Key insights:

• • • UK law firms face a more selective growth market in 2026Ěý— Client demand remains steady, but external legal spend expectations have cooled, with growth concentrated in areas such as Regulatory, Labor & Employment, and international work.

    • Legal expertise alone is no longer enough — UK legal buyers increasingly favor law firms that combine technical excellence with commercial judgment, business understanding, and practical guidance aligned to client priorities.

    • AI adoption is becoming a client expectationĚý— Corporate legal teams are moving faster than their outside law firms on GenAI, and many UK legal buyers now expect outside counsel to use AI to improve efficiency, workflows, and the quality of legal work.

The legal market in the United Kingdom today has shifted into a new normal. While law firms saw an explosion of demand and spending immediately following the pandemic, increasing client caution has resulted in a shift in priorities. Today’s law firms cannot simply rely on their old ways of providing legal service to succeed, as UK clients expect firms to combine expertise, commercial judgment, international reach, and visible AI-enabled improvements in how legal work is delivered.

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2026 State of the UK Legal Market

A new report from the Thomson Reuters Institute, “2026 State of the UK Legal Market,” reveals how the UK legal market is shifting, as more judicious clients are beginning to force law firms to reassess their strategy. Overall anticipated net spend from legal clients has seen declining growth rates in recent years, and while some practices like Regulatory and Labor & Employment continue to see strong demand growth, other practice areas such as Insurance, IP, and Disputes face potential contraction.

This shift is also guided by emerging buyer preferences. The report reveals an increasing commerciality to the UK legal market, one in which clients increasingly favor advisors that combine legal excellence with commercial judgement, and those that are leveraging AI to bolster not only efficiency but improve the overall legal work product.

Taken as a whole, the report paints a picture of clients that now are moving faster than their outside legal advisors, strengthening their internal capabilities, and setting clearer (and higher) expectations. This means that UK law firms cannot rest on their laurels, as clients increasingly push their outside firms to keep up with new business challenges.

The market is cautious, but opportunity remains

The report reveals that UK legal buyers are more cautious about external legal spend than they have been at any point in the last five years. That may mean law firms can no longer rely on the broad-based demand that defined the post-pandemic period and instead need to be more precise about where opportunity exists — and where it doesn’t.

The report tracks buyer sentiment through net spend anticipation (NSA), which measures the share of buyers expecting to increase external legal spend over the next 12 months minus those expecting to decrease it. Since its 2021 peak, UK NSA has fallen steadily to +5 percentage points in 2025, returning the market to the more stable, single-digit baseline that was seen before the pandemic.

For those law firms looking to capture increased business, the report makes clear that legal expertise is now the price of entry, not the point of differentiation. The firms that stand out will be those that know how to apply their expertise in ways that reflect the client’s business realities.

Indeed, that is becoming even more important as corporate legal departments face growing pressure to demonstrate their own value to the wider organization, and they’re increasingly pointing to improvements in their own quality and effectiveness even before mentioning cost savings, efficiency, or time savings. Not surprisingly, more than one-third of UK legal buyers now cite business savviness as a reason they favor a particular law firm.

To help demonstrate their internal value, clients are pushing their outside law firms to leverage advanced technology to improve the overall effectiveness of legal work. Of course, this has resulted in a clear gap, the report notes, between how corporate legal teams are moving and how law firms are responding. For instance, the report shows that more than half of UK corporate legal respondents say their organizations are already using GenAI tools across the business, compared with just about one-third law firm respondents who said this.

That difference in outlook matters because clients increasingly believe AI will become a larger part of how legal work is delivered, and they’re not content to simply wait and see whether their outside counsel will fully adopt the technology. Indeed, corporate legal departments are expecting their outside law firms to keep pace with how legal work is changing, and they will reward those firms that do.

You can download

a full copy of the Thomson Reuters Institute’s “2026 State of the UK Legal Market” by filling out the form below:

Key points:

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

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

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

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

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

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

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

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

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

When two jagged edges meet

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

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

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

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

The matchmaking discipline

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

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

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

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

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

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

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Key insights:

• • • Real-time tax compliance has restructured the tax function — Dozens of nations now require structured invoice data in real time, with the EU mandating cross-border digital reporting by 2030. The traditional file-and-wait audit cycle is gone now, replaced by clearance regimes that can freeze multi-million-dollar invoices for nonconforming data.

    • Regulators have pulled ahead of the businesses they oversee — Tax authorities in mature CTC jurisdictions now arrive at audits with structured transaction data already processed by their own analytics. Government turnaround times that took months now take weeks, forcing multinational tax leaders to compress multi-year roadmaps into 12- and 18-month cycles to keep up.

    • The lessons travel beyond tax — There are two ways to lose this race: Outrun your own controls or surrender entirely. Both showed up in Las Vegas, and both will show up in every other regulated profession over the next decade.

LAS VEGAS — TheĚý sold out. A guest list that included tax directors from Amazon, Walmart, and Procter & Gamble, OpenAI’s tax department, the Big Four, ¶¶ŇőłÉÄę and every other major tax software provider in the market spent three days at the Aria with pool deck, casino floor, and restaurants worth lingering over all a few steps away.

The room had every reason to spend its evenings somewhere else other than a sunless conference room talking about tax. Yet almost no one did. They were too busy grappling with an arms race the corporate audit side had begun to suspect it was losing.

And it’s one they cannot afford to lose.

End of the traditional model

The arms race is real-time tax compliance, and it has dramatically restructured the ground beneath the tax profession in less than a decade. By April, more than 60 jurisdictions have moved or are moving to continuous transaction controls. Italy and Hungary were early; Poland, France, Belgium, Brazil, Saudi Arabia, India, and Singapore are now operational or imminent, and countries like Spain, Germany, the United Kingdom and the United Arab Emirates are on the way. The European Union has locked onto a 2030 deadline for cross-border real-time digital reporting and a 2035 backstop for harmonizing what’s left.

The traditional model — issue an invoice, file a return weeks later, audit when the auditor gets around to it — no longer exists in those jurisdictions. Tax authorities now see the transaction as it happens, validates it in structured form, and pre-fills the return on the taxpayer’s behalf.

What this new process has done to the tax function is fundamentally alter its structure in a way leaves practitioners reeling. The job used to be a craft of Excel, judgment, and institutional memory. Now, at the high end, it has become as much a data science problem as an accounting one.

The arms race is real-time tax compliance, and it has dramatically restructured the ground beneath the tax profession in less than a decade.

Attendees at TEI’s 2026 Tax Technology Seminar polled themselves on tooling, and the answers came back as a list of data pipelines that dozens of attendees seemed to favor: Alteryx, Power Platform, Snowflake, Databricks, Microsoft Fabric, & Palantir Foundry. These platforms are running agentic AI systems against historical filings, deploying validation agents to critique their own outputs, and using AI-driven image-to-text solutions to pull structured data out of state tax notices that never arrive in the same format twice. They are data integration pipelines in 15 minutes that would have sat in an IT queue for two months before being answered.

They have little choice as the stakes are far higher and the challenges far more demanding than they used to be. In a clearance regime, an invoice has no legal force until the tax authority returns its identifier. Did you submit the wrong VAT ID, malformed schema, or mismatched master data? Congratulations! Your invoice is rejected. That means the truck doesn’t move, the buyer doesn’t pay an invoice that may be in the millions of dollars and then the penalties stack on top. Italy, for instance, charges a fee of 70% of the disputed VAT.

And then there are the audits.

Outgunned

The audit isn’t an occasional event anymore. In government jurisdictions with mature continuous-transaction-control tax regimes, it is a conversation that started weeks before the auditor walked in, on data their analytics had already processed.

A speaker on a seminar panel led by Deloitte and ¶¶ŇőłÉÄę described the dynamic plainly: Tax authorities in those jurisdictions have arrived at audits already knowing more about the transactions than the companies and their in-house audit teams sitting across the table. Not because anyone is hiding anything, but because the data arrived at the tax authority in structured form, in real time, and the authority had run its analytics on it before the meeting was even on the calendar. One panelist said this represents “a shift from us preparing returns to us answering notices on the data that’s been shared.”

What the room kept circling around, however, was that regulators have not just kept pace with their counterparties, they’ve now pulled ahead. Singapore, one panelist noted, is doing more with AI than even major companies. Indeed, government turnaround times that used to take months are now closing in weeks, which is forcing multinational tax leaders to compress their multi-year roadmaps into 12- and 18-month cycles — not because they want to but because their counterparties already had.

The lesson that corporate tax functions have been forced to absorb is that there are two ways to lose this race, and both were on display at TEI’s 2026 Tax Technology Seminar as cautionary tales.

This asymmetry is structural, and that is what makes it an arms race rather than a transition. There is no version of this dynamic in which the company being audited wins by being more careful, more thorough, or more well-prepared at the end of the quarter. The advantage now accrues to the side with the fastest and cleanest pipelines, that runs the smartest AI, and that understands the way these increasingly complex systems interact. Increasingly, that winning side is the government. And, more alarming, this isn’t just a problem for this particular industry — tax just happened to get here first. However, it’s coming for everyone.

Two ways to lose

The lesson that corporate tax functions have been forced to absorb is that there are two ways to lose this race, and both were on display at TEI’s 2026 Tax Technology Seminar as cautionary tales. The first is to outrun your own controls. AI coding tools that let a tax analyst build a working data integration pipeline in 15 minutes are genuinely valuable; they also let that same analyst deploy something nobody else has reviewed, documented, or knows how to maintain. An OpenAI panelist conceded the point when an audience member asked about the security implications of vibe coding — clearly, a new capability is also a new problem.

The second way to lose is harder to talk about. One panelist described, to attendees’ general dismay, hearing of companies that have given up on compliance entirely — instead, they pad their numbers with a safety margin and treat the eventual audit as the cheaper of the two costs. The panel recoiled — one member responded with a flat “Do not do this.” However, the anecdote landed because it isn’t theoretical. When the gap between what regulators can see and what your team can produce becomes wide enough, surrender starts to look rational.

Playing to win

Of course, the attendees at TEI’s 2026 Tax Technology Seminar were not surrendering. If they were, they’d have been at the pool deep into their third cocktail. Or they’d have been on the casino floor or were about to catch an afternoon show. Instead, day after day, the tables filled, the exhibit hall ran hot, and the room was buying, listening, and building.

The game has changed and the stakes have risen — and the room is dead set on playing to win.

You can find more ofĚýour coverage of Tax Executives Institute events here

Key insights:

• • • Prediction markets sit in a regulatory gray zone — Prediction markets’ economic function often looks much closer to gambling than traditional finance.

    • That ambiguity creates an AML blind spot — This blind spot allows potentially weaker controls around KYC, source of funds, sanctions screening, and suspicious activity reporting.

    • Banks and payment processors should focus on actual risk, not labels — Reputational, legal, and financial crime risk exposure can arise long before regulators clarify the rules.

Prediction markets have grown into a multi-billion-dollar ecosystem, offering the ability to enter into a contract to predict the outcomes on everything from elections and sports games to economic data and weather events. Yet as these platforms expand, they operate in a regulatory gray zone that raises serious questions for banks, payment processors, and compliance professionals.

Yet, the classification question that regulators and financial institutions continue to debate is not merely academic. It determines whether prediction market platforms will face the same anti-money laundering (AML) and know-your-customer (KYC) obligations as casinos and sportsbook venues, or whether prediction markets can continue to operate with minimal compliance oversight. This distinction has real consequences for the financial system.

“Prediction markets are not just a classification problem, they represent a structural gap in how financial crime risk is currently understood and managed,” says James Lephew, Founder & CEO of , a Charlotte-based consulting firm that serves major gambling operators and financial institutions globally.

Clarification is required in classifying this sector

Prediction markets occupy an ambiguous middle ground. Market operators position their platforms as financial derivatives or forecasting tools rather than gambling venues, emphasizing price discovery and statistical analysis over chance-based wagering. A contract on the outcome of a presidential election or a sports event, they argue, reflects crowd-sourced probability estimates grounded in information aggregation, not gambling luck.

Yet the fundamental mechanics raise legitimate questions. A user who buys a contract predicting that a candidate will lose an election is, in economic terms, wagering money on an uncertain outcome. The distinction between betting on a football game and trading a contract on the outcome of that same game becomes difficult to defend from a regulatory standpoint — and this classification matters enormously.

The distinction between betting on a football game and trading a contract on the outcome of that same game becomes difficult to defend from a regulatory standpoint — and this classification matters enormously.

If prediction markets are treated as gaming operations, they trigger Title 31 obligations under the Bank Secrecy Act, including currency transaction reporting, suspicious activity reporting (SAR) requirements, and comprehensive KYC procedures. If on the other hand, prediction markets are classified more akin to financial markets, these requirements may not apply. Currently, many prediction market platforms claim financial market status, allowing them to operate outside gaming regulations and with potentially weaker AML controls.

There is a compliance gap

Without clear regulatory classification, prediction markets create a significant AML blind spot. Casinos must report cash transactions exceeding $10,000, conduct source-of-funds reviews, and maintain detailed customer profiles. Sportsbooks face licensing requirements, geolocation checks, and responsible-gaming safeguards. Prediction market platforms, by contrast, often operate with minimal reporting obligations.

This gap introduces concrete risks. Digital wallets and cryptocurrency channels can obscure the source of funds. Structuring and layering of sources become easier without robust verification, further clouding who exactly playing in these markets. Collusive trading through multiple accounts allows value transfer that may go undetected. And VPN use and foreign payment channels can enable sanctions evasion.

Further, without mandatory SAR reporting, suspicious patterns tied to money laundering, terrorist financing, or market manipulation may never reach law enforcement.

“What we’re seeing is an AML blind spot,” says Lephew. “Platforms enabling financial flows with characteristics of gambling, but without the controls that regulators would normally expect.” Until classification catches up with the technology, he adds, this blind spot remains open — and exploitable.

Why this matters for banks and processors

Banks and payment processors that support prediction market platforms may carry significant reputational and legal risk if they haven’t conducted thorough due diligence — and they cannot rely on a platform’s self-classification as a financial market or forecasting tool. Nevada and other jurisdictions are actively examining whether these platforms constitute gambling, echoing concerns from the American Gaming Association that products carrying similar economic risks deserve similar regulatory treatment.

If a product allows participants to wager on uncertain outcomes and creates risk that is substantially similar to gambling, it should face AML and customer identification requirements proportionate to that risk.

“Risk must be assessed based on how the product actually behaves, not how it is marketed,” Lephew explains. And that means evaluating whether a platform applies robust KYC procedures, verifies the source of deposits and beneficial ownership, screens against sanctions lists, reports SARs to the government, prohibits contracts on high-risk events such as assassinations or terrorism, and uses geolocation controls to block users in restrictive jurisdictions. Those answers matter far more than whatever label the platform chooses, Lephew says.

The path forward

Regulators have several options. One approach applies gaming regulations uniformly, treating all prediction markets with economic characteristics similar to gambling as gaming operations subject to Title 31. A second approach creates explicit financial market classification with statutory AML obligations and enhanced scrutiny of high-risk contracts. A third option adopts a tiered or risk-based framework, classifying contracts on lower-risk events such as economic data or weather under financial market rules, while sports and election markets could face enhanced scrutiny. Violent outcome markets would be prohibited entirely.

Regardless of which path regulators choose, the principle should be the same: Classification should follow economic function. If a product allows participants to wager on uncertain outcomes and creates risk that is substantially similar to gambling, it should face AML and customer identification requirements proportionate to that risk.

Financial institutions should not wait for regulatory clarity. They should apply rigorous due diligence now, treating prediction markets with a heightened level of scrutiny appropriate to their actual risk profile rather than their claimed legal status.

The goal is not to eliminate prediction markets, but to ensure they operate within a framework that prevents money laundering, terrorist financing, and market abuse. “If it looks like gambling, behaves like gambling, and carries the same financial crime risk, it should be regulated accordingly,” Lephew notes. “Anything less creates systemic exposure.”

You can find out more about the challenges financial institutions face in their anti-money laundering efforts here

• • • The reactive phase is partly a mindset problem — More than half of tax departments remain stuck in reactive, compliance-focused operations, not only because of frozen budgets, but because of cost-center thinking that shapes cost-center behavior.

    • The value is there, but the measurement isn’t — Two-thirds of tax professionals say their department’s technology investment has already enabled more strategic work; yet 22% say they track no performance metrics at all, making that value invisible to the people who control the budget.

    • The rebrand starts internally — With AI integration timelines compressing to between 1 and 2 years, tax departments that shift their posture now by measuring wins, designating leadership, and building the business case will be better positioned to lead — and those that don’t will fall further behind, faster.

Apart from the sales department, most other departments within a business are simply viewed as a cost center, and the tax department is no exception. However, like so much of that thinking, this view isn’t quite accurate because it is the tax department that can uncover the most savings for the business.

You need not look further than recent data that shows while 67% of tax professionals say their department’s technology investment has already enabled them to do more strategic work, 22% say they track no performance metrics at all, making it difficult to demonstrate the tax department’s value to the C-Suite.

Given this, it’s somewhat unsurprising that this cost-center view persists. Worse yet, is often internalized by in-house tax teams themselves. It is one thing to be viewed and treated as a cost center but to act like one is a different matter.

So, what if the bigger problem isn’t how the rest of the business views the tax department but instead how the department views itself?

The , from the Thomson Reuters Institute and Tax Executives Institute, reveals a profession that knows it is capable of far more than it is currently delivering. And yet the same patterns repeat: Budgets stay flat, technology adoption stays slow, and a majority of departments remain stuck in a reactive phase in regard to their technological development that has “remained stubbornly consistent over the past few years,” according to the report.

That’s not just an organizational failure; rather, that’s a mindset problem — and it starts from within the tax department.

The choices we keep making

The report outlines a Technology Maturity Curve that maps a progression in tech development from chaotic through reactive, proactive, optimized, and predictive stages.

This year, 64% of respondents placed their tax department at the chaotic or reactive end of the spectrum — up from 57% last year. The reactive phase is the operational definition of a cost center: Heads-down, output-focused, and disconnected from the broader business.

The report reveals something even more important. In those cases in which the budget isn’t the primary constraint, behavior doesn’t change. Almost one-third of respondents (32%) said their strategy for addressing capacity constraints is process optimization — without new technology or additional hiring. Not because they can’t pursue more, but because that’s the default mode.

One respondent put it plainly: “…Our company as a whole is making significant changes, but the tax department is typically an afterthought in those decisions.”

This raises a question that’s worth asking: Who taught the company to treat tax as an afterthought?

There’s evidence showing that tax departments are more

The data to challenge the cost-center identity isn’t missing; rather, it’s just not being captured or communicated to the C-Suite.

Two-thirds of respondents (67%) said their tax department’s technology investment over the past three years has already enabled a shift toward more strategic, proactive work, such as data analytics, forecasting, risk assessment, and decision-making support. Among larger departments, nearly half (48%) are now spending more time on these higher-value activities. This clearly shows that companies that have invested in tax automation are reporting real results, such as improved accuracy, reduced errors, lower costs, and streamlined workflows.

And yet, 22% of tax departments track no technology performance metrics at all, according to the report — not time savings, not error reduction, not ROI. Nothing.

While 67% of tax professionals say their department’s technology investment has already enabled them to do more strategic work, 22% say they track no performance metrics at all, making it difficult to demonstrate the tax department’s value to the C-Suite.

That is cost-center thinking in action — the belief that it’s the job of the tax department to do the work, but not to prove its value. However, what isn’t measured can’t be communicated — and what can’t be communicated can’t change the perception, either internally or externally.

The rebrand starts with how departments see themselves

The most important audience for the tax department’s rebrand isn’t the C-Suite. It’s the department itself.

That means tracking wins and building a formal business case for investment — grounded in hard ROI and cost savings, which the report identifies as the metrics that are most important to Finance and IT, the two functions that frequently share control of the tax technology budget.

It also means getting serious about leadership. The portion of tax departments with a designated person leading tax technology strategy jumped to 88%, from 51%, in a single year. However, a title only goes so far; and the report is clear — that role only works when backed by a team that believes it belongs at the decision-making table.

Finally, this rebranding means treating AI as an opportunity, not a threat. The majority of tax professionals have compressed their expectations for AI integration to 1–2 years, from 3–5 years, with 7% saying AI is already central to their workflow. Those departments still locked in cost-center mode are the least prepared for that shift — because cost centers don’t invest ahead of the curve.

The narrative changes when the mindset changes

No one is going to rebrand the tax department on its own, it has to come from within. Further, it has to be built through deliberate measurement, consistent communication, and a shift in how tax professionals think about our own work.

Your department is not a cost center. The work proves it, and the data backs it up. Now, you should act like you believe it.

You can download a fully copy of the , from the Thomson Reuters Institute and Tax Executives Institute, here

Key takeaways:

• • • The AI tools demonstrated at the 2026 TEI Tax Technology Seminar were genuinely capable — These included agentic systems running live, nine-year-olds building software by voice, and automation pipelines deployed by major tax departments. The question of Does this work? is effectively settled.

    • That progress shifted the conversation to harder problems — Some of these problems are hallucinations that fail silently, governance vacuums in which tax rarely owns AI implementation, training rollouts that collapse when people aren’t ready, and rising token costs that could entirely change the economic case for automation.

    • The community’s defining posture wasn’t skepticism or hype — Instead, it was honest reckoning. Tax leaders believe in the tools and were actively deploying them but also are refused to treat capability as a substitute for the institutional work of process, ownership, and oversight.

“I think you are a very bad man,” said Dorothy.

“Oh, no, my dear; I’m really a very good man, but I’m a very bad Wizard, I must admit.”

— The Wonderful Wizard of Oz, L. Frank Baum

LAS VEGAS — I arrived in Las Vegas a day early for the , which gave me one free evening before three days of packed sessions. Little question as to what I was going to do: The Wizard of Oz show at the Sphere.

It’s spectacular. The Sphere wraps you in imagery at a scale so vast if feels like you’re going to fall into it. The tornado shakes you like it’s going to rip the entire building apart and fling you to Oz right alongside Dorothy. The technology is genuinely, thrillingly good… and that’s what makes the fissures so disorienting when you spot them. A munchkin’s head rendered as a 2D .png with a visible gap where the neck should be. A bad CGI effect. Dorthy flickering at the edges like a bad cutout on a green screen. You don’t catch the tech glitches when the spectacle is unconvincing; rather, you catch them precisely because it’s so good that the gaps have nowhere left to hide.

The next morning, I walked into the TEI Tax Technoloy Seminar and found three days of panels that played out the exact same dynamic — except the stakes were far more real.

A very good man

TEI organizers opened with the obvious joke: “We’re careful to limit the number of AI sessions,” they noted, before audibly pondering whether it was time to just rename the whole thing. Fair question, given how much has changed over eight annual iterations of this get-together. Indeed, if you’d been dropped into this event from its first meeting nearly a decade ago, you’d think you’d been dropped into Oz.

One presenter described her elementary-school-aged children building video games by dictating instructions to a coding tool, then showing the games running. That alone would have been science fiction five years ago. However, the room was full of it. OpenAI sent four members of its own tax department to demonstrate live automation pipelines. Google and Microsoft walked attendees through building AI agents with nothing more than a mouse and keyboard, making it look so easy my grandmother could have made it work.

One presenter described her elementary-school-aged children building video games by dictating instructions to a coding tool, then showing the games running. That alone would have been science fiction five years ago.

Down the hall, the advanced tax systems that many industry visionaries were dreaming about just two years ago weren’t theoretical anymore — they were running live. Tax directors from Amazon, Walmart, and a dozen other household names sat alongside Big Four advisors and every major tax software provider through three days of sessions, all of it sold out.

We were definitely not in Kansas anymore. Nor was this the AI of two years ago, the one that could draft a passable email or a poem but couldn’t so much as parse a spreadsheet. This was something materially different. The tools had crossed a threshold. They worked, and everything the profession had been promising for years was alive and functioning in the room.

And that changed the conversation entirely.

A very bad wizard

When the technology was half-baked, the debate was simple: Is this even possible? Skeptics said no, enthusiasts said give it time, and everyone argued about capability.

The 2026 TEI Tax Technology Seminar was the place where that argument effectively ended — not because the skeptics lost, but because the question became irrelevant. The tools were plainly, demonstrably good — indeed, a nine-year-old could use them and was.

The new question that arose was harder and less comfortable to discuss: What can’t AI do?

The room answered honestly and brutally. Someone described uploading a tax schedule to an AI agent and getting numbers that didn’t look right. When challenged, the AI confessed: I couldn’t open your file, so I was just telling you what you wanted to hear.

That anecdote landed differently than it would have two years ago. Back then, it would have been evidence that AI wasn’t ready. At the 2026 TEI Tax Technology Seminar, in a room in which people had just watched live agentic demos and were actively deploying these tools, it was evidence of something more unsettling: AI doesn’t fail loudly anymore. It fails quietly and even politely.

AI performs competence it doesn’t have, at a level of sophistication that’s just good enough because it is genuinely smarter than it was a few years ago, and it will get away with it unless a human knows enough to push back. Like its counterpart in Oz, this makes an AI tool is a very good man — genuinely useful, genuinely capable — and sometimes a very bad wizard. It can’t do the thing you actually need it to do on its own, but it may try to trick you into thinking it did.

AI performs competence it doesn’t have, at a level of sophistication that’s just good enough because it is genuinely smarter than it was a few years ago, and it will get away with it unless a human knows enough to push back.

That theme echoed across three days of honest, sometimes uncomfortable conversations that went beyond just the technology itself. A transformation director confessed to deploying a training program across dozens of global clients and failing spectacularly. A tool designed to save two hours of work suddenly consumed an entire day because the people who’d actually had to use it hadn’t been consulted. Others described Alteryx workflows nobody could explain because the person who built them had left the company without documenting the logic.

And, more concerning, when the room was polled on whether the tax function actually owns AI implementation at their company, two hands went up out of more than 50. Human-in-the-loop was a constant refrain, of course, but attendees confessed to grappling with how to review an ever-increasing volume of work when the errors were increasingly polite, quiet, and technical.

Of course, the professionals at the seminar weren’t dismissing the technology, which is what made the honesty remarkable. As one senior director said flatly: “You will not survive in this field if you don’t have a change mindset.” They believed in the tools, and they were buying them, deploying them, building around them. They just refused to pretend the tools alone would be enough.

Going home

Overall, the 2026 TEI Tax Technology Seminar was the place that the tax technology community stopped debating whether the Wizard was real and started grappling with the fact that he couldn’t get them home.

That’s not disillusionment; indeed, it’s the opposite. Dorothy doesn’t have her crisis when Oz looks fake, she has it after she meets the Wizard and discovers he’s real but insufficient — that his balloon won’t get her home. And unlike Baum’s Wizard, the magic isn’t a fraud — which is precisely what makes the problem harder. A humbug you can dismiss, but real capability that still can’t get you home? That’s the problem you actually have to solve.

Like Dorothy, today’s tax leaders will have to click their ruby slippers themselves.

You can find more of our coverage of Tax Executives Institute events here

Key highlights:

• • • Technology satisfaction is in freefall and needs human-side investments — Satisfaction with tax technology dropped sharply to just 34%, from 56%, in a single year, even as many tax departments continued investing in tools.

    • Training cutbacks are accelerating, as the AI era deepens — Only 50% of tax departments provided technology training in 2025, down from 59% the prior year.

    • Hiring changes reveal a false choice between tax expertise and tech fluency — After years of prioritizing tech and IT hires (which were 57% of new roles in 2024), tax departments sharply reversed course, with 62% of new hires in 2025 emphasizing tax expertise over technology skills.

After years of investing in tax technology, corporate tax departments have yet to see peak efficiency because of underdevelopment in workforce training and a growing mismatch between what advanced AI tools can do and what employees can handle. In fact, the , from the Thomson Reuters Institute and Tax Executives Institute, reveals that satisfaction with tax technology has plummeted to just 34%, from 56%, in a single year.

That leaves many tax departments struggle with a widening frustration gap between what they want to achieve and what their current tools will allow. The key to closing this chasm is for heads of tax to reinvest in the human-side of their technology capabilities.

Tech competency remains a challenge

Only 9% of tax professionals rate their colleagues as very competent with technology, according to the report. The majority (60%) said they consider their teams merely somewhat competent, while nearly one-third admit their departments lack technological competence altogether.

What makes this especially alarming is that larger companies with more resources are almost three times more likely to report competency gaps, with 39% of professionals saying this, compared to just 15% at smaller firms. Indeed, these larger organizations are the ones that have invested most heavily in sophisticated tech stacks and should theoretically have the most capable users.

Perhaps a reason for this competence gap is the failure to invest in consistent technology training and knowledge-sharing among peers. Despite being one of the most cost-effective performance levers available, only 50% of corporate tax professionals surveyed said their departments provided technology training in 2025. This is down from 59% the previous year.

This training deficit has consequences because most corporate tax departments remain stuck in the reactive or chaotic phase of technological maturity. Meanwhile, AI timelines are compressing rapidly. In fact, 39% of tax professionals said they now expect AI to be central to their workflow within 1 to 2 years, up from the 31% who thought it would take that long just last year.

Pendulum swing in hiring

The 2026 Corporate Tax Technology Report also reveals a dramatic reversal in hiring priorities that deserves careful attention. In 2024, 57% of new tax department roles were dedicated to tech/IT expertise, with only 24% prioritizing tax knowledge. By 2025, the script had completely flipped, with 62% of new hires now emphasizing tax expertise.

At smaller companies (those with revenue of less than $1 billion), the swing is even more extreme. In fact, 100% of new hires are now those with tax expertise rather than technology specialists.

This pendulum swing likely reflects a correction after years of heavy tech/IT hiring combined with greater technological maturity that subsequently requires less technology expertise. At the same time, however, the solution is not one or the other; rather, hiring for both makes the most sense. In fact, the data supports this as hybrid tax/tech roles are on the rise, according to the report.

4 actions corporate tax leaders should take now

While the data makes the problem of this frustration gap clear, the more pressing issue is what tax leaders can do about it right now. Four concrete actions stand out:

1. Make training non-negotiable — If corporate tax leaders are investing in technology but not in developing their people’s ability to use it effectively, they are wasting money. Make formal training — along with mentoring and peer knowledge-sharing — a performance requirement.

2. Hire for the future — The pendulum swing back to tax expertise is understandable, but it’s essential that heads of corporate tax departments do not overcorrect. Prioritize candidates who demonstrate both deep tax knowledge and technological fluency or invest in upskilling current staff with explicit development paths to build in the missing capability.

3. Track what matters — Two-thirds of tax departments now measure time savings and efficiency gains, while 55% track accuracy improvements. In addition, it is important to track where your corporate tax department staff are struggling with tools and where additional training or process optimization could unlock value.

4. Prepare for the AI acceleration — With 39% of corporate tax professionals expecting AI to be central to their work within the next 1 to 2 years and another 15% expecting it within a year, corporate tax executives must start experimenting with AI for technical research, compliance automation, and document analysis to build the team’s comfort and competency through hands-on experience.

The bottom line

The frustration gap among corporate tax professionals highlights the mismatch between advanced technological capability and the human capacity to leverage it. As one survey respondent described: “Technology is extremely important to reduce manual processes and help reduce errors. I don’t see a path for any tax department to not lean into technology.”

However, leaning into technology without investing equally in your people is a recipe for disillusionment. The 56% dissatisfaction rate with current tech stacks underscores the frustration in the human-technology relationship and the perception that the technology tools are not solving users’ problems very well.

Those corporate tax departments that will thrive in the AI era will be the ones that invested in building technological competence, hired for hybrid capabilities, and created cultures of continuous learning. The technology maturity curve and the talent maturity curve must ascend together.

You can download a full copy of from the Thomson Reuters Institute and Tax Executives Institute here

Key insights:

• • • AI as a force multiplier — Advanced analytics now reveal financial and behavioral anomalies that traditional monitoring systems routinely miss, giving executives a clearer view of emerging risks.

    • Geospatial and digital intelligence converge — Intelligent networks like OSINT, ADINT, and location-based data expose hidden networks and movement patterns, improving the detection of money laundering, trafficking, and smuggling operations.

    • Enterprise risk strategies must evolve — Organizations that integrate AI-driven intelligence across compliance, security, and operations can respond faster, reduce blind spots, and operate with greater resilience during high-risk events.

Illicit financial activity has always evolved faster than the systems designed to stop it. And today, the speed and sophistication of criminal networks are accelerating in ways that traditional compliance processes can no longer match. Major international events, such as the 2026 FIFA World Cup, bring millions of visitors, heightened commercial activity, and a surge in cross‑border movement, all creating fertile ground for exploitation.

AI as an intelligence multiplier

In this environment, financial institutions are on the front lines of detection and mitigation, and corporations must strengthen their ability to detect hidden risks. AI — particularly when combined with digital intelligence sources, behavioral analytics, and geo-referenced data — has emerged as the most powerful accelerator of that transformation.

Among all of this high-volume activity, AI is redefining how institutions detect early-stage indicators of illicit activity. Instead of relying solely on manual reviews or rule-based monitoring, organizations are increasingly deploying systems capable of analyzing vast volumes of structured and unstructured data at once. Three capabilities are shaping this new frontier:

Open-source intelligence (OSINT) — Criminal activity, even when intentionally concealed, tends to leave trace signals online. OSINT tools can examine social platforms, online marketplaces, media sources, forums, and digital discussion channels to uncover suspicious behavioral patterns, potential recruitment or exploitation signals, inconsistencies between official identification and online presence, or clusters of accounts linked by shared attributes. For many executives, OSINT has become an indispensable layer of enhanced due diligence, risk scoring, and early threat detection long before suspicious activity appears in financial records.

Advertising intelligence (ADINT) — ADINT focuses on metadata produced by mobile applications and digital advertising ecosystems. While it does not expose personal identifiers, it reveals mobility patterns, device behavior, and clustering anomalies. This type of intelligence becomes particularly powerful during large-scale events because of the ability to monitor the movement of devices across high-risk corridors, identify unusual concentrations of activity near event venues or border regions, or detect digital behavior consistent with organized criminal logistics. ADINT introduces a geographic and behavioral dimension to risk that enables institutions to understand not only who a customer appears to be, but where they go, how they behave, and whether those patterns align with legitimate economic activity.

AI-enhanced investigations — Modern platforms now merge financial data with OSINT and ADINT inputs and then apply descriptive and generative AI (GenAI) to draw connections that would be impossible to detect manually. These systems can classify digital communications by sentiment or intent, identify unusual financial behavior within seconds, convert large datasets into actionable intelligence summaries, translate and interpret foreign-language content, and map networks through recurring metadata or visual similarity. For decision-makers and organizational stakeholders, this shift represents a dramatic reduction in blind spots and a faster escalation pathway when emerging threats surface.

Why financial institutions and corporations must lead

Human trafficking, migrant smuggling, and money laundering cannot function at scale without the financial system. Even when exploitation occurs offline, profits eventually make their way into the formal economy through remittances, structured cash movements, shell companies, digital wallets, recruitment payments, or short-term rental arrangements.

AI enhanced investigations can help institutions identify subtle but meaningful indicators, such as coached or inconsistent customer responses, accounts linked through shared devices or addresses, rapid deposits followed by immediate withdrawals, purchases that do not correspond to a customer’s risk profile, payments directed to unverifiable recruiters, unusual patterns of short-term housing across multiple individuals, or transaction flows that follow established exploitation routes.

Illicit financial activity has always evolved faster than the systems designed to stop it. And today, the speed and sophistication of criminal networks are accelerating in ways that traditional compliance processes can no longer match.

All this information already exists inside institutional data today; AI simply makes it visible and usable much more easily and quickly.

While financial institutions are central in detecting illicit finance, companies across multiple sectors face heightened exposure during large events. Hospitality, logistics, transportation, construction, real estate, and digital services all see risk intensifying as demand surges and oversight becomes more complex.

Those senior leaders who responsible for operational continuity should integrate AI-powered monitoring into their internal controls. This can help detect unusual workforce recruitment patterns, unexpected badge or access activity, subcontractor behavior that conflicts with declared operations, repeated presence in high-risk zones, or digital communications that hint at coercive or exploitative conduct.

In the fight against illicit finance, technology is no longer optional. Indeed, it is our most powerful ally.

You can find out more about the fight against illicit finance and money laundering here

Key insights:

• • • Conflict of interest doesn’t start with bad intent — Often, conflict of interest starts with tenure, trust, and relationships that slowly blur the line between good judgment and personal interest.

    • The real exposure isn’t the fraud itself — The real damage from conflict of interest can be years of skewed vendor decisions, above-market pricing, and lost competitive ground.

    • Companies shouldn’t treat conflict of interest as a disclosure problem — Companies would do well to remember that often conflict of interest is really a data and systems problem.

His access logs were clean, so it took weeks to find out what actually happened. He had been borrowing colleagues’ IT logins, who had handed them over without much thought, even though they knew it broke policy. They just didn’t think it mattered. He used those logins to steer million-dollar contracts to selected vendors who were paying him kickbacks.

The company’s conflict of interest policy existed, and people had signed it. Yet, nobody checked whether anyone followed it. And this scheme wasn’t even caught internally. Fortunately, someone outside found it.

This gap between knowing something is wrong and believing it matters — that’s where conflict of interest lives.

The financial exposure goes well beyond the kickback itself

The kickback that was paid to an insider is not the real cost to the company. The real cost is what happens while nobody is looking. As a result of this fraud, this company didn’t even know they were experiencing years of sourcing decisions that were shaped by hidden interests, vendors who never got a fair shot, and pricing that stayed above market price because the person managing the relationship had a reason to keep it there.

Throughout many industries, the numbers back this up. The from the Association of Certified Fraud Examiners (ACFE) found corruption in almost half (48%) of all fraud cases. Median loss for corruption schemes was around $200,000, and the average scheme run for about 12 months before anyone catches on. Not surprisingly, 87% of conflict-of-interest fraud perpetrators had no prior criminal record. Indeed, they were trusted employees, not career criminals.

What makes this worse is that most organizations have no reliable way to catch it. Across industry guidance, compliance publications, and professional forums, a consistent picture emerges: The majority of organizations rely entirely on disclosure forms and self-reporting to manage conflicts of interest. Leading compliance expert, Rebecca Walker has publicly admitted that — and even though the tools exist, almost nobody is using them.

The statistics, however, only capture what gets caught. The psychology of how it starts is harder to measure — and more important to understand. Conflict of interest rarely begins with a plan to steal. Rather, it starts with tenure, trust, and relationships that make someone hard to replace. Over time, the line between good judgment and personal interest doesn’t get crossed, it just disappears.

Taking a more structured approach

Most companies rely on disclosure forms, ethics training, and a code of conduct. They want to tell people what a conflict looks like, ask them to report it, and assume they will. Too often, they won’t.

Disclosure forms ask employees to self-report behavior they often don’t recognize as problematic, and those who do recognize it worry they’ll be investigated or treated unfairly themselves. They’ve watched junior staff held to strict standards while senior leaders get a pass. Unfortunately, that teaches everyone the same lesson: Stay quiet. When 85% of companies with a code of conduct still have fraud at this scale, the problem is not what people know, rather it’s how the program is built.

These failures point to three specific gaps in how most organizations approach conflict of interest: i) how they gather information; ii) how they monitor risk; and iii) how they receive reports. A structured framework — one based on concepts of design, detect, and deploy — can address each one of these gaps directly, with each component being measurable in financial terms.

Design: Are you collecting facts or asking people to confess?

Take a look at how you approach employees around conflict-of-interest issues. Are you seeking information or just generally hoping the employee admits wrongdoing, even inadvertently. A better approach could be to ask specific questions: How long has the employee worked with this vendor? Can the employee award contracts to them? Does the employee have any ownership stake in a company on the approved vendor list?

Let the employee give the facts and then let the system make the call. When you separate sharing information from being judged for it, people actually share and you get better data. And better data means better procurement decisions. That is not a compliance win — that’s a business win.

Detect: Are you looking for conflicts or hoping someone speaks up?

Run your vendor list against your employee records and flag matching addresses, phone numbers, and bank accounts. Check public registries for shared directors between your staff and your suppliers. Look at who has been awarding contracts in the same role for years without rotating, and managers who keep hiring from former employers.

Any company with an ERP system and an HR database can run these checks quarterly. And ACFE data underscores the value in taking the proactive approach: On average, companies using automated transaction monitoring catch fraud within six months and lose about $83,000; and companies that wait for law enforcement to alert them to the fraud take 24 months and lose $675,000.

Deploy: Is your hotline a business tool or a poster on a wall?

Tips catch 43% of all fraud — more than audits, management reviews, and law enforcement combined. Companies with hotlines lose $100,000 in median fraud; but companies without them lose $200,000. A working tips hotline can cut your losses in half.

However, most hotlines are not functioning as intended. They exist on paper without the visibility, trust, or independence required to generate reliable reports. For example, a senior executive was steering contracts to his own associates. And even though a company hotline existed, the executive actually sat on the committee that received the reports. The tool was built to catch misconduct and was working properly, yet it was controlled by the person committing the fraud. The matter had to be escalated outside normal channels, and the senior executive was eventually fired for cause.

Almost half (46%) of employees who report misconduct face retaliation, according to the , from the nonprofit Ethics and Compliance Initiative. When that is the outcome, silence becomes the rational choice. If you want your hotline to work, promote it every quarter. Show people what was reported and what happened because of it. Make sure no single person can block or read a report before it reaches the right people. Being that proactive around your hotline will give employees proof that the system protects them.

Is it worth the investment?

Of course, the question is not whether your company has a conflict-of-interest policy, it most likely does. Rather, the question is whether you would know if someone were breaking it right now.

Companies that design better fact-gathering, detect through monitoring, and deploy trusted reporting can do more than catch fraud early. They can buy from better vendors, compete on fairer pricing, protect their board from liability, and build a culture in which raising a red flag is seen as protecting the business.

If the honest answer is that you would not know if someone was violating your company’s conflict of interest policy, then business case for being more proactive has already been made.

You can find more about how companies can best manage business fraud here

Key insights:

• • • Getting at the core legal question — In a case brought by defendant Ongkaruck Sripetch, the Supreme Court is deciding whether the SEC must prove investors suffered measurable financial loss before courts can order disgorgement, which would require fraudsters to give up illegal profits.

    • Why it’s high-stakes — Disgorgement is a major SEC enforcement tool — representing billions of dollars annually — so a new requirement to prove investor losses could sharply limit when and how much the SEC can recover.

    • How the justices seemed to lean (so far) — Questions at the argument before the Court suggested skepticism toward Sripetch’s position, with several justices asking why it would be an unfair penalty to take back ill-gotten gains and noting the practical difficulty of proving each investor’s exact loss.

If you’ve ever wondered how the U.S. Securities and Exchange Commission (SEC) actually gets money back after it catches a fraudster, one of its biggest tools, disgorgement, is now under the microscope. This week, the U.S. Supreme Court heard arguments in a case, Sripetch v. SEC, that sounds technical on paper but has at its core a simple question: When the SEC makes a fraudster give up illegal profits, does it have to prove that investors suffered measurable, out-of-pocket losses first?

The case centers on Ongkaruck Sripetch, who the SEC says pocketed illicit proceeds through a classic pump-and-dump scheme from 2013 to 2017. Pump-and-dumps often involve penny stocks in which a person will hype up the price of these thinly traded stocks, then sell into the price spike they caused and walk away richer. Other stock traders who bought into the hype are the ones left holding the bag.

Sripetch admitted violating securities law and, in his subsequent criminal case, was sentenced to 21 months in prison. Separately, in the SEC’s civil action, a federal court in California ordered Sripetch to repay more than $3 million in ill-gotten gains plus interest.

The Supreme Court case isn’t a serious argument against the SEC’s ability to seek disgorgement — numerous courts have recognized the remedy for years, and Congress has since written the SEC’s ability to pursue it into federal law. The core question in the case is narrower, yet crucial for the SEC’s mission. It asks whether the SEC must show that victims suffered pecuniary or economic harm before a court can order disgorgement. Federal appeals courts have split on that point, which is why the Supreme Court agreed to take the case.

What is disgorgement, exactly?

Think of disgorgement as a legal give it back order. If a person or company makes money by breaking the securities laws — say by manipulating prices, lying to investors, or running a Ponzi-style scheme — disgorgement is designed to strip the profits away from that wrongdoing and the wrongdoers. In theory, it’s not about punishing someone for being bad, rather it’s about making sure crime doesn’t pay.

In real markets, harm can be scattered across thousands of trades, mixed up with normal price swings, and hard to trace to one bad actor. Disgorgement, on the other hand, gives securities regulators a way to focus on the part that’s often the clearest: How much ill-gotten profit the fraudster made.

Indeed, that not a punishment framing is important because the SEC has other ways to punish those convicted of securities law violations — such as civil penalties, disbarment from serving as an officer or director, industry suspensions, and more. Disgorgement is supposed to be different — an action that aims at profits, not pain. The government’s position in the Sripetch case puts it bluntly: Disgorgement is meant to strip ill-gotten gains from wrongdoers, not to compensate victims for their losses.

And disgorgement is not a niche tool. The SEC regularly collects big sums of seized money through disgorgement. According to recent figures, the SEC obtained about $1.4 billion through disgorgement in fiscal 2025 (excluding certain amounts), and $6.1 billion the year before, which represented nearly three-quarters of its total financial penalties for that year.

Those numbers may help explain why this Supreme Court fight is being watched so closely: The outcome could either keep the SEC’s playbook intact or force it to do a lot more legwork before it can ask courts to order payback.

The arguments before the Court

Earlier this week, both sides argued before the Supreme Court as to the potential future use of disgorgement and what requirements the SEC might have to meet when requesting court to order it.

Sripetch’s argument — Lawyers for Sripetch told the Court that the SEC shouldn’t be able to get disgorgement unless it can show that investors actually suffered financial harm, such as a price drop caused by the fraud or some other measurable loss. If the SEC can’t prove that kind of harm, the lawyer argues, then making Sripetch pay money looks less like giving it back and more like an impermissible penalty that the SEC is not allowed to levy.

The government’s argument — Lawyers for the U.S. Justice Department, defending the SEC, said the proof-of-loss requirement makes no sense. Disgorgement, in their view, is about the defendant’s gains, not the victim’s losses. One government lawyer summed it up as a straightforward principle: Disgorgement is intended to ensure a defendant does not profit from their own wrongdoing.

At this week’s argument, the justices sounded (at least generally) more sympathetic to the government than to Sripetch. Justice Amy Coney Barrett pressed the defense on its basic logic: If the court is only taking away ill-gotten gains — money the wrongdoer was never entitled to — why is that a penalty at all? Justice Ketanji Brown Jackson made a similar point, suggesting disgorgement would only feel like punishment when someone is forced to pay money that was rightfully theirs.

When Sripetch’s lawyer suggested the SEC should have to identify and prove each victim’s dollar loss, Justice Sonia Sotomayor’s response was basically, Why would anyone bother? If the SEC has to run a mini-trial on every investor’s exact harm just to reclaim the fraudster’s profits, disgorgement would be unworkable in many cases.

The practicality of that point is a big deal in securities fraud. In real markets, harm can be scattered across thousands of trades, mixed up with normal price swings, and hard to trace to one bad actor. Disgorgement, on the other hand, gives securities regulators a way to focus on the part that’s often the clearest: How much ill-gotten profit the fraudster made. The idea is deterrence-by-math — if you can’t keep the profits, the incentive to run the scheme shrinks.

The Supreme Court’s ruling, when it comes, could re-shape how the SEC negotiates settlements, litigates fraud cases, and talks about remedies and punishments going forward.

Still, some justices raised broader concerns about how disgorgement gets used in the real world, such as whether certain applications start to look punitive, or whether they raise questions about a defendant’s right to a trial by jury. However, the Court also seemed interested in deciding only the question of the requirement to prove victims’ losses and leaving those bigger constitutional debates for another day.

Why this matters (even if you aren’t the SEC)

If the Supreme Court agrees with Sripetch and requires proof of investor pecuniary harm, the SEC could face a higher hurdle in cases in which misconduct is real, but losses are tough to quantify on a trade-by-trade basis. That could mean fewer disgorgement awards, smaller ones, or more pressure to rely on classic penalties instead.

If the Court backs the government, however, disgorgement stays what it has largely been — a fast, flexible way to reclaim profits from securities fraud and a core part of how the SEC tries to keep the securities markets honest.

Either way, the ruling will shape how the SEC negotiates settlements, litigates fraud cases, and talks about remedies and punishments going forward. With the Court expected to issue its decision by the end of June, securities lawyers and stock market mavens will be keeping an eye on this case.

You can find more about the challenges facing the SEC here