures remain effective, if guiding principles need refinement, and whether internal policies require updates. This continuous improvement approach transforms each AI implementation into a learning opportunity that strengthens the court’s overall AI readiness for its subsequent initiatives.

You can access the from the National Center for State Courts and the State Justice Institute here

Key insights:

• • • Automation and AI can significantly alleviate administrative burdens in courts 鈥� Court professionals may be able to reclaim up to nine hours per week over the next five years, according to research.

    • Courts are under pressure to modernize and meet the expectations of digital natives 鈥� Courts are facing a generational shift in expectations that is pressuring them to adopt more modern tools and technology.

    • Successful implementation of technology requires a thoughtful and collaborative approach 鈥� Collaboration between judges, administrators, and IT staff is essential, and external-facing tools should prioritize user experience to reduce complexity and increase access to justice.

Bringing automation and AI-powered tools to data entry, case-filing processing, and updating court management systems over the next few years could help court professionals use their time more efficiently, according to the听Staffing, Operations and Technology: A 2025 survey of State Courts听from the听成人VR视频 Institute and the National Center for State Courts (NCSC).

Indeed, the report found that alleviating this invisible administrative burden could help professionals reclaim as much as nine hours per week over the next five years. As private sector law firms embrace automated technology, public sector legal departments and courts risk falling further behind.

The time for innovation is now, as caseloads mount, case complexity increases, and retirements and staffing shortages continue to plague courts. Fortunately, administrative professionals are beginning to warm up to targeted automation efforts and AI-powered tools to expand their efficiency.

The cost of administrative burdens

A produced for the Administrative Conference of the United States defines administrative burdens as 鈥渙nerous experiences people encounter when interacting with public services.鈥� And unfortunately, many people do not access the rights or benefits to which they are entitled because of these onerous administrative processes within stressful, frustrating, and overwhelming government systems. In a legal context, administrative burdens hinder access to justice. In fact, low-income Americans did not receive any legal help or enough legal help for 92% of the problems that impacted their lives, according to the Georgetown study.

Recent years have seen a in civil cases. Given this, the processes that were designed for navigation by attorneys and legal and court professionals need to be simplified to reflect the needs of non-professional court users. A on experiences with state courts in particular notes that court users strongly desire courts to be easier to navigate. Even among those who had previous court experience, 50% indicated that it was a little hard or very hard to navigate court paperwork and steps in a case.

A modernizing court workforce

Millennial-aged workers constitute approximately and are the most prevalent court users today and in the foreseeable future. As digital natives, this generation expects modern tools when navigating the legal system.

A commissioned by the NCSC last year found that large percentages of registered voters surveyed support increased use of AI chatbots to answer court FAQs (with 63% saying this), using AI to translate court documents into other languages (64%), and using AI to break down complex legal jargon and make information more accessible (71%).

Further, this lack of modernization in courts has consequences for judges and court professionals as well. Court staff are feeling strained by their workload, and many report simply not having enough time to catch up. More than half (57%) of court professionals and administrative staff reported not having enough time, according to according to the听Staffing, Operations and Technology report.

The report also found that 91% of court staff report working more than 40 hours each week, with about one-third of them working more than 46 hours per week.

Given all this, the pressure courts are under to modernize is understandable; however, it should be looked at as an impetus for improvement: Courts face a once-in-a-generation opportunity to reimagine their workflows.

Resources available to fund statewide technology improvements

Several states leveraged one-time resources available through the to fund major investments in court technology. The , for example, used $38 million to update a two-decade-old in-house case management system. (The AOC is the operations arm of the state court system, which supports 3,000 employees and more than 400 elected justices, judges, and circuit court clerks.) Kentucky courts鈥� AOC selected that offers online tools for judges, circuit court clerks, attorneys, as well as a tool for pro-se litigants.

On the other hand, opted to build its own in-house court management system, as the cost was significantly less than vendor rates. Initial estimates to upgrade a legacy system were $70 million, and Arkansas was able to build its own for $20 million, funded through that came from the state legislature. Indeed, Arkansas has been a leader in court technology for more than 20 years and signed contracts for automated document redaction more than a decade earlier.

The state courts new customized cloud-based solution incorporates multiple vendors, and the development process (now two years underway) has launched Contexte Case Management, an internal facing tool, and , a public-facing case information tool. All and nearly half of district and juvenile courts already have implemented the system.

Moving forward, slowly and thoughtfully

While private sector legal technology has advanced quickly, courts face unique challenges that often make off-the-shelf solutions an inadequate fit. Investment in court modernization must balance the efficiency gained with fiscal responsibility around such investment.

Successful implementation in courts will take cultural, procedural, and budgetary shifts. Internally, collaboration between judges, administrators, and IT staff is essential; and externally, any public-facing tools should center around user experience and ease-of-use, perhaps offering a dedicated customer service team to guide users so that technology reduces complexity rather than adding to it.

The real return on investment in court systems will be realized when all users can access justice more easily, equitably, and reliably.

You can download a full copy of the听Staffing, Operations and Technology: A 2025 survey of State Courts from the听成人VR视频 Institute and the National Center for State Courts听AI Policy Consortium听for Law and Courts here

Key highlights:

• • • Education and licensing misalignment 鈥� Legal education and attorney licensing are misaligned with the real-world skills and practical competencies new lawyers need to serve clients and address the nation鈥檚 growing access to justice crisis.

    • Strong support for licensing reform 鈥� There is strong momentum and support for reforming traditional pathways to legal licensure, according to research conducted by a body of chief justices and state court administrators.

    • Change will require leadership 鈥� Lasting, systemic change requires leadership and collaboration among state supreme courts, law schools, bar examiners, and the practicing bar.

For decades, cracks have widened in the nation鈥檚 promise of justice for all, with millions of people every year unable to find or afford legal help when they need it most. As the legal system in the United States faces a reckoning, one outline for change has emerged with the recently released (CLEAR), a body of chief justices and court administrators from a variety of states across the country. (CLEAR cited support from the 成人VR视频 Institute in the production of the report.)

The CLEAR group is calling for a radical change in how lawyers are taught and licensed. The report cites several factors driving the need for reform, including:

Increases in legal deserts and self-represented litigants 鈥� Judges in courtrooms across the country routinely see self-represented litigants, while so-called legal deserts, especially in rural areas, leave entire communities with few or no attorneys at all. Indeed, according to the American Bar Association, are considered legal deserts, with less than one lawyer per 1,000 people. As a result, most litigants are left to navigate a complex court system with inadequate or no legal assistance in family, probate and estate, housing, consumer, and criminal matters, according to the .

Declining interest in public sector work 鈥� The public interest sector, which includes civil legal aid, public defenders, and prosecutors, is buckling under the weight of crushing caseloads, stagnant federal and state funding, and a persistent shortage of lawyers. Indeed, students face numerous barriers to pursuing a career in public interest law, according to the CLEAR report, from less predictable career paths as compared to private practice, to a perceived lack of prestige in many schools, to the prospect of managing educational loans on a public interest lawyer鈥檚 salary.

Rapid technology changes 鈥� Compounding these challenges, advanced technology and especially AI are rapidly reshaping the legal profession. This, in part, is leading to that are essential for skill development because AI 鈥� which excels in tasks like legal research, writing, and drafting 鈥� now is handling work that had been historically assigned to associates and was a big part of how they learned their craft.

Defining practice readiness and minimum competence

Against this backdrop, the CLEAR report calls for overhauling how law schools educate attorneys and how bar admissions assess attorney readiness. More specifically, the report recommends a sharper, modern definition of practice readiness that more clearly defines the blend of knowledge, skills, and professional abilities that new lawyers must possess to competently serve clients from day one across four essential pillars. These pillars are i) foundational legal knowledge and analytical skills; ii) strong ethics and professionalism; iii) durable communication and interpersonal abilities; and iv) practical legal skills like advocacy, negotiation, and client management.

For the report, CLEAR surveyed of more than 4,000 judges, 4,000 attorneys, and 600 law students; and the committee鈥檚 findings consistently reveal that new lawyers struggle with practical legal skills, which include effective client communication, negotiation, and courtroom advocacy in addition to 17 other skills.

Feedback from survey participants points to the fact that these skills, which are crucial for the daily realities of legal practices, are not taught in law schools to a large degree. For example, only 7%听of experienced attorneys with more than five years of practice report that newly admitted attorneys, most of which are right out of law school, were very well or extremely well prepared to communicate effectively with clients. Likewise, 61%听of experienced attorneys said new lawyers were not well prepared or only slightly well prepared in negotiation, and 55%听of experienced attorneys said the same about new lawyers when it came to questioning and interviewing witnesses.

In addition, 66%听of judges say that new attorneys in their first five years of practice sometimes, rarely, or never competently conducted direct and cross examinations.

New pathways to licensure beyond the bar exam

Meanwhile, an additional insight from the CLEAR report highlights how the bar exam continues to focus heavily on theoretical knowledge and memorization, rather than the practical, day-to-day skills that define minimum competence. At the same time, the is more focused on foundation skills, including legal research, legal writing, and issue-spotting and analysis.

To address the dissatisfaction with the traditional bar exam, some states have been piloting innovative licensure pathways that better align with the skills new lawyers need. Such approaches include curricular pathways, such as in the in New Hampshire, and at the University of Wisconsin鈥檚 law school. Other methods are supervised practice models, such as in Oregon鈥檚 , , and temporary pandemic-era alternatives that provided graduates with the ability to prove their competence under the guidance of experienced attorneys.

Top recommendations for state supreme courts

The CLEAR group advocates for state supreme courts, as the profession鈥檚 primary regulators, to lead and foster innovation in licensure and practice readiness. The report urges state supreme courts to take such action as:

Lead collaborative efforts to realign legal education, bar admissions, and new lawyers鈥� readiness with public needs 鈥� State supreme courts are uniquely well-positioned to lead efforts to create a legal system that better addresses the legal needs of the communities they serve.

Encourage law school accreditation that serves the public 鈥� State supreme courts should encourage an accreditation process that promotes innovation, experimentation, and cost-effective legal education geared toward the goal of having lawyers meet the legal needs of the public.

Reform bar admissions processes to better meet public needs 鈥� This reform includes adjusting bar admission by setting passing scores based on evidence and piloting alternative pathways to passing the exam or equivalent assessment.

To put CLEAR鈥檚 recommendations for state supreme courts into practice, however, bold, coordinated action by law school administrators and the American Bar Association (as the accreditor of law schools) are critical as well. In particular, there is a need for expansion of experiential learning, such as clinics, externships, and simulation courses, to help students gain meaningful, hands-on experience and have direct responsibility with clients. In addition, aligning curricula with the realities of practice by integrating practical skills, ethics, and professional identity formation throughout, rather than relegating those factors to optional or add-on courses is another necessary reform.

Legal education and licensing must rapidly evolve to meet the nation鈥檚 urgent access-to-justice challenges, the CLEAR report notes. Law schools and state supreme courts must work together with renewed urgency and vision to lead this transformation. The failure to act by both law schools and courts means the justice gap in the US will only widen. Only with urgent, collaborative innovation to enact these changes can the legal profession deliver on the promise of justice for all in the decades to come.

You can access the full here