Key insights:

• • • Accountability, not adoption, is the central governance challengeĚý— 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 — 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 reformĚý— 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 — 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’ implications for security, confidentiality, and discoverability. As one expert observed: “Shadow AI is already happening.”

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 — the risk then becomes not adoption itself, but uneven adoption that evolves beyond institutional control.

It’s 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’s 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 — 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 — 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 — 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’s 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 ofĚýour Scaling Justice blog series here

Key insights:

• • • Consensus among AI systems is not the same as correctness — 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 — Multi‑agent 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‑standing human decision-making — Modern multi‑agent 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‑part 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 Göttingen 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 ¶¶ŇőłÉÄę 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 “very early” 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’s 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 — 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’s 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’sĚý2026 AI in Professional Services Report

Key highlights:

• • • AI simulation can replace the “repetition loop” used to train junior lawyers — 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 — 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 — 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’s (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 — if designed with enough precision to force real decision-making — 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’s Liles, this rubric must be paired with a feedback loop that’s 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’s 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’s 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 — 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,” explains Shayesteh. “It is the best gift you can give them.”

You can find more about the

Key insights:

• • • As a single answer‑machine, AI may be unsafe for executive decision‑making — Treating AI as a tool that delivers one authoritative answer makes it easy to either ignore any advice you don’t 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 — Instead of providing consensus answers, AI systems need to be intentionally designed to identify and preserve disagreement.

    • Disagreement is the insight — 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‑part 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. ¶¶ŇőłÉÄę’ 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 ¶¶ŇőłÉÄę 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 — and in the interest of transparency, he’s also a colleague; this story started as an internal conversation at ¶¶ŇőłÉÄę. 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. — a CTO-minded persona trained on architecture teardowns and engineering post-mortems — 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, — a CFO-minded persona drawing on earnings transcripts and filings with the U.S. Securities and Exchange Commission (SEC) — 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. “That’s how two different minds work,” Khan says. “They need to work together in order to bring their insights to bear on decisions.”

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 — Should a leadership team accelerate an AI rollout? — the value of their divergence sharpened further. Elara’s response cut directly to the blind spot a technology-focused advisor like Adrian would miss: “Adrian says the system is ready,” Elara stated. “I 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.”

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… and increasingly, the research is saying yes.

In the second part of this series, we’ll 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’sĚý2026 AI in Professional Services Report

Key insights:

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

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

    • Judges should lead — 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 — one that highlights the importance of responsible and thoughtful adoption.

A recent webinar, , presented by theĚý — a joint effort by the National Center for State CourtsĚý(NCSC) and the Thomson Reuters Institute (TRI) — 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 — from ‘I’m just learning’ to ‘I want to be a power user’ — says, ‘But I want to do it right,’” 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.

“If 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,” she explains. “I want the self-represented litigant to understand what I’m doing and why I’m doing it — and AI helps me translate lawyer-speak into plain-speak, quickly.”

You can explore the white paperĚý 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’ 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,” she explains. “The 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, visitĚýĚýto 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. “And we need people to engage for learning purposes.” Instead, she notes that she advocates for voluntary transparency — judges explaining their use of AI in ways that build public understanding and confidence.

Prof. Cyphert agrees. “You can’t assess risks and benefits if you don’t understand the technology,” 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 — with care, purpose, and always with people at the center.

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

Key takeaways:

• • • Corporate legal AI strategies should start with business goals, not just efficiency — While many corporate law departments first adopt AI for internally-focused use cases, the bigger opportunity is to align AI with broader business priorities such as revenue growth, risk reduction, and improved operational performance.

    • GCs should measure AI success by business impact — Metrics such as time saved and tool usage help, but stronger AI metrics connect legal work to business results. In contract review, for example, success may be reflected in improved win rates, reduced revenue leakage, faster deal completion, or dollars of risk avoided.

    • A strong legal AI strategy should produce multiple forms of business value at once — The most effective approaches do not focus on a single benefit such as cost savings. Rather, they aim to improve service delivery, strengthen operations, support growth, and reduce risk across the business.

Over the past several years, corporate law departments have begun to rapidly adopt AI tools, often spurred on by company-wide AI initiatives. In fact, in just the past year alone, department-wide AI adoption has risen to nearly half (47%) of all departments, according to respondents surveyed for Thomson Reuters Institute (TRI) research.

However, it’s not enough to simply adopt technology. For AI to truly make an impact, it needs to be integrated strategically. In taking this strategic approach, however, GCs and other legal department leaders are still in the early stages.

According to findings from TRI’s 2026 State of the Corporate Law Department Report, more GCs are focused on technology than ever before. When asked their top strategic priorities over the next year, 28% answered that technology was a top priority, double the portion that prioritized technology just one year ago. And out of those mentions of technology, a vast majority specifically referenced AI as a primary area of focus.

Historically, many legal departments have thought about AI from an internal efficiency standpoint, leveraging it to perform their work quicker and cheaper. Increasingly, however, C-Suites are looking to their legal departments to provide more effective business counsel and connect legal analysis to business outcomes — and, not surprisingly, they’re expecting AI to play a role in that shift.

So how can GCs effectively make AI a priority not only for the legal department but also for the entire business? It starts with broadening the potential impact of AI processes.

From unlocking to deploying capacity

Still less than four years since the public release of generative AI (GenAI) tools through ChatGPT, many corporate legal departments are still in the early days of rolling out the technology. As a result, most GenAI use cases still tend to focus on low-hanging fruit such as document summarization and review, contract drafting and review, research, and more.

This is understandable from an individual use case standpoint. The problem is, when these use cases are translated to the leadership level for overall strategic guidance, many GCs remain focused on how to maximize the gains from that low-hanging fruit. According to TRI research, less than 20% of corporate law departments measure return-on-investment from AI at all, meaning many departments are using AI tools without any sort of guiding measurement around what success should look like. And even among those departments that are measuring AI success, most of the metrics they use center around internal department usage or department cost savings from the tool.

Those measurements are more helpful than no tracking at all, to be sure. They focus on how AI is unlocking capacity for the legal department and look for ways that attorneys can perform their work more efficiently than before. Indeed, the majority of legal departments that have invested in AI tools are currently at this point.

However, there is an additional step that legal departments need to take in order to full take advantage of the strategic value of AI. And that is connecting AI’s use to that of larger business goals by deploying the capacity it has unlocked. This requires thinking about AI less in terms of how it will impact the legal department, and more in terms of how it will impact those that the legal department serves.

For example, take a common AI use case such as contract review. Currently, the most common measurement around contract review technology is speed, such as how quickly the legal department can help a contract go from start to signature. Maximizing that value can improve the efficiency of the department, to be sure. But C-Suite partners aren’t necessarily looking for an efficient department as the end goal — they’re looking for business success.

As a result, some forward-thinking GCs are looking to connect AI usage directly with business goals or revenue. For contract review, that could mean demonstrating the impact on overall contract win rate, or whether close rates increased through use of AI. Or it could mean more successful revenue leakage protection; and it could even mean risk avoidance, measured in dollars of risk avoided. All of these can demonstrate value and be connected to the rest of the business.

Further, all of this requires close collaboration with other business units, both in terms of sharing metrics as well as understanding what success throughout the organization should mean to all parties. That said, GCs have told TRI for countless years that breaking out of a silo is a top priority for the legal department. In this case, AI implementation should be no different.

Wide areas of impact

As it currently stands, corporate law departments are seeing the most impact from AI in areas of efficiency and time saved. More than three-quarters of GCs who have talked with TRI say that AI is either currently benefiting the department’s efficiency and productivity, or that they’re expecting those benefits to occur within the next 12 months.

Connecting AI outcomes with business imperatives provides more areas of improvement, however. In this year’s State of Corporate Law Department Report and elsewhere, TRI breaks down the law department’s role into four key functions that we call the four spinning plates:

1. 1. 1. Provide effective legal services and operational excellence
      2. Offer efficient legal value within budget
      3. Enable business and strategic growth, and
      4. Protect the business’s assets and competitive advantage.

AI’s impact on efficient legal value is clear; but GCs are beginning to see that it can actually impact all four of those plates.

Those GCs looking to adopt AI as a strategic goal should be aware that said strategy should encompass more than simply internal efficiency. Not all of these benefits will be applicable to all departments, but all departments should be considering more than just one of these areas. An effective AI strategy should have multiple benefits in mind — and as such, it should take into account multiple business factors when measuring the success of the department’s AI strategy.

Entering into an AI strategy is a laudable goal for today’s GCs, but also not a light undertaking. When thinking about how AI will impact the department, leaders should take the next step beyond deploying capacity into unlocking capacity, helping attorneys not only work more efficiently but also make a bigger impact on the business at large.

You can download a full copy of the Thomson Reuters Institute’s

Key insights:

• • • Well-being is a professional necessity — 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 — Staying connected to the communities they serve helps judges develop empathy, recognize bias, and deliver fairer decisions.

    • Diverse experience strengthens the judiciary — 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 — 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 — 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 — 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’ trust in the courts.

These themes are explored more in depth in the Thomson Reuters Institute’s video series,ĚýBeyond the Bench. For example, in the episodeĚý,ĚýAssociate 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’ decision-making and strengthens community ties.

Another episode of the series,Ěý,Ěýexamines 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 — it’s 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’ 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.

TheĚý“Beyond the Bench”Ěývideo series is available on

Key insights:

• • • AI’s greatest strength in criminal justice is pattern recognition— 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— Legal professionals are integral to evaluating AI-generated outputs, especially when decisions affect evidence, warrants, and individuals’ constitutional rights.

    • The most effective model is human/AI collaboration— 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 — 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 theĚý, a joint effort by the National Center for State CourtsĚý(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,” said Mark Cheatham, Chief of Police in Acworth, Georgia, during the webinar. “It 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 — 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,” Judge DuBois explained. “But 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’s 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’ rights are on the line. “A human cannot just rely on that machine,” said Joyce King, Deputy State’s Attorney for Frederick County in Maryland. “You 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 ¶¶ŇőłÉÄę Special Services (TRSS) echoed that sentiment with a principle that cuts across every application of AI in the justice system. “The number one thing… 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’s 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’s 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 — not against them — has never been greater.

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

Key insights:

• • • Non-compliance is significantly more expensive than compliance — Data consistently shows the cost of non-compliance can be greater than proactive compliance investments.

    • Reputational damage and hidden costs often outweigh direct fines — Beyond financial penalties, the damage from legal fees, loss of customer trust, and operational disruptions from non-compliance can inflict long-term harm.

    • Strategic investment in compliance yields a competitive advantage — A robust compliance program builds trust, attracts investors, and demonstrates greater operational resilience in a complex regulatory landscape.

There’s a persistent myth in the business world that compliance programs are a necessary burden, a line item to be minimized and managed rather than invested in strategically. The data tells a very different story, however, and it has for quite some time. For organizations still treating compliance as an overhead expense, it’s time to reconsider the math and view the broader strategic picture.

The numbers don’t lie: Non-compliance costs more

Non-compliance costs are 2.65-times the cost of compliance itself, a finding that dates back to the of multinational organizations. While the average cost of compliance for the organizations in that study was $3.5 million, the cost of non-compliance was much greater. That means simply by investing in compliance activities, organizations can help avoid problems such as business disruption, reduced productivity, fees, penalties, and other legal and non-legal settlement costs.

According to a later report from from 2017 (the most recent set of analytical data on the subject), the numbers have only grown more striking. The study showed that average cost of compliance increased 43% from 2011 to 2017, totaling $5.47 million annually. However, the average cost of non-compliance increased 45% during the same time frame, adding up to $14.82 million annually. The costs associated with business disruption, productivity losses, lost revenue, fines, penalties, and settlement costs add up to 2.71-times the cost of compliance.

And these non-compliance costs from business disruption, productivity losses, fines, penalties, and settlement costs, among others aren’t simply abstract risks. They’re real, recurring, and measurable, and they don’t stop with the fine itself.

Beyond the fines themselves, legal costs are a significant and often underestimated component of non-compliance.

This gap between compliance and non-compliance provides evidence that organizations do not spend enough of their resources on core compliance activities. If companies spent more on compliance in areas such as audits, enabling technologies, training, expert staffing, and more, they would recoup those expenditures and possibly more through a reduction in non-compliance cost.

While the math here is straightforward, the strategic case is even clearer. Compliance isn’t overhead; rather, it’s an investment with a measurable, proven return.

The hidden costs: Legal fees, fines & reputational fallout

Regulatory fines get the headlines, but they represent only part of what non-compliance actually costs an organization — a cost that has only risen over time. As of February, a total of 2,394 fines of around €5.65 billion have been recorded in the database, which lists the fines and penalties levied by European Union authorities in connection with its General Data Protection Regulation (GDPR).

Beyond the fines themselves, legal costs are a significant and often underestimated component of non-compliance. Regulatory norms are shifting constantly and navigating them requires specialized expertise. As quickly as the rules change, outside counsel and compliance specialists must keep pace, and that knowledge comes at a price. Every alleged compliance violation triggers an immediate need to engage qualified counsel, adding to a cost burden that compounds quickly and unpredictably.

Then there is reputational damage, perhaps the most enduring consequence of all. The cost of business disruption, including lost productivity, lost revenue, lost customer trust, and operational expenses related to cleanup efforts, can far exceed regulatory fines and penalties. Consider , whose compliance failures around its anti-money laundering (AML) efforts became a cautionary tale for the industry. TD Bank’s massive $3 billion in fines from US authorities wasn’t just the result of a few missteps; rather, it was caused by years of deep-rooted failures in its AML program, pointing to a culture that prioritized profit over compliance.

The findings from both the 2011 and 2017 studies provide strong evidence that it pays to invest in compliance.

TD Bank’s failure to make compliance a priority not only led to a huge fine but also seriously damaged its reputation, with revising TD’s outlook to negative in May 2024, where it remains. This is the kind of a reputational stigma that can take years to repair.

Leveraging compliance as a competitive advantage

There is also a positive side of the ledger that often goes unacknowledged. A robust compliance program signals to investors, partners, and clients that an organization is well-governed and trustworthy. That reputation doesn’t just retain market value; it actively attracts it.

Organizations that cut corners in compliance risk engaging in a short-sighted, high-risk strategy that will ultimately result in a negative outcome for the organization. Businesses that take compliance seriously tend to operate with greater predictability, fewer surprises, and stronger stakeholder confidence.

The 2017 Ponemon and Globalscape and study found that, on average, only 14.3% of total IT budgets were spent on compliance then, not much of an increase from the 11.8% reported in 2011. This clearly indicates that organizations are underspending on core compliance activities in the short term and aren’t prepared to allot further resources as the years go on. That gap represents not just risk, but a clear missed opportunity.

“The findings from both the 2011 and 2017 studies provide strong evidence that it pays to invest in compliance,” explains Dr. Larry Ponemon, Chairman and Founder of the Ponemon Institute. “With the passage of more data protection regulations that can result in costly penalties and fines, it makes good business sense to allocate resources to such activities as audits and assessments, enabling technologies, training, and in-house expertise.”

The organizations that recognize compliance as a strategic function, not a reactive one, are the ones that will earn the trust of clients, the confidence of investors, and the operational resilience to weather an increasingly complex regulatory environment. The data is clear, and the choice is a critical one.

You can find out more about the challenges faced by corporate compliance professionals here

Key takeaways:

• • • Efficiency is a means, not an end — Gains from AI only count when you can show what they enabled: better advice, stronger protection, smarter business support.

    • Narrow measurement invites cuts — Legal departments that measure AI value only through cost savings are telling C-Suites that legal costs less, thereby inviting budget and headcount reductions.

    • Measure across all four plates — A framework that captures effectiveness, risk, and enablement alongside efficiency is what shifts perception of the legal department from cost center to strategic asset.

Your legal department has invested in AI tools, adoption is growing, your team is saving time on routine work and, by most accounts, work operations are running faster. Then your CFO asks a simple question: What has AI delivered for the legal department?

If your answer centers on hours saved and cost reduced, you are not alone. However, you may be leaving your most important value story untold. And in a climate in which legal departments are under more scrutiny than ever to demonstrate the full return on their AI investment, that gap matters.

This is the fourth and final part of our series on the “Four Spinning Plates” model, which frames the GC’s evolving responsibilities as:

1. 1. 1. delivering effective advice
      2. operating efficiently
      3. protecting the business, and
      4. enabling strategic ambitions.

This article focuses on the Efficient plate and specifically on the risk of letting it do too much of the talking.

The Efficient plate under pressure

For a GC, making the best use of what are often limited resources is a constant pressure. The Efficient plate sits alongside, not above, the other three plates and must be kept always spinning. Right now, however, for many in-house legal teams the Efficient plate is receiving disproportionate attention, and for understandable reasons.

AI adoption in corporate legal departments is accelerating quickly. According to the Thomson Reuters Institute’s AI in Professional Services Report 2026, nearly half (47%) of corporate legal respondents surveyed said their department has already integrated generative AI (GenAI) into their work — more than double the figure from the previous year. A further 18% reported that they’re already using agentic AI, with more than half expecting agentic AI to be central to their workflow within the next two years.

GCs are genuinely excited about what this makes possible. As one GC said in the survey that underpinned the AI in Professional Services Report: “It presents the promise of getting out of low-value work and into higher-value work that supports the business.” Another described their vision of a legal department that is “boldly digital-first, relentlessly innovative, and tightly woven into business priorities.”

Clearly, the opportunity is real, but so is the risk of measuring it badly.

The measurement trap

Our 2026 research found that only one-quarter of legal departments are currently measuring the ROI of their AI tools. That alone is striking given the pace of adoption but the follow-up finding is where the real problem lies — of those departments that are measuring ROI, 80% are tracking it in terms of internal cost savings.

Reducing external spend, automating high-volume processes, and bringing more work in-house are all legitimate efficiency gains and worth reporting, of course. However, when cost reduction becomes the only story being told, two things can happen. Your C-Suite learns to associate your department’s value with how little it costs, a frame that is very difficult to escape once it’s established. And the wider value that efficiency enables in terms of sharper risk identification, faster business support, and higher-quality advice goes unmeasured and therefore unrecognized.

ĚýIf your metrics only capture time saved and cost reduced, and not what that freed-up capacity actually delivered, you are measuring the means and ignoring the end.

Think about what GCs themselves say they want from AI. As several GCs said in the survey, they’re hoping AI will provide them with “better output on more meaningful tasks,” “proactive, strategic insight,” and “getting out of low-value work.” These are not efficient outcomes, per se; rather, they are effectiveness, protection, and enablement outcomes, made possible by improved efficiency.

So, if your metrics only capture the input (time saved, cost reduced) and not what that freed-up capacity actually delivered, you are measuring the means and ignoring the end. This is the efficiency trap — measuring the plate so narrowly that it starts to work against you.

Reframing how you measure efficiency

Measuring efficiency well does not mean measuring it more. It means measuring it differently, and always in relation to the business you support. A few principles worth applying include:

Present spend in a business context — Legal spend as a percentage of company revenue tells a more credible story than a raw cost figure. It scales with the business and can be benchmarked meaningfully against peers.

Show what technology investment actually delivered — Time saved through automation is a useful starting point, but the stronger case is what the team did with that time. Tracking the shift from routine to strategic work over a period of time is a far more compelling ROI story.

Connect efficiency gains to business outcomes — An efficiency gain that enabled a faster product launch, prevented a compliance risk, or improved stakeholder satisfaction has a value that no cost metric will capture. Build those connections explicitly into how you report the value of the legal department to the C-Suite.

New resources to help

To support GCs in getting this right, the Thomson Reuters Institute has added two new resources to its Value Alignment Toolkit that directly address this measurement gap.

The Metrics Library brings together more than 100 metrics organized across all four spinning plates. It is a practical starting point for GCs to browse, select, and adapt to the specific goals of their departments, making it easier to build a measurement framework that reflects everything departments do, not just the part that appears in a budget line.

The AI Success Metrics guide addresses the AI measurement gap head-on with a best practice guide and a hands-on worksheet designed specifically for legal departments navigating AI adoption and asking: How do we actually know whether this is working? It looks beyond cost savings to capture the fuller picture of AI value including quality, capacity, strategic contribution, and risk.

Getting the balance right

In today’s environment, every GC needs to consider their answer when their C-Suite asks what the legal department delivers. Are your department’s metrics giving them the full answer or just the part that’s easiest to count?

Efficiency is not the enemy of strategic value. A department that runs well, uses its resources wisely, and embraces technology thoughtfully can in turn create the conditions for everything else the business needs from its legal function. However, that case only lands if your metrics measure across all four plates, not just one.

You can explore the new Metrics Library and AI Success Metrics guide, along with the full Thomson Reuters Institute’s Value Alignment toolkitĚýhere