Key insights:

• • • Judges can lead by example in responsible AI adoption 鈥� By engaging thoughtfully with AI and using it to enhance (and not replace) human judgment, judges can set an important standard for accountability, verification, and ethical use across the legal system.

    • AI excels at streamlining preparation, not decision-making 鈥� AI is most valuable for reducing the burden of preparatory tasks, such as summarizing briefs, organizing records, identifying questions, and clarifying technical or statutory language. It should not substitute for judicial reasoning, discretion, or decision-making.

    • Verification and guardrails are non-negotiable 鈥� Responsible AI use requires vetted tools, strong privacy protections, precise prompting, and independent verification of all AI-generated content. The integrity of judicial work depends on maintaining these disciplined processes.

There is a leadership opportunity sitting inside every courtroom in America, and most judges haven’t claimed it yet.

AI is already shaping the legal system, in how attorneys research, how clerks draft, and how litigants prepare their cases. In , the latest webinar from the听听鈥� a joint effort by the National Center for State Courts听(NCSC) and the 成人VR视频 Institute (TRI) 鈥斕鼵hief Justice Matthew Fader of Maryland observed that the 鈥渢echnology is just becoming ubiquitous.鈥�

鈥淚t’s spreading faster than any technology I’ve encountered in my lifetime,鈥� Chief Justice Fader said. The reality, of course, is that AI will influence a wide spectrum of legal processes, including how judicial work gets done. The question is whether judges will be passive observers of that shift, or active, thoughtful participants in it.

The advantages in engaging with AI extend beyond efficiency 鈥� it is also about leadership. Judges who use AI responsibly, with clear guardrails, careful verification, and a firm commitment to human judgment, can model the standard that the legal profession needs from the bench.

The preparation problem AI can actually solve

The bulk of the work for a successful hearing or trial is done long before the judge enters the courtroom. This happens through careful review of records, briefs, and complex subject matter. Yet time constraints often limit thorough preparation, and this is where AI adds real value.

When used appropriately, AI can streamline early-stage work without compromising judicial rigor. Judges can upload briefs and receive structured summaries of each party’s arguments, providing a clear roadmap before deep analysis begins. Transcripts can be searched instantly for specific testimony, and lengthy records can be organized by topic, which reduces manual review.

For more technical cases, AI can generate concise primers on unfamiliar subjects, helping judges engage expert testimony within the proper context. AI also can draft targeted oral argument questions, flag hallucinated or incorrect citations in briefs, identify factual inconsistencies, and create procedural checklists for motions or default judgments. It can even simplify complex statutory language to enhance the clarity of proposed jury instructions.

As Justice Linda Kevins of New York observed, the more creatively AI is applied, the more use cases emerge. Judges who begin with one small task often discover a range of new efficiencies, which transforms preparation from a burden to a strategic advantage.

Responsible use is the point

What separates a judge using AI as a thoughtful professional from one using it carelessly is the discipline built around it. And that starts before the first prompt.

Any tool a judge uses should be vetted by the court’s IT department, its administrative office, or both. The terms of service matter, such as whether prompts are retained, whether data is used for model training, whether confidential information could be exposed. These are not technical details to delegate, rather they are the minimum a judge should understand before uploading anything.

Prompting with intention matters too. Instructing AI tools to always cite its sources, to flag uncertainty, and to present arguments neutrally are habits that will produce better output and reduce the risk of erroneous data. Asking for authority behind every claim, and explicitly telling the tool not to guess, should become standard practice. The right questions to ask before any task, as Dr. Maura Grossman of the University of Waterloo, described it, are: “What are you trying to accomplish? And what’s the best tool for that?”

And verification is non-negotiable. AI tools can confuse a dissent with a majority holding, misstate what a lower court decided, or misquote language.

One simple rule, offered by Justice Kevin summed it up: “Whatever it gives me, I have to verify.” Every case that AI identifies, every citation it provides, every factual summary it generates must be checked against primary sources.

This is not a burden that undermines the value of AI; indeed, it is the practice that makes AI valuable.

What AI cannot do

In AI use in courts, the boundaries matters as much as the capability of the tools.

“This is not a truth-finding tool,鈥� explained Chief Justice Fader. 鈥淭his is a truth-agnostic, predictive tool.” AI generates statistically likely output based on patterns in its training data. It does not reason, weigh competing values, or sense that something in a case doesn’t add up. It has no judgment, no empathy, no moral compass, and no capacity to recognize when the law should evolve because justice requires it.

Judicial discretion is not an inefficiency AI can optimize away 鈥� and this is the point. As Justice Kevins said in the webinar, AI is “only an assistant, an extra assistant.” The analysis of how the law applies to a specific set of facts, before specific parties, in a specific moment 鈥� that is unavoidably human work. The moment any judge asks AI to assess which party has the stronger argument, a vital task has been delegated to the AI that should not have been.

Leading from the bench

Judges who engage with AI carefully set a visible standard for everyone who appears before them. These judges are better equipped to recognize when a filing鈥檚 AI-generated content is inaccurate. They can set informed expectations for clerks and staff, and they can ask the right questions when AI-related issues surface in litigation 鈥� many of which are already surfacing regularly.

The goal for judges is to use AI 鈥渢o do their work more productively, but not to replace their judgment,鈥� said Chief Justice Fader. And that balance should guide responsible judicial use: Neither avoidance, which is increasingly unrealistic; nor uncritical adoption, which carries real risk 鈥� but instead a more disciplined approach than either of these extremes.

The prepared judge is not simply the one who has read everything before walking into a courtroom. It’s the one who has used every available tool wisely, verified rigorously, and exercised the human judgment that no algorithm can replicate. That standard, consistently applied, is a form of leadership for which the legal profession has been waiting.

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

Key insights:

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

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

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

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

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

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

Bringing GenAI in

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

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

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

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

The literacy gap that follows adoption

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

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

What courts actually expect

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

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

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

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

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

Key insights:

• • • Knowledge flows both ways 鈥� The AI era has introduced a new dynamic in courthouses with knowledge flowing in more than one direction.

    • Both sides bring irreplaceable expertise鈥� Clerks bring AI fluency; and judges bring the legal instinct to know when something is wrong. Neither works without the other.

    • Informal learning must become institutional practice鈥� Courts that formalize this exchange, rather than leaving it to chance, will be better positioned for the wave of AI change ahead.

Picture a seasoned federal judge, with decades of experience on the bench and thousands of cases behind them, leaning in to watch a first-year law clerk navigate an AI research tool. The clerk is the one doing the teaching.

This scene, quietly playing out in courthouses across the country, may be one of the most under-reported shifts in how the judiciary is adapting to a rapidly changing technological landscape. On the latest episode of , Kaitlyn Frank, who leads courts-focused market intelligence for 成人VR视频, described exactly this phenomenon and what it signals for the future of judicial work.

From skeptics to early adopters 鈥� in 18 months

The pace of change has been striking. Courts and judges were among the least willing professional groups to adopt AI tools just 12 to 18 months ago. Today, research from the indicates that more than 60% of federal judges are using at least one AI tool in their work. That is not a gradual drift 鈥� it鈥檚 a shift.

What drove it? Judges recognized that the question was never really whether AI would enter the courthouse, but how well-prepared the institution would be when it arrived. Courts continue to experience significant staffing shortages 鈥� last year more than two-thirds (68%) of courts were facing staffing challenges, with nearly half of court professionals saying they lacked the time to complete their work, according to the . Yet, many court professionals say they have found particular value in tools that can assist with research, document summarization, and administrative workflows. This makes the clear point that AI, used well, does not replace the people doing this work, rather, it gives them room to do it better.

A symbiotic relationship at the heart of adoption

The traditional judge-clerk relationship is one of the most distinctive in the legal profession. Judges impart legal reasoning, institutional knowledge, and decades of pattern recognition. Clerks bring fresh legal training, intellectual rigor, and an outsider’s perspective. It has always been a two-way exchange, even when it was rarely described that way.

AI has made that dynamic more explicit and expanded it. Clerks arriving from law schools where AI literacy is increasingly woven into the curriculum bring a technological fluency that many sitting judges have not had the opportunity to develop. Judges, in turn, bring something AI cannot replicate: The hard-earned ability to sense when a legal argument is wrong before they can fully articulate why. One without the other creates risk; yet together, they form a genuinely effective check on AI output.

This symbiosis extends beyond individual courtrooms. Courts strengthen their institutional resilience when experienced staff and new clerks learn from each other, sharing knowledge about AI tools and their limitations. When the next generation of AI capabilities arrives 鈥� and it will 鈥� those courts will adapt from a position of strength rather than scrambling from a standing start.

Building the institutional foundation

AI in courts is developing rapidly, and that pace is largely a good thing. Legal research that once consumed hours can be completed in minutes; voluminous case files can be summarized with accuracy; and unfamiliar bodies of law can be mapped quickly, giving judicial staff a workable orientation before they dive deeper. These are meaningful gains for institutions that work under real resource pressure.

Those courts that are making the most of AI are not simply those with the most technologically curious judges or the most skilled clerks. They are the ones treating AI adoption as an institutional decision rather than an individual one. That means creating written policies that define appropriate use, establishing risk tiers that help staff calibrate how much oversight different tasks require, and developing training that evolves as the tools themselves evolve.

Some courts currently have no official AI policy in place, creating a risk that staff may use AI inappropriately. And without a shared framework, every individual is left to make judgment calls that should be made collectively 鈥� and the knowledge being built through experimentation stays siloed rather than becoming a shared institutional asset.

Courts that invest in policy infrastructure now are not slowing themselves down. They are building the foundation on which broader, more confident AI adoption becomes possible.

A new kind of judicial wisdom

The best judges have always learned from the people and experiences around them 鈥� through difficult cases, insightful clerks, and fellow judges. A willingness to keep learning throughout their careers is itself a mark of judicial wisdom.

AI does not change that instinct; rather it expands the range of what there is to learn from, and who is doing the teaching.

For courts navigating this moment, the reverse mentorship dynamic offers more than a practical model for AI adoption. It offers a reminder that the judiciary has always adapted by drawing on the full range of knowledge within its walls. The same should be true now, because even though the tools are newer, the principle is not.

For more on this download a full copy of the 成人VR视频 Institute鈥檚听report,

Key insights:

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

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

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

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

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

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

Justice as machinery

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

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

Reimagining the machinery

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

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

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

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

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

Where do we start?

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

Set the tone from the top听

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

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

Build AI literacy

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

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

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

Rethink the systems, not just the tools

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

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

Invite diverse perspectives

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

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

Finally, remember to start small

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

Closing thoughts

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

You can find more insights from Judge Braswell here

Key takeaways:

• • • Legacy data architectures can’t keep up with modern demands 鈥� Traditional, centralized data cores were designed for stable, predictable environments and are now bottlenecks under continuous regulatory change, rapid M&A, and AI-driven business needs.

    • AXTent aims to unify modern data principles for regulated enterprises 鈥� The modern AXTent framework integrates data mesh, data fabric, and composable architecture to create a data core built for distributed ownership, embedded governance, and adaptability.

    • A mindset shift is required for lasting success 鈥� Organizations must move from project-based data initiatives to perpetual data development, focusing on reusable data products and decision-aligned outcomes rather than one-off integrations or platform refreshes.

This article is the second in a 3-part blog series exploring how organizations can reset and empower their data core.

For more than a decade, enterprises have invested heavily in data modernization 鈥� new platforms, cloud migrations, analytics tools, and now AI. Yet, for many organizations, especially in regulated industries, the results remain underwhelming. Data integration is still slow because regulatory reporting still requires manual remediation, M&A still exposes hidden data liabilities, and AI initiatives struggle to move beyond pilots because trust and reuse in the underlying data remains fragile.

The problem is not effort, it is architecture. Since 2022, the buildup around AI has been something out of science fiction 鈥� self learning, easy to install, displace workers, autonomous, even Terminator-like. Moreover, while AI may indeed revolutionize research, processes, and profits, the fundamental challenge is not the advancing technology, rather it is the data used to train and cross-connect these exploding capabilities.

Most data cores in use today were designed for an earlier operating reality 鈥� one in which data was centralized, reporting cycles were predictable, and governance could be applied after the fact. That model breaks down under the modern pressures of continuous regulation, compressed deal timelines, ecosystem-based business models, and AI systems that consume data directly rather than waiting for curated outputs.

So, why is the AI hype not living up to the anticipated benefits? Why is the data that underpinned process systems for decades failing to scale across interconnected AI solutions? The solution requires not another platform refresh, but rather, a structural reset of the data core itself.

That reset uses data meshes, data fabrics, and modern composable architecture as a single, integrated system, and aligns it to the AXTent architectural framework, which is designed explicitly for regulated, data-intensive enterprises.

Why the traditional data core no longer holds

Legacy data cores were built to optimize control and consistency. Data flowed inward from operational systems into centralized repositories, where meaning, quality, and governance were imposed downstream. That approach assumed there were stable data producers, limited use cases, human-paced analytics, and periodic regulatory reporting.

Unfortunately, none of those assumptions hold today. Regulatory expectations now demand traceability, lineage, and auditability at all times (not just at quarter-end). M&A activity requires rapid integration without disrupting ongoing operations. And AI introduces probabilistic decision-making into environments built for deterministic reporting, with business leaders expecting insights in days, not months.

The result is a growing mismatch between how data is structured and how it is used. Centralized teams become bottlenecks, pipelines become brittle, and semantics drift. Compliance then becomes reactive, and the cost of change increases with every new initiative.

The AXTent framework starts from a different premise: The data core must be designed for continuous change, distributed ownership, and machine consumption from the outset. Indeed, AXTent is best understood not as a product or a platform, but as an architectural framework for reinventing the data core. It combines three design principles into a coherent operating model:

1. 1. 1. Data mesh 鈥� Domain-owned data products
      2. Data fabric 鈥� Policy- and metadata-driven connectivity
      3. Data foundry 鈥� Composable, evolvable data architecture

Individually, none of these ideas are new. What is different 鈥� and necessary 鈥� is treating them as a single system, rather than independent initiatives as conceptually illustrated below:

Fig. 1: The AXTent model of operation

The 3 operating principles of AXTent

Let鈥檚 look at each of these three design principles individually and how they interact with each other.

Data mesh: Reassigning accountability where it belongs

In regulated enterprises, data problems are rarely technical failures. Instead, they are accountability failures. When ownership of data meaning, quality, and timeliness sits far from the domain that produces it, errors propagate silently until they surface in regulatory filings, audit findings, or failed integrations.

A structured framework applies data mesh principles to address this directly. Data is treated as a product, owned by business-aligned domains that are then accountable for semantic clarity, quality thresholds, regulatory relevance, and consumer usability.

This is not decentralization without guardrails, however. AXTent enforces shared standards for interoperability, security, and governance, ensuring that domain autonomy does not fragment the enterprise. For executives, the benefit is practical: faster integration, fewer semantic disputes, and clearer accountability when things go wrong.

Data fabric: Embedding control without re-centralization

However, distributed ownership alone does not solve enterprise-scale problems. Without a unifying layer, decentralization simply recreates silos in new places.

A proper framework addresses this through a data fabric that operates as a control plane across the data estate. Rather than moving data into a single repository, the fabric connects data products through shared metadata, lineage, and policy enforcement.

This allows the organization to answer critical questions continuously, such as:

• • • Where did this data come from?
    • Who owns it?
    • How has it changed?
    • Who is allowed to use it 鈥� and for what purpose?

In this way, governance is no longer a downstream reporting activity; rather, it is embedded into how data is produced, shared, and consumed. Compliance becomes a property of the architecture, not a periodic remediation effort.

And in M&A scenarios, the fabric enables incremental integration, which allows acquired data domains to remain operational, while being progressively aligned rather than forcing immediate and costly consolidation.

Composable architecture: Designing for evolution, not stability

The third pillar of the AXTent model is a modern data architecture that鈥檚 designed to absorb change rather than resist it. Traditional architectures usually rely heavily on rigid pipelines and tightly coupled schemas. While these work when requirements are stable, but they may collapse under regulatory change, new analytics demands, or AI-driven consumption.

AXTent replaces pipeline-centric thinking with composable services, including event-driven ingestion and processing, API-first access patterns, versioned data contracts, and separation of storage, computation, and governance.

This approach supports both human analytics and machine users, including AI agents that require direct, trusted access to data. The result is a data core that evolves without constant re-engineering, which is critical for organizations operating under continuous regulatory scrutiny or frequent structural change. AXTent allows acquired entities to plug into the enterprise architecture as domains while preserving context and enabling progressive harmonization.

The architectural compass

This framework exists for one purpose: to provide a practical, business-oriented methodology for building a reusable, decision-aligned, compliance-ready data core. It is not a product nor a platform. It is a vocabulary that鈥檚 backed by building blocks, patterns, and repeatable workflows 鈥� and it鈥檚 one that executives can use to organize data around outcomes instead of systems.

Overall, the AXTent model prioritizes data clarity over system modernization, decision alignment over model sophistication, continuous compliance over intermittent remediation, reusable data products over disconnected pipelines, and enterprise knowledge codification over one-off integration work.

In essence, organizations should move away from project thinking and toward perpetual data development, in which every output contributes to a compound knowledge base. This is the mindset shift the industry has been missing as it prioritizes AI engineering over business purpose.

In the final post in this series, the author will explain how to shift from 鈥渂uild and operate鈥� to 鈥渂uild and evolve鈥� via a data foundry. You can find more blog posts听by this author here

Key findings:

• • • AI adoption accelerates across professional services听鈥� Organization-wide use of AI in professional services almost doubled to 40% in 2026, with most individual professionals now using GenAI tools, and many preparing for the next wave of tools such as agentic AI.

    • Strategic integration and measurement lag behind usage 鈥� While AI use is widespread, only 18% of respondents say their organization tracks ROI of AI tools, and even fewer measure AI’s impact on broader business goals such as client satisfaction or revenue generation.

    • Communication around AI use remains inconsistent听鈥� While most corporate departments want their outside firms to use AI on client matters, less than one-third are aware whether their firms are doing so. Meanwhile, firms report receiving conflicting instructions from clients about AI use, highlighting a need for clearer dialogue and shared strategy around AI adoption.

Over the past several years, AI usage within professional services industries has come into focus. As we enter 2026 in earnest, the early adoption phase of generative AI (GenAI) has come and gone. Today, most professionals have experimented with some form of GenAI, and many organizations integrated GenAI into their workflows 鈥� and now, a number are preparing for the next wave of technological innovation such as agentic AI.

Given this, the question for professionals and organizational leaders has now become: What will be AI鈥檚 long-term impact on my business?

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2026 AI in Professional Services Report

To delve into this question further, the 成人VR视频 Institute has released its 2026 AI in Professional Services Report, which takes a broad view into the current usage and planning, sentiment towards, and business impact of AI for legal, tax & accounting, corporate functions, and government agencies. Taken from a survey of more than 1,500 respondents across 27 different countries, the report finds a professional services world that has embraced AI鈥檚 use but is continuing to evolve business strategy around its implementation.

For instance, the report shows that to 40% in 2026, compared to 22% in 2025 鈥� and for the first time, a majority of individual professionals reported using publicly-available tools such as ChatGPT. Additionally, a majority of respondents said they feel either excited or hopeful for GenAI鈥檚 prospects in their respective industries, and about two-thirds said they felt GenAI should be applied to their work in some manner.

At the same time, however, many are exploring GenAI tools without much guidance as to how that use will be quantified or measured. Only 18% of respondents said they knew their organization was tracking return-on-investment (ROI) of AI tools in some manner, roughly the same proportion as last year. And even among those tracking AI metrics, most are tracking mainly internally-focused, operational metrics; and only a small proportion analyzed AI鈥檚 impact on their organization鈥檚 larger business goals 鈥� such as client satisfaction, external revenue generation, and new business won.

This slow move to strategic thinking also impacts client-firm relationships. Although more than half of both corporate legal departments and corporate tax departments want their outside firms to use AI on client matters, less than one-third said they were aware whether their firms were doing so or not. From the firm standpoint, meanwhile, confusion reigns: 40% of firm respondents said they have received orders both to use AI on matters and not to use AI on matters from various clients.

Indeed, bout three-quarters of corporate respondents and firm respondents agreed that firms should be taking the lead in starting these conversations around proper AI use. Yet these discussions have not yet happened en masse. 鈥淔irms are reluctant 鈥� they claim it would compromise quality and fidelity,鈥� said one U.S.-based corporate chief legal officer. 鈥淚 think they are threatened by it.鈥�

All the while, technological innovation progresses ever quicker. This year鈥檚 version of the report measures agentic AI use for the first time, finding that already 15% of organizations have adopted some type of agentic AI tool. Perhaps more interesting, however, is that an additional 53% report their organizations are either actively planning for agentic AI tools or are considering whether to use them, indicating perhaps an even more rapid pace of adoption than we鈥檝e already seen with the speedy rise of GenAI.

Overall, the report makes it clear that most professionals do understand that change, driven by AI in the workplace, is undoubtedly here. Even compared with 2025, a higher proportion of professionals said they believe that AI will have a major impact on jobs, billing and revenue, and even the need for legal or tax & accounting professionals as a whole. The percentage of lawyers calling AI a major threat to the unauthorized practice of law rose to 50% in 2026 from 36% in 2025.

Further, this report paints the picture of a professional services world that has embraced AI, begun to see its impact, and realized that it will have broader business and industry implications than previously imagined. As a result, the time for professionals and organizations to begin planning in earnest for an AI future has already arrived.

As a corporate general counsel from Sweden noted: 鈥淲e cannot keep up with the modern-day corporations鈥� demands unless we also develop and adapt our way of working.鈥�

You can download

a full copy of the 成人VR视频 Institute’s 2026 AI in Professional Services Report听here

Key insights:

• • • AI usage in courts needs verifiable reliability鈥� Unlike other fields, errors and hallucinations caused by AI in a court setting can create due-process issues.

    • Skepticism is professional responsibility鈥� Judges’ interrogation of AI sources and accountability concerns are vital guardrails to minimizing these problems.

    • Governance over perfection鈥� Courts and legal professionals should focus on systematic management of AI hallucinations through clear protocols, human oversight, and mandatory verification to ensure veracity.

AI hallucinations have become one of the most urgent and most misunderstood issues in professional work today; and as generative AI (GenAI) moves from and interesting experiment to common usage in many workplace infrastructures, these issues can cause significant problems, especially for courts and the professionals and individuals that use them.

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Responsible AI use for courts: Minimizing and managing hallucinations and ensuring veracity

Today, AI can be used in everything from assisted research to guided drafting of documents, court briefs, and even court orders. With the development of tools supported by GenAI and agentic AI, the very infrastructure of professional work has shifted to include these offerings.

Yet, in most business settings, a wrong answer is an inconvenience. It requires minor corrections and has minimal impact. In the justice system, a wrong answer can be a due-process problem that strongly underscores the need for courts and legal professionals to ensure that their AI use is verifiably reliable when it counts.

At the same time, the direction of travel is clear: AI adoption isn’t a fad we can simply wait out, and it isn’t inherently at odds with high-stakes decision-making. Used well, these tools can reduce administrative burden, speed up access to relevant information, and help court professionals navigate large volumes of material more efficiently. The real question is not whether courts will encounter AI in their workflows, but how they will define responsible use, especially in moments in which accuracy isn’t a feature, it’s the foundation.

鈥淲hether you are a judge [or] an attorney, credibility is everything, particularly when you come before the court.鈥�

鈥� Justice Tanya R. Kennedy Associate Justice of the Appellate Division, First Judicial Department of New York

To examine these issues more deeply, the 成人VR视频 Institute has published a new report,听, which frames hallucinations not as a sensationalistic gotcha, but as a practical risk that must be managed with policy, process, and professional judgment. The report also features valuable insight on this subject from judges and court stakeholders who today are evaluating AI in the real operating environment of legal proceedings, courtroom expectations, and the daily administration of justice.

This perspective is essential. Technical teams can explain how models generate language and why they sometimes produce confident-sounding errors. However, judges and court staff can explain something equally important 鈥� what accuracy actually means in practice. In courts, accuracy isn’t just about getting the gist right; rather, it’s about precise citations, faithful characterization of the record, correct procedural posture, and language that withstands scrutiny. As the report points out, relied-upon hallucinated information isn鈥檛 merely bad output, it can lead to a potential distortion of justice.

Managing AI as professional responsibility

Crucially, the report reflects that judicial skepticism about AI is not simple technophobia 鈥� it’s professional responsibility. Judges are trained to interrogate sources, weigh credibility, and understand the downstream consequences of errors. Judges may ask, What is the provenance of this information? Can I reproduce it independently? And who is accountable if it’s wrong? These questions aren’t barriers to innovation; indeed, they are the guardrails that this innovation requires.

What emerges is a pragmatic middle ground that embraces the upside of AI use in courts while treating hallucinations as a predictable occurrence that can be managed systematically. Rather than concluding AI hallucinates, therefore AI can’t be used, the more workable conclusion is AI can hallucinate, therefore AI outputs must be designed, handled, and verified accordingly, likely with other advanced tech tools. As the report points out, courts don’t need a perfect AI; rather, they need repeatable protocols that keep human decision-makers in control and keep the record clean.

As the report ultimately demonstrates, managing hallucinations in courts isn’t about chasing perfection, it’s about protecting veracity. It’s about using the right advanced tech tools to build workflows in which the technology consistently supports the truth-finding process instead of quietly eroding it. And it’s about recognizing that in the legal system, responsibility doesn’t disappear when a new tool arrives 鈥� it becomes even more important to ensure the new tool doesn鈥檛 erode that either.

You can download

a full copy of the 成人VR视频 Institute’s 听here

Key insights:

• • • AI literacy is role-specific and essential 鈥� Courts need to move beyond general AI conversations and focus on concrete, role-based strategies that support AI readiness.

    • Balanced AI adoption is crucial 鈥� The goal for courts is not to automate blindly but rather should adopt a balanced AI-forward mindset.

    • Ongoing education and adaptability are vital 鈥� AI literacy requires continuous learning and upskilling that focus on building managers’ comfort and capability to lead their teams.

For today鈥檚 court system, AI literacy is quickly becoming a core professional skill, not just a technical curiosity. In the recent webinar AI Literacy for Courts: A New Framework for Role-Specific Education, panelists emphasized that courts need to move from holding abstract conversations around AI to enacting concrete, role-based strategies that support judicial officers and court professionals throughout their AI journey.

The webinar is part of a series from the听, a joint effort by the National Center for State Courts (NCSC) and the 成人VR视频 Institute (TRI).

The need for AI literacy is great

Courts are being urged to treat AI literacy as a foundational pillar of AI readiness, not as an optional add-on training. AI literacy is “the knowledge, attitudes, and skills needed to effectively interact with, critically evaluate, and responsibly use AI systems,” said the NCSC鈥檚 , adding that it cannot be one-size-fits-all. “The important thing to know about the definition of AI literacy is it’s going to be different for every single personnel role.”

Building a serious AI literacy strategy therefore begins with defining what success looks like for each role, and then aligning recruitment, training, and evaluation practices around those expectations.

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To support this, policy and security concerns must come before (and alongside) AI use. Webinar panelist , Chief Human Resources Officer at Los Angeles County Superior Court, described how the court started by clarifying the sandbox for safe AI use. First, the court鈥檚 generative AI (GenAI) policy sets parameters, such as prohibiting staff from using court usernames or passwords to create accounts on external AI tools. Only then, after those guardrails were in place, did the training really lean into the technical how-to of writing prompts and experimenting with tools. Policy development and skills development happened in tandem, Griffin explained.

To make space for learning in an already overloaded environment, her team lit a creativity spark with managers first, she said, giving them concrete use cases 鈥� such as drafting performance evaluations, coaching documents, and job aids. As a result, these managers, in turn, feel motivated to create room for their teams to experiment.

This, Griffin added, is all anchored in a clear, people-centered message from leadership: “We have a lot of work to do, and not enough people to do our work 鈥� and so AI is going to help us serve the court users and help us provide access to justice.”

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How to make AI 鈥渨ork鈥�

On the webinar, the conversation repeatedly returns to what lawyers and court professionals are actually doing with AI tools today and where they’re getting stuck. , Founder of Creative Lawyers, noted that despite AI’s rapid advance, many professionals are still at a surprisingly basic stage in how they use it. For example, Leonard said that users tend to treat AI as a one-way question-and-answer box instead of using it as an expertise extractor that asks them targeted questions. To combat this, she suggested that users ask AI to ask them questions to extract from their expertise.

When thinking about how to interact with AI generally, users should treat it like a smart colleague and ask themselves (and implicitly the AI) these questions:

• • • What information would this colleague need from me to do the assignment well?

    • What questions would I want them to ask me?

    • What specific task do I actually want them to execute?

    • What feedback would I give them to make the work product better?

As the webinar examined, leadership messaging needs to be explicit. AI is being adopted to augment human work, reduce burnout, and expand access to justice 鈥� not to eliminate jobs, particularly in courts that are already understaffed. For example, LA Superior Court has been meeting with unions around their GenAI policy, repeatedly affirming that they are not using AI to replace court staff, Griffin said. Instead, they show how AI can be used to demonstrate use cases, and offload repetitive tasks that will make remaining work more meaningful.

At the same time, managers themselves often feel unprepared to talk about AI, which is why building their comfort and capability 鈥� especially around explaining where the court is going 鈥� is becoming a critical managerial competency, panelists noted.

Supporting the journey

To support all of this, the TRI/NCSC AI Policy Consortium has built practical training resources that courts can plug into their own strategies. For example, the offers curated materials mapped to specific roles such as judges, court administrators, court reporters, clerks, and interpreters. Courts can use these resources as targeted supplements when rolling out AI projects to better prepare staff members who are just starting their AI journey.

Complementing this is the , an environment in which staff can safely experiment with GenAI tools without sending data back to the open internet. This gives judges and staff a place to practice prompt-writing, ask follow-up questions, and give feedback, all while staying inside a controlled environment and within the bounds of most court AI policies.

Looking ahead, the panelists argued that the most durable 鈥渇uture skills” may not be specific technical proficiencies but human capabilities, such as adaptability, creativity, critical thinking, and change leadership. In fact, HR leaders across industries largely agree they cannot predict exactly which tools or skill sets will dominate in a few years, Griffin said, and instead, courts should focus on helping managers to craft better prompts, interpret outputs critically, and lead their teams through repeated waves of technological change.

Leonard similarly urged legal organizations to move beyond basic, adoption use cases 鈥� such as document summarization and email refinement 鈥� and start exploring more creative, transformative uses that could redesign legal services and court systems to be more responsive to the public.

Finally, the webinar stressed that AI literacy cannot be a one-and-done initiative. The , published by NCSC, encourages courts to treat AI projects as catalysts for revisiting their overall literacy strategy and HR practices.

You can find out more about the work that NCSC is doing to improve courts here

Key insights:

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

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

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

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

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

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

The cost of administrative burdens

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

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

A modernizing court workforce

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

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

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

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

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

Resources available to fund statewide technology improvements

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

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

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

Moving forward, slowly and thoughtfully

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

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

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

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

Key insights:

• • • The rapid pace of AI development is testing the limits of a legal system built for process and deliberation 鈥� The shortcomings create uncertainty and challenges for both lawmakers and innovators.

    • Specialized tribunals might offer a solution 鈥� They could provide a forum for faster, more precise guidance and directives, while also ensuring decisions are grounded in expertise and applied to concrete facts.

    • A balanced approach is needed to regulate AI 鈥� If AI is constrained too much, it risks stifling innovation and reducing access to justice; however, if AI is not constrained enough, it could create serious risks.

AI is moving fast 鈥� faster than our legal system is built to move. Our courts and legislatures are designed to be contemplative, cautious, and process driven. When technology moves at lightning speed, this deliberative process creates a gap between the questions being raised and the answers we have available. Lawmakers are left to catch up and patch up, and innovators are discouraged by the legal uncertainty.

To bridge that gap, we have to think outside the box.听If we don鈥檛, AI-related disputes could pile up. Indeed, some are already in court. And claims of algorithmic bias, AI-generated harm to vulnerable individuals, and discriminatory outcomes from automated systems, are growing.听Meanwhile, courts and legislatures are working at their necessary pace鈥攁nd it鈥檚 not fast. This means AI systems will continue to be developed and deployed without clear rules or remedies.

This isn鈥檛 the first time technology has outpaced the law. The industrial revolution forced lawmakers to confront unprecedented questions of workplace safety and labor rights. Then-existing laws, systems, and structures were ill-suited to address those issues, and we were forced to adapt.

The same can be said about the AI revolution. Our laws, systems, and structures will have to adapt. If they don鈥檛, we may face serious risks that may one day become existential risks. On the other hand, if we do too much to constrain AI, we risk slowing biomedical advances, missing educational opportunities, reducing access to justice, compromising national security, and limiting the prosperity that might flow from these technologies. The balance is delicate, and the consequences are profound.

Learning from established models

In the wake of the industrial revolution, Congress created the Occupational Safety and Health Review Commission (OSHRC) as part of the Occupational Safety and Health Act of 1970. The OSHRC, an Article I independent federal administrative agency court, adjudicates disputes between the U.S. Secretary of Labor and employers, when the Occupational Safety and Health Administration (OSHA) issues citations on behalf of the Secretary for violations of the Act.

The industrial revolution forced lawmakers to confront unprecedented questions of workplace safety and labor rights. Then-existing laws, systems, and structures were ill-suited to address those issues, and we were forced to adapt.

The same can be said about the AI revolution.

Under this structure, federal administrative law judges (ALJs) issue decisions utilizing a structured system that ensures fairness and due process. The ALJs do not have regulatory or enforcement authority 鈥� that rests with the Secretary of Labor and OSHA 鈥� but their decisions have significant impact. They interpret the law, resolve disputes, and guide employers, employees, and OSHA in their understanding and application of the law.

There may be lessons to draw from this and other models such as the U.S. Tax Court and the Court of Appeals for Veterans Claims, because specialized tribunals that respond to emerging needs have proven effective.

The need for specialization & speed

What sets AI apart from other challenges is the combination of speed and reach.

It was less than three years ago, in November 2022, that ChatGPT captured public attention. In just a few months, ChatGPT reached monthly active users, becoming the fastest-growing consumer application in history. Within a year, companies began harnessing AI for scientific and medical breakthroughs, and today over a billion people use AI chatbots on a regular basis. The conversation has also shifted from basic text-based models to fully agentic AI systems. Some even warn that is on the horizon, if we鈥檙e not careful.

This trajectory underscores a simple truth: even if foundational models never achieve the kind of artificial general intelligence that some proponents predict, the systems we have today are already very powerful, adaptable, and certain to be leveraged in ways their developers may never have intended. Moreover, the speed of AI development means we only have a short window within which to build the right guardrails.

Yet, our current systems make it nearly impossible to put these guardrails in place at the pace of innovation. There is no comprehensive federal legislation addressing AI, and efforts in that direction have met resistance out of concern that broad rules could stifle innovation. In the absence of a unified framework, states have begun to act on their own, creating a patchwork of laws that address some issues while leaving significant gaps in others. At the same time, individual disputes that could shape the legal landscape move slowly through our backlogged courts, where judges 鈥� generalists by design 鈥� must divide their attention among a wide range of cases and cannot realistically conduct detailed inquiries into every emerging technology that comes before us.

Beyond our current structures

A tribunal with the right expertise and built for efficiency might be a useful tool for building the right guardrails.

Certainly not every AI-related dispute requires AI expertise. However, when a dispute concerns the guardrails on AI development, deployment, and use, adjudicators would benefit from learning how these systems work, where they fail, and the societal risks they pose. Training on ethical frameworks, human-centered design, the evolving legal and regulatory landscape, and the dynamics of AI innovation could help adjudicators appreciate both the benefits and risks of imposing certain limitations. Additionally, with some fluency, adjudicators would be better positioned to ask the right questions, recognize when expert testimony is needed, and issue decisions that are not only legally sound, but technologically informed.

Even if foundational models never achieve the kind of artificial general intelligence that some proponents predict, the systems we have today are already very powerful, adaptable, and certain to be leveraged in ways their developers may never have intended.

To be sure, designing jurisdiction for a specialized tribunal would require great care. The sheer breadth of the AI revolution may make a federal agency adjudication structure 鈥� like the OSHRC 鈥� inadequate. A specialized tribunal could also be built on a consent model, however, which would allow it to handle private disputes with mutual agreement from both parties for expert and speedy resolution. Or perhaps a specialized tribunal could serve as a resource for existing courts, who could certify technical questions for non-binding resolution, allowing them to tap into the tribunal鈥檚 expertise without surrendering their authority to decide cases.

There is certainly much to consider, including the potential drawbacks of a specialized tribunal. But this is not a call for a set system or framework. Instead, it is an invitation to think beyond the current limits of our system and ask, 鈥淲hat will it take for our legal system and institutions to keep up with AI advances? And how can we mitigate major risks, while continuing to promote and support innovation?鈥�

If we do not explore solutions beyond the limits of our current system, we risk: i) delays that allow unsafe AI practices to advance unchecked; ii) fragmentation in the absence of comprehensive legislation; and iii) overbroad, one-size-fits-all regulations that could inhibit critical innovation.

Looking for balance

A specialized AI tribunal might offer something for everyone. For those who worry that regulation is too sparse or too slow, it would provide a forum for faster, more precise guardrails and guidance. For those concerned that sweeping regulations would limit innovation or miss the mark, a specialized tribunal could deliver narrowly tailored decisions, rather than broad, over-inclusive rules.

A specialized tribunal might also spare us the impossible task of trying to legislate for every hypothetical future problem. Instead, issues could be resolved as they come 鈥� with decisions grounded in expertise and applied to concrete facts.

No framework is perfect, but when the pace of change is unprecedented, the competing interests critical, and the consequences profound, we need fresh ideas that bring all concerns to the table.

Judge Braswell wishes to thank Judge Patrick Augustine for his OSHRC insights

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