Key findings:

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

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

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

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

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

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

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

Far-reaching consequences

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

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

To learn what students are asking for and what the legal profession must do next, you can access a full copy of the white paper here

Key points:

• • • AI capability is jagged 鈥� Ethan Mollick’s frontier metaphor describes a coastline of strengths and weaknesses, in which a model that excels at contract analysis can fabricate a citation in the same conversation.

    • Human intelligence is jagged too 鈥� A century of psychology, from multiple intelligences to the Big Five, shows that each lawyer has their own coastline of strengths and weaknesses.

    • Person-AI fit is the next discipline 鈥� Firms that take this seriously will move from one-tool deployments to portfolios that match each lawyer to an AI partner whose jagged edges meet theirs.

Welcome back to The AI Law Professor. Last month, I examined how AI first drafts can blind us to other lines of reasoning and hijack our legal judgment. This month, I want to take up what determines whether an AI works for any given lawyer at all: Not which model is best, but which model is best for this lawyer, on this kind of work, at this point in their career

Professor and author gave us the metaphor that started this conversation 鈥� the jagged frontier of AI capability. Picture a coastline, irregular and unpredictable. On one side, the model is capable; on the other, it fails, sometimes catastrophically. The line itself does not run where you expect. Tasks that look hard turn out to be easy, and tasks that look easy turn out to be hard.

In terms of legal work, this means that a model that has just produced a useful contract analysis will confidently invent a citation. A model that has summarized a 90-page deposition with insight will fail at basic arithmetic. The capabilities of AI form a coastline, with bays and inlets and the occasional cliff. Mollick’s contribution was to give us a way to see this clearly. AI is not uniformly competent or uniformly incompetent 鈥� rather, it is jagged.

Humans are jagged too. Psychology has been telling us this for a century, although the message is uncomfortable enough that we keep flattening it back into a single number. The single-number version is IQ; yet the deeper issue with IQ is that it pretends intelligence is one-dimensional.

Developmental psychologist Howard Gardner’s , whatever its empirical limits, points us toward a more honest picture, one in which linguistic, logical-mathematical, spatial, musical, interpersonal, intrapersonal, and kinesthetic intelligences, are each largely independent. People are not equally strong across all these dimensions. So, it follows that a great trial lawyer and a great patent lawyer are drawing on different intelligences, and each could be lost in the other’s territory.

Human intelligence, like AI capability, is jagged, and each of us has an edge. The jaggedness is not a flaw to be smoothed; rather, it鈥檚 a feature of being a unique individual.

When two jagged edges meet

Place the two coastline maps 鈥� the human and the AI model 鈥� side by side. Press them together at random and they grind, with gaps where neither side fills the space and ridges where both claim the same territory. The lawyer’s strength overlaps with the AI model’s strength, so neither is amplified. The lawyer’s weakness overlaps with the model’s weakness, so neither is covered. The pair produces less than either party would produce alone.

However, align the same two surfaces with attention to their contours and something different happens. The peaks of one fit the valleys of the other. The lawyer’s weakness is met by the model’s strength; and the model’s weakness is met by the lawyer’s strength. The pair becomes more capable than either party alone.

A law firm that takes this seriously will not deploy a single AI tool across all of its lawyers and call the rollout complete. It will offer a portfolio of models and configurations and help each lawyer find the AI partner that works with their actual mind.

Every foundational model now ships with a model card, a document describing the model’s intended uses, training data, performance characteristics, and known limitations. The cards exist because models are not interchangeable. Read three of these cards side by side and the matching question becomes clear. A cautious generalist that hedges and flags uncertainty fits a lawyer who already holds strong views and wants a partner that will test them. A citation-anchored specialist that refuses to invent cases and stays grounded in retrieval fits a lawyer in heavily regulated practice areas in which errors are catastrophic.

The matchmaking discipline

Organizational psychology has worked on a version of this problem for 50 years under the . When a person’s strengths, values, and working style align with the demands and culture of their role, performance and well-being both rise. When they misalign, performance drops and burnout follows.

The same logic applies to person-AI fit. On the human side, cognitive style, domain expertise, personality profile, and the actual tasks performed in a typical week are key. On the AI side, behavior under different prompt styles, default tone, willingness to push back, hallucination patterns, and the shape of strengths and weaknesses across the practice areas in question may matter most. Yet, law firms are still treating AI procurement as a software decision rather than a partnership decision.

A law firm that takes this seriously will not deploy a single AI tool across all of its lawyers and call the rollout complete. It will offer a portfolio of models and configurations and help each lawyer find the AI partner that works with their actual mind. The first generation of legal AI has been dominated by the question of which model is best; however, the second generation will be dominated by a different question: Not which model, but which pairing works best. Not capability, but fit.

Those lawyers that flourish with AI will not necessarily be the most technical or the most enthusiastic users. Instead, they will be the ones that found, by luck or by design, an AI partner whose jagged edges meet theirs.

When two jagged intelligences fit well together, they can accomplish more than what either 鈥� human or AI 鈥� could do alone. Today, fit is the frontier.

Tom Martin is CEO & Founder of LawDroid, Adjunct Professor at Suffolk University Law School, and author of the forthcoming

听

Key insights:

• • • Accountability, not adoption, is the central governance challenge听鈥� With many institutions using AI a variety of tasks, informal “shadow AI” use is expanding without consistent oversight.

    • Justice systems now face a parallel governance problem 鈥� They must find a way to regulate AI while using AI inside the institutions that enforce rights, while allowing responsible innovation that improves efficiency and access to justice.

    • AI needs to be integrated into broader justice reform听鈥� Without strong data governance and clear boundaries between AI assistance and legal judgment, courts risk automating inefficiency, deepening inequities, and undermining public trust.

Even as AI governance frameworks remain mired in ongoing debate, justice systems are moving ahead with implementation. Courts and dispute resolution institutions are integrating AI into their operations to more efficiently digitize records and automate workflows.

This introduces the very real challenge of parallel governance. We must now determine not only how AI should be regulated, but how it operates within the very institutions responsible for enforcing rights.

And this intersection is no longer theoretical: Does AI governance strengthen fairness, preserve independence, and expand access 鈥� or does it undermine their very foundations?

From experimentation to embedded use

Across jurisdictions, AI is often framed as an administrative tool that can handle basic tasks such as transcription, translation, case triage, and more, as well as providing analytics to identify delays or inefficiencies.

These applications respond to real constraints, such as overburdened courts, limited resources, and persistent backlogs. Similarly, dispute resolution platforms are integrating AI to guide users through processes and structure negotiations.

However, this formal adoption tells only part of the story. AI is also entering justice systems informally. Judges, clerks, and lawyers are independently using general-purpose tools in their daily work, often without guidance, oversight, or a clear grasp of the tools鈥� implications for security, confidentiality, and discoverability. As one expert observed: 鈥淪hadow AI is already happening.鈥�

The absence of governance does not prevent AI use; and, in fact, it may encourage misuse. This shadow AI simply pushes AI usage into unstructured and unmonitored areas 鈥� the risk then becomes not adoption itself, but uneven adoption that evolves beyond institutional control.

It鈥檚 no longer a question that justice systems need to engage with AI; however, that engagement has be done deliberately and in a way that allows governance frameworks to keep pace without constraining beneficial use.

While it鈥檚 no longer a question that justice systems need to engage with AI, that engagement has be done deliberately and in a way that allows governance frameworks to keep pace without constraining beneficial use.

Automating inefficiency?

Efficiency is often the entry point for AI in justice systems; but efficiency alone is not reform. And misapplied efficiency can often lead to its direct opposite: a scramble to repair broken systems or to plug technology and personnel gaps.

Many current AI initiatives remain isolated pilots 鈥� layered onto existing processes rather than integrated into broader institutional strategy. Without addressing underlying structural constraints like fragmented data, inconsistent procedures, and uneven infrastructure, AI risks automating inefficiency rather than resolving it. And without strong data governance, infrastructure, and institutional alignment, even well-designed AI tools will underperform or produce unreliable outcomes.

That means that efforts to tightly control AI deployment without addressing these foundational issues risk focusing on symptoms rather than the system itself. AI should not function as a parallel modernization effort; rather, it must align with broader justice system reform.

Clearly, the most consequential questions arise when AI tools begin to shape legal reasoning or outcomes. And while there is broad agreement that AI can support judicial work without replacing independent human judgment, in practice, however, the boundary between assistance and influence is not always clear.

Even administrative tools can shape decisions. Summaries may omit nuance, or suggested language can influence framing. Over time, reliance on system outputs can create subtle forms of dependency. In fact, this dynamic is compounded by what has been described as the myth of verification 鈥� the assumption that human oversight alone is sufficient. In reality, time constraints, cognitive bias, and limited technical fluency can make meaningful review difficult. And automation bias affects even experienced decision-makers.

Overall, these boundaries require deliberate definition. Left on their own, AI tools and their outputs will be shaped implicitly through practice rather than through principled governance.

Design determines outcome

Institutional capacity will determine how these dynamics play out because digital maturity varies widely across jurisdictions. Some courts operate advanced platforms, while others remain largely paper based. In lower-resource environments, infrastructure may not support even basic digitization. In more advanced systems, adoption may outpace governance.

Yet, one consistent challenge among all jurisdictions is reliance on external vendors. Without internal expertise, institutions risk adopting tools that meet technical requirements but fall short of rule-of-law standards, particularly in transparency, accountability, and data governance.

Justice systems are not neutral environments for technology adoption 鈥� they are the operational core of the rule of law.

Addressing this gap requires more than a procurement issue. It requires institutional literacy. Judges and administrators need a working understanding of how AI systems function, where risks arise, and how to evaluate them. Training efforts are underway, but scaling this capacity will take time. In the interim, governance gaps will persist and attempts to compensate for these gaps through overly rigid restrictions may limit adoption but do little to build the institutional capability required for effective oversight.

From adoption to accountability

Clearly, AI will not improve justice systems by default; rather its impact will be determined by institutional design, which includes clear boundaries on use, transparency around deployment, safeguards to protect independence, and mechanisms for oversight and accountability. It also requires alignment with broader justice system goals of efficiency, fairness, and accessibility.

Yet, justice systems are not neutral environments for technology adoption. They are the operational core of the rule of law. Their legitimacy depends on trust, which in turn requires accountability.

This makes the path forward not purely a technical one. It requires institutional self-assessment, alignment with human rights frameworks, and collaboration across policymakers, courts, technologists, and the public. The measure of success will not be the sophistication of the tools deployed, but whether they strengthen the system鈥檚 core functions of impartiality, accessibility, and trust.

AI tools can support those goals, of course, but only if they are designed into justice systems from the outset.

You can find other installments of听our Scaling Justice blog series here

Key insights:

• • • Consensus among AI systems is not the same as correctness 鈥� Agreement between AI models often signals shared blind spots, not truth; and AI errors can be highly correlated across instances and even across model families.

    • Productive disagreement must be explicitly designed into AI advisors 鈥� Multi鈥慳gent AI systems are most effective when they are intentionally built to preserve meaningful disagreement, not just to synthesize a unified response.

    • The future of AI advisory mirrors long鈥憇tanding human decision-making 鈥� Modern multi鈥慳gent AI design has a long historical lineage; yet, across all examples, the same principle holds: The best decision systems are engineered for internal conflict.

In this new two鈥憄art blog series, we explore why AI works best as an executive advisor not by delivering consensus answers, but by being intentionally designed to identify, preserve, and productively leverage disagreement. In the first part, we saw why a single AI advisor is structurally vulnerable; now, in this concluding part, we look at what happens when you design disagreement on purpose.

The academic evidence for multi-agent AI systems has been building rapidly, and the most important findings aren’t about the power of agreement. They’re about the danger of it.

In February, , a product that sends every query simultaneously to three frontier AI models (Claude, GPT, and Gemini) then uses a fourth chair model to synthesize a unified answer. The product’s value proposition isn’t that three models produce a better answer than one; rather, it’s that divergence between models is treated as a signal. When models converge, that indicates confidence, but when they diverge, that indicates the user should slow down.

Studies have borne this out. Multi-agent debate compared to single-model generation, and researchers at the University of G枚ttingen found that , with their voting protocols outperforming other decision structures. However, potentially the most important finding cuts against the hype. In a 2026 paper, , the authors demonstrated that AI model errors are highly correlated both within and across model families. When three instances of the same model agree, it doesn’t mean they’re right, rather it means they may share the same blind spots. Aggregation increases consensus faster than it increases truth.

The future of AI-assisted executive decision-making may look less like a single brilliant oracle and more like a room full of advisors that may often disagree because that’s how the best decisions have always been made.

This finding cuts both ways for practitioners like 成人VR视频 enterprise architect Zafar Khan and his two AI advisors, Adrian and Elara, that were built on the same underlying model but differentiated by their analytical frameworks rather than their architecture. The divergence they produce is real and visible. For example, the analysis the two AI advisors did on a deal undertaken by Eaton Corp., in particular generated genuinely different conclusions because the two advisors were oriented towards different priorities.

Yet, research suggests that same-model divergence, while effective, has a ceiling. Prompt-driven personas can ask different questions, but they share the same training, the same blind spots, and the same failure modes. Khan is candid about this, noting that his current system is in the 鈥渧ery early鈥� stages and is not a finished product. The value right now, he says, isn’t that Adrian and Elara are equivalent to truly independent minds, it’s that even a first-generation version of structured disagreement can identify insights that a single advisor would miss. It鈥檚 a large stride rather than an arrival at the ultimate destination.

The future of AI advisory is in the past

The principle behind this diverging analysis concept isn’t new. Indeed, it might be one of the oldest ideas in institutional design, rediscovered independently by many institutions that had to make decisions under uncertainty. Socrates built a philosophical method around cross-examination; Pope Sixtus V formalized opposition by creating the Devil’s Advocate in 1587; and the RAND Corporation operationalized it during the Cold War with the Delphi Method, using structured anonymous iteration to prevent groupthink.

The through-line across two millennia is simply that the best decision-making systems don’t minimize disagreement, rather, they engineer it.

Today, the developer community now uses production-grade code review tools to assign architecture, security, and functionality analysis to separate agents, using majority voting for routine decisions and unanimous consent for irreversible ones. And what Khan has built and what Perplexity, Microsoft’s Agent Framework, and a growing ecosystem of multi-agent tools are now pursuing, are the latest iterations of the simple concept: Internal conflict is not a system failure, it is a design requirement.

The question is no longer “whether”

Khan’s vision for what should sit at the decision table is specific 鈥� five AI advisors spanning technology, finance, regulation, workforce, and geopolitical risk. Each applies its own analytical framework, with the human executive responsible for integration and final judgment. The guardrails are three: i) transparency about what data the system uses; ii) verifiability that sources are legitimate; and iii) human accountability at every decision point.

“The race towards AGI [artificial general intelligence] is moving faster,” Khan acknowledges, adding that the human needs to be in the loop in order to bring AI to work in a governance fashion and an ethical way.

“I want to show the interaction between human and AI advisor, how they’re thinking through the problem together,” he explains. “Where the human judgment covers the analysis and where it diverges.” In other words, when the AI advisors agree, that’s your green light. When they diverge, that’s the conversation your board should be having.

The future of AI-assisted executive decision-making may look less like a single brilliant oracle and more like a room full of advisors that may often disagree because that’s how the best decisions have always been made. The technology to build that room now exists; however, the question is whether today鈥檚 leaders have the discipline to listen when the room argues back.

For more on AI transformation in the professional services market, you can download the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report

Key highlights:

• • • AI simulation can replace the “repetition loop” used to train junior lawyers 鈥� AI is taking over the repetitive work junior lawyers used to learn from and replacing it with simulation-based learning.

• • • Three design pillars can determine whether AI simulations will work 鈥� The best simulation tools are built around three pillars: clear learning goals, realistic unpredictability, and specific feedback.

• • • AI simulation tools offer law students spaces to fail 鈥� For law students and junior lawyers, simulation creates a rare low-risk space to practice, make mistakes, and improve.

For decades, junior lawyers learned by doing. Assignments landed on their desks, senior lawyers marked them up, and judgment accumulated through repetition and proximity to experience. Now, as AI takes over these foundational tasks, that repetition loop is breaking down, according to , which underscores how junior lawyers are being thrust into higher-level advisory work far earlier in their careers. Unfortunately, this is occurring before they have developed the instinctive gut feel for judgement that only comes from years of experience.

and , co-founders of legal training platform , and , Executive Director at the Stanford Law School鈥檚 (liftlab) all say they see the need to build new educational programs and pedagogical tools. And these learning capabilities must be heavily focused on the specific skill sets that underlie the judgment of drafting and the judgment of taking a deposition, explains Dr. Ma.

AI and the cultivation of legal judgment

The broken repetition loop demands a substitute that underscored the implicit teaching of legal judgement in the early years of practice. Simulation-based learning is the profession’s most promising answer, and the idea predates AI.

Moot courts and mock trials have existed for years because of the stark difference between understanding something in theory and executing under pressure. Historically, however, simulation was costly as delivering experiential learning to small groups required significant expertise and time from multiple individuals. AI changes that equation by offering scalability at a level the legal profession never could access before. Indeed, role-playing is one of the greatest strengths of AI models, says Dr. Ma.

The traditional dynamic in legal education, in which law schools teach lawyers how to think, and law firms teach lawyers how to practice is no longer tenable as AI-enabled legal practice grows.

Legal judgment has always been difficult to define and nearly impossible to teach directly. Partners describe it as instinct or as something accumulated after enough transactions, depositions, and hard experience. AI simulation 鈥� if designed with enough precision to force real decision-making 鈥� can create the repetitive environments in which that judgment can be developed.

These AI simulation tools work best when designed around three pillars: i) clear learning goals; ii) realistic unpredictability; and iii) specific feedback.

First, a rubric tied to clear learning objectives needs to be established. According to AltaClaro鈥檚 Liles, this rubric must be paired with a feedback loop that鈥檚 anchored to specific skills and expected judgment calls. AltaClaro has been offering online, simulation-based training to the Am Law 200 for almost a decade and uses AI-powered feedback in its simulation tools.

Second, realistic unpredictability needs to be built in. For example, AltaClaro’s uses a lightly scripted framework that gives the witness a fixed truth and significant freedom within it, offering a scenario with enough unpredictability to force adaptation. This non-determinism makes AI outputs difficult to control in some contexts and becomes the source of realistic pressure in a simulation. The tool currently covers commercial and employment litigation deposition simulations, and there are plans to roll out other deposition scenarios, including IP, securities, mass tort/product liability, and antitrust over the next six months.

To further enable adaptation, Dr. Ma and her team inserted personality dials into liftlab鈥檚 deposition simulation tool. Instructors can push a witness toward the extreme of forgetfulness, evasiveness, or hostility. The user must find a path through behavior that no script could have anticipated. Repetitive use of these tools allows the instinctual learning of legal judgement. Similarly, DepoSim, which uses as its underlying engine, also allows for adjustments in witness cooperation or hostility and the opposing counsel’s aggressiveness.

Finally, feedback is the third critical design pillar. Both tools evaluate the user鈥檚 performance with feedback, which can include instances in which the attorney held their ground, or in which a vague answer was allowed to slide, or when an opening to gain ground was missed entirely. Feedback of this specificity is what allows simulations to most mimic practice and transform repetition into learning.

AI simulation tools work best when designed around three pillars: clear learning goals; realistic unpredictability; and specific feedback.

Of course, user experience is the design element that determines whether all of the above actually gets used. Shayesteh describes the range of ways the DepoSim tool is being used in practice to teach judgement. For example, one litigation chair ran the tool as a live teaching demonstration in front of 500 attorneys and paused to narrate decisions as events unfolded on screen. Also, mentor-mentee pairs are using the tool’s embedded feedback as the foundation for coaching conversations; and associates with upcoming real depositions are using the tool for targeted preparation.

AI simulations in law schools

The traditional dynamic in legal education, in which law schools teach lawyers how to think, and law firms teach lawyers how to practice is no longer tenable as AI-enabled legal practice grows. Dr. Ma says she sees simulation fitting naturally into existing experiential courses such as negotiation workshops, trial advocacy classes, and mediation seminars, serving as a between-class practice layer.

Of course, the greatest benefit of AI simulations in law schools is the creation of safe spaces for students to fail, Dr. Ma notes, describing how the law offers very few environments in which failure carries no consequences. Encountering transactions that go wrong, learning to manage impossible witnesses, and experiencing negotiations that collapse in a controlled setting are invaluable experiences for future lawyers 鈥� and now they can be experienced through simulations.

Although signs of progress are visible across the profession, resistance remains entrenched. “The profession needs to wake up and look at training as a really core strategic piece of the [learning] process,” Lilies says, adding that without intentional, rubric-based simulation infrastructure, the default is handing associates a set of AI tools and pointing them toward the work. This approach produces productivity without judgment and will result in lawyers generating AI output without a full understanding of what makes it right or wrong.

As AI tools proliferate across legal workflows, legal education needs to transform in tandem. “Law schools have to embrace this to really prepare students for the world that is three to four years away, by giving them the opportunity to increase reps and receive feedback based on a structured rubric and framework,鈥� explains Shayesteh. 鈥淚t is the best gift you can give them.”

You can find more about the

Key insights:

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

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

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

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

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

A new model for learning

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

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

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

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

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

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

Leadership in an AI age

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

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

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

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

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

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

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

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

The new rules of professional growth

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

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

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

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

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

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

Key insights:

• • • AI as a design decision, not just a tech add-on 鈥� AI gives us a chance to rethink the 鈥渕achinery of justice鈥� and redesign it for today鈥檚 needs rather than simply automating existing systems and processes.

    • AI to expand access and usability, without replacing judgment 鈥� The most promising value is in reducing friction for litigants and helping people navigate the process.

    • Progress requires disciplined, court-by-court experimentation 鈥� We can start small, build AI literacy, set leadership tone, invite diverse perspectives, and address legal and ethical issues as design constraints, not deal-breakers.

Today, interest in AI across the judiciary is clearly growing, but most discussions are still constrained by certain fears:

• • • Fear that AI will replace human judgment 鈥� This concern is legitimate, but it focuses almost entirely on endpoints. Judging (and the systems around it) involve far more than final decisions. Focusing only on high-stakes endpoints misses much of what judges and courts do day-to-day.
    • Fear of hallucinations, errors, and bias 鈥� These are also legitimate fears, but there are ways to mitigate these risks, which are not new. The source may be different, but we have long needed to protect against errors, bias, and misstated law.
    • Fear of change 鈥� This is a difficult one to overcome, but a desire to protect the status quo sometimes presupposes that the system as it exists today is working exactly as it should. It isn鈥檛. At least not for everyone.

I鈥檇 like to see the narrative shift from fear of AI in courts, to the possibilities of AI in courts. AI presents a rare opportunity to upgrade the machinery of justice.

Justice as machinery

Most of us were taught to think about justice as an outcome, something the system delivers. However, justice is also the machinery we use to deliver it, and that machinery is a set of design choices. Rules, procedures, forms, hearings, briefs 鈥� we crafted these frameworks to manage conflict and produce decisions that feel fair and legitimate. Like most frameworks, they reflect the era in which they were built.

Once we start thinking about justice as something to be designed rather than simply delivered, the access-to-justice problem looks different. The question is no longer how to get more of the current system to more people; rather, it鈥檚 whether the machinery itself is still fit for its purpose.

Reimagining the machinery

The machinery has been redesigned before. Justice was once deeply human because it had to be: Law lived in minds, judges traveled from town to town, decisions were announced aloud. That system was more human and personal, but it was limited, exclusionary, and fickle. It was dependent on local norms and personal relationships. It yielded uneven outcomes.

The first great upgrade was writing, and more importantly, the printing press. It brought stability and protected litigants from arbitrary local power. But it also entrenched a new kind of authority. Yet, understanding it required literacy, training, and expertise. A professional bar emerged and ordinary people were pushed further from the center of their own disputes. Then came the digital age. It optimized the process and made more information available. But many people feel overwhelmed by the deluge of information and experience modern justice as a series of obstacles.

Does AI present a different kind of opportunity? Could it deliver an upgrade that finally closes the gap rather than widens it? I鈥檓 optimistic that the answer is yes, but our design choices matter and we have to be willing to reimagine justice from the ground up.

What if every litigant had access to an AI agent that could help them navigate forms, understand the process, and translate legalese? What if AI could take messy human stories and translate them into structured information for the court? What if courts offered AI-assisted dispute resolution in the early stages of litigation or at key milestones during the litigation? Can AI make navigating the legal system feel less like data entry and more like a conversation?

We鈥檙e not ready for giant leaps, and we can鈥檛 ignore the open questions: Unauthorized practice of law issues, privilege and work product implications, the reliability of AI-assisted work product, and more 鈥� but these are not dead ends. They鈥檙e current design constraints to account for, and they shouldn鈥檛 keep us from reimagining what鈥檚 possible.

Where do we start?

The institution of justice will not be redesigned overnight, and there is no central authority to drive change. Rather, it will be redesigned court by court. The principles below apply broadly and reflect a starting point for thinking about AI as a design decision, not just a technology decision.

Set the tone from the top听

Fear can be paralyzing, and in courts it often is. If judges and court staff are afraid to experiment, nothing moves. We need environments in which thoughtful, controlled experimentation is encouraged and supported. When more people are engaged in testing ideas and thinking about how to improve their processes, the likelihood of meaningful innovation and redesign increases.

Court leadership can create that space by setting a vision, encouraging responsible experimentation, and supporting innovative mindsets.

Build AI literacy

Encouraging experimentation is an important first step, but it can create risk if not paired with the right training and education. AI requires new competencies in prompting, guardrail development, output verification, bias awareness, iteration, context framing, documentation for audibility, fit-for-purpose judgment, and more. As tools evolve, education should evolve, too. Agentic AI, for example, will require a different set of skills and a different type of supervision than we鈥檙e accustomed to now.

For more information about toolkits and resources around AI in courts, visit

Judges and court staff do not need to become technologists, but they need enough training and education to ask the right questions, spot the right issues, and use the tools responsibly.

Rethink the systems, not just the tools

This one is critical. Currently, most conversations about AI focus on use cases, such as whether AI can assist with research or automate certain workflows. These are good questions, but the tougher questions will lead to bigger rewards. Where are our pain points? What can we do better? Which policies and processes are essential, and which have never been re-examined? Which parts of the machinery were built for a different era and have outlived their usefulness? And perhaps most importantly, who is the system failing?

We shouldn鈥檛 start with the technology and look for places to apply it. We should start with the people we serve and ask how the technology can help us serve them better.

Invite diverse perspectives

The strongest ideas emerge from the push and pull of different viewpoints. Court leadership can form committees that bring together innovators and skeptics, technologists and traditionalists, those who are excited and those who are concerned. We also need perspectives across different court functions. AI is not something to hand off to IT departments. They are essential partners, but the questions AI raises go far beyond any one department.

Outside perspectives are helpful, too. Many people across the country are already approaching this work with a multidisciplinary lens, and courts can draw on that experience.

Finally, remember to start small

It鈥檚 easy to create so much process and deliberation that progress slows. We need concrete steps that move us forward, however incrementally. Start with policies and data governance, then move to small, targeted pilots that can address low-hanging fruit. Small adjustments can help teams become comfortable with change; and early wins build confidence and create momentum.

Closing thoughts

Justice has been redesigned before, and it is on the brink of being redesigned again. AI will reshape courts whether or not we participate. However, as the people who know the system from the inside and want it to work for everyone, we may be in the best position to guide the next upgrade. The chance to build something more equitable, more accessible, and better designed for today鈥檚 world does not come around often, let鈥檚 not miss it.

You can find more insights from Judge Braswell here

Key insights:

• • • As a single answer鈥憁achine, AI may be unsafe for executive decision鈥憁aking 鈥� Treating AI as a tool that delivers one authoritative answer makes it easy to either ignore any advice you don鈥檛 like or exploit advice you do like, both of which can lead to major failures.

    • AI works better when designed as a panel of disagreeing personas 鈥� Instead of providing consensus answers, AI systems need to be intentionally designed to identify and preserve disagreement.

    • Disagreement is the insight 鈥� AI advisors should not replace executive judgment. Rather, its role should be explicit: it produces analysis, not decisions; and human leaders remain responsible for synthesizing competing viewpoints and making the final call.

In this new two鈥憄art blog series, we explore why AI works best as an executive advisor not by delivering consensus answers, but by being intentionally designed to identify, preserve, and productively leverage disagreement

AI has arrived at the executive table. Albania has one in its cabinet to evaluate government procurement contracts. 成人VR视频’ CoCounsel is already helping attorneys navigate emerging case law and draft legal strategies for high-stakes, bet-the-company work. And in boardrooms that will never make headlines, leaders are quietly consulting AI on decisions that move millions of dollars around every day.

It doesn’t tend to make the news when it goes well. When it goes badly, however, it makes very big news: like a gaming CEO who bypassed his own legal team, asked ChatGPT how to dodge a $250 million bonus payout, followed its step-by-step plan, and a month ago.

The instinct most executives have (and most AI products encourage) is to treat AI as a source of answers. Ask a question, get a response, act on it or don’t. The emerging evidence, however, points somewhere more complex: AI advisors aren’t at their best when they’re telling you what to do. They may be at their best when they’re telling you what you don’t want to hear or better yet, when they’re arguing with each other and forcing you to understand why.

This is not how most organizations think about AI. Most executives today are still using the technology as a faster way to draft emails or summarize meetings, what 成人VR视频 enterprise architect calls “an automation mindset, not intelligence.” Yet, a small and growing number of practitioners, researchers, and product teams are converging on a radically different model: AI not as a single oracle delivering answers, but as a structured advisory panel designed to argue with itself.

The instinct most executives have (and most AI products encourage) is to treat AI as a source of answers: Ask a question, get a response, act on it or don’t. However, the emerging evidence, however, points somewhere more complex.

Khan is one of them 鈥� and in the interest of transparency, he’s also a colleague; this story started as an internal conversation at 成人VR视频. However, the research landscape it uncovered extends well beyond any one company’s work, and it suggests Khan is onto something that ancient Greek mathematicians, the Catholic Church, and Cold War military strategists have all independently arrived at.

What disagreement looks like in practice

When Eaton Corp. announced a $9.5 billion acquisition of a thermal management company earlier this year, Khan ran the same news through two AI advisors he’d built to seek analysis of the deal. 鈥� a CTO-minded persona trained on architecture teardowns and engineering post-mortems 鈥� produced an infrastructure thesis, determining why someone would buy the cooling layer of the AI economy, and how computing demand is scaling and constrained by physics. A second AI advisor, 鈥� a CFO-minded persona drawing on earnings transcripts and filings with the U.S. Securities and Exchange Commission (SEC) 鈥� questioned whether the acquisition math actually holds and what capital cycle was driving the demand.

Same news. Two genuinely different reads. The value isn’t that either analysis was definitively right, it’s that a leader which can see both would ask different questions than one seeing either analysis alone. 鈥淭hat’s how two different minds work,鈥� Khan says. 鈥淭hey need to work together in order to bring their insights to bear on decisions.鈥�

Adrian and Elara aren’t chatbots. They’re fully realized AI personas with names, faces, voices, and their own YouTube channels publishing weekly video analysis. Both are built on agentic workflows that Khan developed alongside his book . Both are transparent about what they are. Both carry the same disclaimer in their own words: The synthesis is mine. The judgment call on what matters is human.

And when Khan posed to both a more difficult scenario 鈥� Should a leadership team accelerate an AI rollout? 鈥� the value of their divergence sharpened further. Elara’s response cut directly to the blind spot a technology-focused advisor like Adrian would miss: 鈥淎drian says the system is ready,鈥� Elara stated. 鈥淚 say the financial model isn’t ready for what happens when the system works. Don’t pick a winner. The disagreement is the insight. It tells you exactly where the risk sits.鈥�

What happens when there’s no disagreement

If structured disagreement is the goal, the failure mode is its absence. We have fresh evidence of what that costs.

This is not how most organizations think about AI. Most executives today are still using the technology as a faster way to draft emails or summarize meetings. Yet, a small and growing number of practitioners, researchers, and product teams are converging on a radically different model.

A month ago, a Delaware court ruled against Krafton, the South Korean gaming company behind battle royale video game PUBG, after its CEO bypassed his own legal team to ask ChatGPT how to avoid a $250 million earnout payout to one of its studios. His head of corporate development had warned him that firing the studio’s founders wouldn’t void the earnout and would invite a lawsuit. He didn’t want that answer. So, he found an AI that gave him the one he wanted: A detailed, multi-stage corporate takeover strategy dubbed Project X., which he executed to the letter.

Unsurprisingly, a court battle ensued and in the end, the court ordered the fired studio head reinstated and noted that executives must exercise “independent human judgment,” not outsource good-faith decisions to a chatbot.

Khan wrote about the mirror image of this failure mode before it happened. In the opening chapter of his book, a fictional company called Rev Motors ignores its own AI model’s warnings about an adverse weather event. Leadership refused to spend millions preparing for a hypothetical scenario, and it nearly cost them more than $1 billion in damage.

These scenarios are two sides of the same coin: the fictional Rev Motors had leaders dismissing AI that disagrees with them; and the real-world Krafton had a leader seeking out AI that agrees with him. In both cases, the root cause is the same: A system with no structural mechanism for surfacing and preserving disagreement.

So clearly, a single AI advisor is structurally vulnerable to both failure modes. It can be ignored when its advice is inconvenient and exploited when it tells you what you want to hear. The question is whether there’s a better architecture鈥� and increasingly, the research is saying yes.

In the second part of this series, we鈥檒l look at what the research says about multi-agent debate, why consensus can be a trap, and what a real executive AI advisory panel could look like in practice.

For more on AI transformation in the professional services market, you can download the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report

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�

You can explore the white paper听 here

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.

For judges ready to engage, visit听听to join the conversation

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.

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