• • • Collaboration is the strongest enabler of detection 鈥� Financial institutions are most effective at identifying human trafficking when they work closely with NGOs, law enforcement, and regulators, combining financial intelligence with victim-centered and investigative insights.

    • Data, technology, and AI can uncover trafficking networks 鈥� By analyzing financial transactions alongside open-source intelligence, social media activity, public records, and specialized datasets, organizations can identify patterns, relationships, and high-risk accounts more efficiently.

    • Financial institutions have a critical role in disrupting trafficking 鈥� Because human trafficking depends on moving and laundering illicit profits, banks and other financial institutions can help stop it by detecting suspicious activity, filing targeted reports, and supporting law enforcement investigations.

Human trafficking is not only one of the most devastating financial crimes but also one of the most complex as it cuts across fraud, money laundering, and organized crime, with some crime rings use their existing drug trafficking networks for human trafficking-related crimes.

Financial institutions are in a unique position to help battle this scourge as they can see the financial flows generated from human trafficking and sexual exploitation. Without the ability to launder the proceeds, human trafficking as a crime would lose some of its appeal.

To understand this better, a multi-city initiative around the FIFA World Cup, co-led by 抖阴成年 and , brought in leaders from financial institutions, law enforcement, non-governmental organizations (NGOs), regulators, and corporate risk departments to address human trafficking from a financial crime perspective.

Indeed, as research shows, forced labor in the private economy generates as much as $236 billion in , according to the International Labour Organization. If financial institutions can identify the proceeds of traffickers and their patterns, however, they can close suspected accounts, file prioritized suspicious activity reports, and notify law enforcement to help put a quicker end to this terrible problem.

The use of data and technology

Unfortunately, financial institutions often lack the context and the data points to act with certainty. These data points often include the names of victims, their behaviors, and their relationships with traffickers and can provide important clues about the origins and methods of human trafficking, including locations and transportation patterns. NGOs can help in this area; and such NGOs as the and already are providing critical, victim-centered insight.

In addition, NGOs often build datasets and proprietary content on their own to uncover trafficking. , for example, maintains a large, proprietary dataset that鈥檚 built from network metadata and behavioral signals collected from publicly accessible online environments. This data is then analyzed into real鈥憈ime intelligence, such as risk scores and activity patterns, which helps law enforcement identify and prioritize suspected child exploitation offenders.

Traffickers use social media platforms, online ads, and messaging apps to recruit victims and to advertise illicit services, often leave a digital footprint that can be analyzed, which enables law enforcement and analysts to identify victims, map relationships between illicit actors, detect recruitment patterns, identify locations, and uncover entire trafficking networks.

Other relevant information sources include the Illicit Massage Business (IMB) database from 抖阴成年 Special Services, which includes business accounts, the location, and the owner of every massage parlor in the US, in which trafficking victims are forced to operate.

Because traffickers use social media platforms, online ads, and messaging apps to recruit victims and to advertise illicit services, they often leave a digital footprint that can be analyzed. This enables law enforcement and analysts to identify victims, map relationships between illicit actors, detect recruitment patterns, identify locations, and uncover entire trafficking networks. This information can then be enhanced by combining it with public records and data from the open web, deep web, and dark web.

Learning the lessons of collaboration

As we at the ACAMS鈥摱兑醭赡� Human Trafficking Initiative looked back at the lessons learned and reviewed best practices, we can see that any success in identifying illicit trafficking accounts is based on three factors: i) close cooperation with law enforcement and NGOs; ii) specialized investigative resources with human trafficking backgrounds; and iii) the use of data and open-source intelligence, either standalone or integrated into monitoring workflows.

Financial institutions understand their role and the need to obtain specialized data and expertise; and leveraging these capabilities typically results in the termination or de-risking of suspicious accounts.

Because collaboration with law enforcement is not consistent across financial institutions, particularly in the US, this means that overall, there鈥檚 a very uneven focus on human trafficking detection and prevention, depending on the availability of resources and the level of collaboration.

The role of regulators, like the U.S. Treasury Department鈥檚 , is crucial because these entities can leverage AI to act even more rapidly and connect information quicker, which can help disrupt human trafficking more effectively. Investigators are instructed to make a specific selection, field 38(h), when filing a report and include a specific reference to human trafficking. This will allow FinCEN to analyze and identify patterns, trends, and trafficking networks by linking these reports together.

In that context financial institutions have another reason to embrace AI within their customer data. By analyzing transactions and other patterns of risk using all available data sources and building agentic capabilities and workflows within their own customer data, financial institutions will be able to better identify high-risk accounts without carrying out labor-intensive investigations.

While this event series focused on the 2026 World Cup, human trafficking existed long before the tournament and will not stop once it concludes. However, if NGOs, authorities, and financial institutions can significantly improve their ability to detect and disrupt it, that would represent a major step forward.

You can find out more about how law enforcement and others are disrupting human trafficking networks here

Key insights:

• • • AI is now a common facet of the legal landscape 鈥� AI is increasingly a part of legal workflows across aspects of the practice, involving not only work matters, but also interactions with clients, opposing counsel, and the courts.

• • • Foundational understanding of AI in legal is crucial鈥� The guide provides concise, practical information that lawyers and legal professionals can use to get a better grasp on AI use in legal practice

• • • Guidance needed in a fast-changing environment 鈥� AI technology and its uses, its limitations, and lawyers鈥� professional responsibilities in the practice of law are evolving rapidly 鈥� and this guide provides needed guidance and help in navigating today鈥檚 environment.

AI is influencing virtually every corner of the legal profession, impacting how legal research is conducted, documents are drafted, discovery is handled, client expectations are managed, and how courts are addressing questions of professional responsibility. Whether lawyers themselves are using AI or not, they are likely to at least be on the receiving end of AI-assisted work product from opposing counsel or clients.

To help bring clarity to this rapidly changing legal arena, the Thomson Reuters Institute and the have released the 鈥� a resource for lawyers and legal professionals who want to approach AI with clarity, confidence, and professional rigor. This 鈥淔oundational Overview鈥� is the first installment of the 鈥淎I Guideline Series鈥� being published by Thomson Reuters Institute and ILTA, with additional guides to be published within coming months.

You can also access the newly published

For AI-enabled lawyers to be the most effective, it鈥檚 important that they first understand the complex legal and technical terminology related to AI, as well as the different categories of AI, within which legal practice these technologies best fit, and the professional responsibilities that accompany their use.

Practical, concise overviews

The AI Guide is a resource for establishing a solid foundation by using the most current information in this fast-moving environment. Written with contributions from a variety of leading attorneys, legal scholars, and legal technologists, the Guide offers lawyers a practical orientation to today鈥檚 AI landscape and the issues that matter most for their legal practice.

The Guide contains concise overviews on:

• • • the current state of AI adoption across the legal profession
    • essential AI terminology
    • the major categories of AI technologies and platforms
    • the situations in which AI is often used to support legal work
    • the ethical and professional responsibility considerations that lawyers must understand, and
    • the emerging trends likely to shape AI use in legal in the years ahead.

The Guide also offers a collection of additional resources for more in-depth exploration.

As AI shows itself to be remarkably effective at assisting with many routine, time-consuming, and information-intensive legal tasks, it also continues to require careful human judgment, verification, and oversight to be most effective. That鈥檚 why understanding AI鈥檚 strengths and its limitations is becoming an essential professional skill.

A different way of interacting with information

Unlike previous technologies, AI is not simply another software application. It is a fundamentally different way of interacting with information 鈥� one that鈥檚 capable of generating analysis, drafting documents, identifying patterns, and assisting with increasingly sophisticated legal work.

AI鈥檚 application within the legal profession brings forward unique, specific considerations. It also raises questions 鈥� as well as answers that are still evolving around accuracy, trustworthiness, ethics, professional responsibility, and many other issues.

Today, these are no longer theoretical discussions; rather, they鈥檙e practical questions that lawyers are confronting every day, regardless of whether those lawyers are currently using AI in their workflows.

The 鈥�AI Guide for Legal Professionals: A Foundational Overview鈥� can give lawyers a foundational understanding on how they and other legal professionals can integrate AI into their legal practice, better understand their responsibilities, and critically evaluate new AI technologies as they evolve.

You can access the newly published

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 抖阴成年, 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 Thomson Reuters 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.

You can explore the white paper听听here

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.

抖阴成年鈥� 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:

• • • 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 抖阴成年 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 Thomson Reuters Institute鈥檚听2026 AI in Professional Services Report

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. 抖阴成年’ 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 抖阴成年 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 抖阴成年. 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 Thomson Reuters 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 Thomson Reuters 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:

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

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

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

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

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

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

The weight of the robe & the value of connection

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

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

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

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

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

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

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

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

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

A judiciary that reflects society

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

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

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

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

Improving judges鈥� well-being

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

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

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

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

Key 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 Thomson Reuters 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 抖阴成年 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

• • • AI is already reshaping the legal profession 鈥� AI听is already embedded in lawyers’ day-to-day legal work with a significant share of both law firm attorneys and in-house legal teams actively using GenAI tools, with many expecting it to become central to their work within the next five years.

    • AIFLP Advisory Board was formed to prepare lawyers for an AI-reshaped profession 鈥� TRI convened 21 respected leaders from legal education, private practice, the judiciary, and AI ethics and governance to help ensure lawyers and law students are prepared for a profession reshaped by AI.

    • Human judgment remains central in an AI enabled legal future听鈥� Becoming AI ready is not simply about learning to use new tools; the Advisory Board emphasizes strengthening irreplaceable human capabilities is critical.

In today鈥檚 tech-driven environment, AI is no longer a future concept for the legal profession 鈥� it鈥檚 already here, and it鈥檚 changing how lawyers work, learn, and serve clients. Recognizing just how fast the evolution is moving, the Thomson Reuters Institute (TRI) has launched the AI and the Future of Legal Practice (AIFLP) Advisory Board, bringing together a group of respected leaders from across the legal ecosystem to help guide what comes next.

The board includes 21 accomplished voices from legal education, private practice, the judiciary, and AI ethics and governance. Their shared goal is simple but ambitious: Help ensure that both today鈥檚 lawyers and tomorrow鈥檚 law students are prepared for a profession being reshaped by AI.

Why now?

Because the shift is already underway. According to TRI鈥檚 recent 2026 AI in Professional Services Report, 41% of law firm attorneys say their organizations are already using some form of generative AI (GenAI); and nearly half of those at corporate legal departments report that AI tools are being rolled out there too. Even more telling, most professionals said they expect GenAI to become central to their day鈥憈o鈥慸ay work within the next five years.

That pace of change raises big questions about competence, ethics, education, risk, and access to justice. And those questions don鈥檛 have easy answers.

What the Advisory Board will focus on

The AIFLP Advisory Board is designed to tackle those challenges head鈥憃n. Its work will center on four key areas that are already under pressure as AI adoption accelerates:

• • • Legal education and talent development
    • Ethics, professional competence, and accountability
    • Governance, risk management, and client counseling
    • Access to justice and modern service delivery

The Advisory Board鈥檚 early focus areas will look at how AI is actually changing legal practice today, what future鈥憆eady lawyers really need to know, and how legal education and real鈥憌orld practice can better align. The emphasis is not just on using AI tools, but on strengthening the human skills that matter most, such as sound judgment, critical thinking, and careful verification of AI鈥慻enerated outputs.

Shaping the future, not reacting to it

Citing the critical need for this Advisory Board鈥檚 creation, Mike Abbott, Head of the Thomson Reuters Institute, notes that the legal profession is at a crossroads, and it can either react to AI鈥慸riven disruption or take an active role in shaping how these technologies are used to support lawyers, courts, and the public.

鈥淏y assembling a board of distinguished leaders, our goal is to help practicing lawyers and the lawyers of the future navigate a rapidly evolving landscape,鈥� Abbott said. 鈥淓nsuring that legal education strengthens irreplaceable skills such as critical thinking, human judgment and effective communication helps make AI use safe and effective. The Board鈥檚 efforts will ultimately help shape a future-ready profession, leading to better outcomes for all.鈥�

Meet the AIFLP Advisory Board Members

By convening experienced leaders from across the profession, TRI hopes to help lawyers navigate this landscape with confidence. Advisory Board Members include:

• • • Michael Abbott, Head of the Thomson Reuters Institute
    • Soledad Atienza, Dean of IE Law School (Spain)
    • The Honorable Jennifer D. Bailey, (Ret.), Partner, Bass Law
    • Benjamin Barros, Dean, Stetson University College of Law
    • Professor Sara J. Berman, University of Southern California, Gould School of Law
    • Megan Carpenter, Dean Emeritus, University of New Hampshire Franklin Pierce School of Law
    • Ronald S. Flagg, President, Legal Services Corporation
    • Donna Haddad, AI Ethics and Governance expert, and founding member, IBM AI Ethics Board
    • Nick James, Executive Dean of the Faculty of Law at Bond University (Australia)
    • Johanna Kalb, Dean and Professor of Law, University of San Francisco School of Law
    • The Honorable Nelly Khouzam, Florida Second District Court of Appeal
    • The Honorable William Koch, Dean, Nashville School of Law, and former Tennessee Supreme Court Justice
    • Sheldon Krantz, retired partner, DLA Piper, and a founder, DC Affordable Law Firm
    • Stefanie A. Lindquist, Dean, School of Law, Washington University in St. Louis
    • The Honorable Mark Martin, Founding Dean and Professor of Law, Kenneth F. Kahn School of Law at High Point University, and former Chief Justice, Supreme Court of North Carolina
    • Caitlin (Cat) Moon, Professor of the Practice and founding co-director, Vanderbilt AI Law Lab, Vanderbilt Law School
    • Hari Osofsky, Myra and James Bradwell Professor and former Dean, Northwestern Pritzker School of Law; Founding Director, Northwestern University Energy Innovation Lab; and Founding Director, Rule of Law Global Academic Partnership
    • Joanna Penn, Chief Transformation Officer, Husch Blackwell
    • The Honorable Morris Silberman, Florida Second District Court of Appeal
    • The Honorable Samuel A. Thumma, Arizona Court of Appeals, Division One
    • Mark Wasserman, Partner and CEO Emeritus, Eversheds Sutherland
    • Donna E. Young, Founding Dean, Lincoln Alexander School of Law, Toronto Metropolitan University

What鈥檚 next?

The Advisory Board held its first meeting in February and will meet quarterly going forward. As the work progresses, TRI plans to publish research findings, best practices, and practical recommendations for legal educators, law firms, and courts.

In a profession built on precedent and careful reasoning, the rise of AI presents both opportunity and responsibility. The AIFLP Advisory Board is an effort to make sure the legal community meets that moment thoughtfully and on its own terms.

You can learn more about the impact of advanced technology on the legal profession here

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 Thomson Reuters 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