Key insights:

• • • UK law firms face a more selective growth market in 2026听鈥� 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 鈥� 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 expectation听鈥� 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鈥檚 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.

Jump to 鈫�

2026 State of the UK Legal Market

A new report from the 成人VR视频 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 鈥� and where it doesn鈥檛.

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鈥檙e 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鈥檙e 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 成人VR视频 Institute’s “2026 State of the UK Legal Market” by filling out the form below:

Key points:

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

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

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

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

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

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

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

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

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

When two jagged edges meet

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

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

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

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

The matchmaking discipline

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

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

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

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

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

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

听

Key insights:

• • • AI can improve officer safety听鈥� By helping them prepare for high-risk situations and make better decisions under pressure, advanced technology can enhance officer safety.

    • AI can increase operational efficiency听鈥� AI can reduce administrative burdens and improve efficiency overall, allowing officers to spend more time on police work.

    • Responsible implementation is essential听鈥� To ensure AI strengthens public trust while protecting civil liberties, proper guardrails and oversight need to be enacted.

Each year, during , we pause to honor the brave men and women in law enforcement who have made the ultimate sacrifice in service to their communities. As we pay tribute to those we have lost, we are reminded of the inherent dangers officers face every day.

In recognition of the importance of reflection and advancement, it is imperative that we examine the responsible application of emerging technologies, especially AI, to enhance officer safety, support their objectives, and reinforce overall public safety.

AI is already being integrated into public safety systems in meaningful, measurable ways. When guided by strong ethical principles, transparency, and commitment to community trust, AI can serve as a force multiplier and a protective partner for members of law enforcement. The goal is not to replace officers, of course, but to equip them with better tools, that allow them to reduce risk and return home safely after every shift.

Improving situational awareness and operational readiness

One of the most immediate benefits of AI in law enforcement is its ability to enhance situational awareness. When officers respond to a call, the first minutes on scene are often the most critical 鈥� and the most dangerous. AI can help reduce uncertainty by providing rapid access to relevant information.

For example, AI-powered systems can analyze incident data, criminal records, and community reports to give officers a clearer picture of what to expect when they arrive on scene. This includes identifying patterns of violence, recognizing repeat offenders, or flagging locations that may have a history of high-risk activity. Such insights allow for better preparation, smarter deployment, and more informed decision-making under pressure.

Additionally, AI can assist in public records and open-source searches, pulling critical data from comprehensive databases, the internet, and connected devices in seconds rather than hours. This immediate access to information enables faster, more effective responses. In short, AI can save valuable time when seconds count.

Streamlining administrative work to focus on the mission

Law enforcement officers spend a significant portion of their time on administrative duties, such as writing incident reports and managing court schedules and citations. These tasks, while necessary, take officers away from community engagement and proactive policing.

AI can help reduce this administrative burden by automating routine documentation. Natural language processing tools can draft reports based on officer input, ensuring consistency and freeing up time for frontline duties. Similarly, AI-driven scheduling systems can optimize shift assignments, account for court appearances, and manage on-call rotations. This AI-enabled administrative assistance goes a long way in ensuring that staffing levels are appropriate and that officers are not overburdened.

When guided by strong ethical principles, transparency, and commitment to community trust, AI can serve as a force multiplier and a protective partner for members of law enforcement.

By reducing the administrative load, AI allows officers to focus on what they do best 鈥� serving and protecting their communities. This not only improves job satisfaction among officers themselves but also increases operational efficiency and public safety outcomes.

Building guardrails for responsible AI use

As with any powerful advanced technology, the integration of AI into law enforcement must be guided by clear policies, oversight, and accountability. The goal is not to deploy AI indiscriminately, but rather to ensure its use enhances safety without compromising civil liberties or public trust.

This requires proactive collaboration between technologists, law enforcement agencies, policymakers, and the communities they serve. Standards must be developed for data privacy, algorithmic transparency, and bias mitigation. AI-enabled systems should undergo rigorous testing and independent review before deployment. Further, officers must be trained not only on how to use these tools, but also on the limitations and ethical implications of using these tools as well.

Finally, public trust is essential. Members of the community need to know that AI is being used to protect their safety and that of law enforcement 鈥� it is not a tool to surveil them without cause. Communicating transparently how the AI systems are designed, what data they use, and how decisions are made will be key to maintaining legitimacy and trust with the public.

A future of safer streets and stronger trust

The integration of AI into law enforcement is not about replacing human judgment 鈥� rather, it鈥檚 about augmenting officers鈥� judgment. When used responsibly, AI can reduce risk, improve preparedness, and support officers in carrying out their duties more safely and effectively.

In the years ahead, we can expect to see broader adoption of drone first responders, real-time language translation tools, and predictive systems that further help enhance officer and community safety measures. However, technology alone is not the answer. Success will depend on how thoughtfully these tools are implemented, how well citizens鈥� rights are safeguarded, and how deeply communities are involved in the process.

This week, as we honor those officers who have fallen in the line of duty, let us also commit to doing everything we can to protect those who serve today. AI, when applied with care, can be a powerful ally in their mission, keeping officers safe, allowing them to make better decisions, and together, building stronger, safer communities for all.

The data provided to you may not be used as a factor in establishing a consumer鈥檚 eligibility for credit, insurance, employment, or for any other purpose authorized under the Fair Credit Reporting Act.

You can find more on the challenges facing law enforcement here

Key insights:

• • • Real-time tax compliance has restructured the tax function 鈥� 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 鈥� 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 鈥� 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 鈥� The听 sold out. A guest list that included tax directors from Amazon, Walmart, and Procter & Gamble, OpenAI’s tax department, the Big Four, 成人VR视频 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鈥檚 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 鈥� issue an invoice, file a return weeks later, audit when the auditor gets around to it 鈥� 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鈥檚 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 成人VR视频 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鈥檝e 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 鈥� 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鈥檚 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鈥檛 just a problem for this particular industry 鈥� tax just happened to get here first. However, it鈥檚 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鈥檚 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 鈥� clearly, a new capability is also a new problem.

The second way to lose is harder to talk about. One panelist described, to attendees鈥� general dismay, hearing of companies that have given up on compliance entirely 鈥� instead, they pad their numbers with a safety margin and treat the eventual audit as the cheaper of the two costs. The panel recoiled 鈥� 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鈥檚 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 鈥� and the room is dead set on playing to win.

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

• • • The reactive phase is partly a mindset problem 鈥� 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 鈥� Two-thirds of tax professionals say their department鈥檚 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 鈥� 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 鈥� 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鈥檛 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鈥檚 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鈥檚 value to the C-Suite.

Given this, it鈥檚 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 成人VR视频 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 鈥� 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 鈥� 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 鈥� 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: “鈥ur company as a whole is making significant changes, but the tax department is typically an afterthought in those decisions.”

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

There鈥檚 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鈥檚 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 鈥� not time savings, not error reduction, not ROI. Nothing.

While 67% of tax professionals say their department鈥檚 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鈥檚 value to the C-Suite.

That is cost-center thinking in action 鈥� the belief that it鈥檚 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 鈥� 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 鈥� 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 鈥� 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鈥�2 years, from 3鈥�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 鈥� 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 成人VR视频 Institute and Tax Executives Institute, here

Key highlights:

• • • Technology satisfaction is in freefall and needs human-side investments 鈥� 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 鈥� 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 鈥� 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 成人VR视频 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 鈥� 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 鈥� along with mentoring and peer knowledge-sharing 鈥� a performance requirement.

2. Hire for the future 鈥� The pendulum swing back to tax expertise is understandable, but it鈥檚 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 鈥� 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 鈥� 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.鈥�

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鈥� 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 成人VR视频 Institute and Tax Executives Institute here

Key highlights:

• • • Universities face pressure over pedagogy鈥� Academic institutions are adopting AI as a reputational marker that鈥檚 driven by market pressure rather than educational need, creating a risk for students who can work with AI but not independently of it.

    • Entry-level roles under threat鈥� AI is being deployed most heavily to automate the grunt work of entry-level positions in which foundational professional skills are traditionally built through struggle and feedback.

    • K-shaped cognitive economy emerging鈥� Experienced professionals with existing expertise are gaining efficiency from AI, while entry-level workers are losing access to skill-building experiences.

According to Harvard University’s Professional & Executive Development division, innovation is defined as a 鈥減rocess that guides businesses through developing products or services that deliver value to customers in new and novel ways.鈥� Along this journey, professional judgement in decision-making is used numerous times to determine next steps at key stages.

Notably, the word technology is nowhere to be found in this definition 鈥� an absence , Assistant Professor of Learning Technologies at the University of Minnesota, has long found revealing. Instead, innovation is framed as creative problem-solving, contextual intelligence, and the ability to work across perspectives. Interestingly, Dr. Heinsfeld adds, none of these require constant automation. In fact, many of them are undermined by it.

However, AI adoption has the real potential to automate away the very experiences that build these capabilities from university lecture halls to corporate offices. With notable data already suggesting that , the risk that the current approaches to AI use in universities and companies are engineering away innovation and professional judgement skills is real, notes , Group Leader in AI Research at Harvard and NTT Research.

Indeed, some observers view AI as the largest unregulated cognitive engineering experiment in human history. Yet, unlike medical drugs that require years of approval and testing, AI systems are reshaping how millions of students think, learn, and make decisions without a comparable approval process or a shared framework for discussing any potential 鈥渟ide effects,鈥� as Dr. Heinsfeld pointed out.

Most worrisome is that AI is being deployed most heavily to automate precisely the entry-level roles where foundational professional skills are built.

So, what happens when an entire generation of future employees learn to delegate judgment before they develop it? And what actions do universities and companies need to take now to avoid this reality?

Risks of universities adopting AI under pressure

For universities, AI 鈥渉as become a reputational marker, and not adopting AI is framed as institutional risk, regardless of whether an educational case has been made or not,鈥� says Dr. Heinsfeld, adding that this is being driven, in part, by market pressure rather than pedagogical need.

Already, companies can greatly influence universities as employers of new graduates; and as such, AI systems are currently being optimized for speed, agreeability, and accessibility to stimulate ongoing use. However, as Dr. Heinsfeld contends, as universities race to earn the label AI ready without a careful, cautious and detailed understanding of how it may impact students鈥� cognitive processes, they run the risk of damage to their reputations of pedagogical integrity.

In addition, the “data as truth” paradigm is a complicating factor, she says. Drawing on her research, Dr. Heinsfeld explains how data 鈥渋s often framed as the idea of being a single source of truth based on the assumption that when collected and analyzed, it can reveal objective, indisputable facts about the world.鈥� Indeed, this ubiquitous mindset across universities and corporations treats data 鈥� such as that used to train large and small language models 鈥� as objective and indisputable.

Yet this obscures critical decisions about what gets measured, whose perspectives are included, and what forms of knowledge are systematically excluded from AI systems. As Dr. Heinsfeld warns, when data becomes synonymous with truth, “knowledge is what is measurable and optimizable.鈥� This narrows professional judgment to efficiency metrics rather than the interpretive depth, ethical reasoning, and cultural context that are essential for sound decision-making.

Judgment gap widens in workforce downstream

Under the current AI adoption approach, students could leave universities able to work听with听AI but not independently听of听it, a distinction emphasized by Dr. Heinsfeld. Like calculators, AI works as a tool only when foundational skills for its use exist first. Without this, graduates enter the workforce with a critical judgment gap that compounds from their lives as students at college campuses to becoming employees working in corporations.

AI adoption has the real potential to automate away the very experiences that build these capabilities from university lecture halls to corporate offices.

Most worrisome is that AI is being deployed most heavily to automate precisely the entry-level roles where foundational professional skills are built, warns Dr. Tanaka. Indeed, this is exactly the type of grunt work that teaches judgment through struggle and feedback. Over time, overuse of AI will result in quality being sacrificed because critical evaluation skills have atrophied.

Looking into the future, Dr. Tanaka foresees a K-shaped economy of cognitive capacity. Experienced professionals with existing expertise and contextual judgment built through years of experience will gain increasing efficiency from AI. Entry-level workers, however, will lose access to the valuable experiences that build professional judgement. This gap widens between professionals who can independently accelerate their workflows using AI and those whose traditional tasks are merely displaced by it.

Intervention may be able to break the cycle

The pattern is not inevitable, as both Dr. Tanaka and Dr. Heinsfeld explain. Drawing on Dr. Heinsfeld鈥檚 emphasis on institutional agency, meaningful intervention will depend on conscious, intentional choices made at every level. Both experts share their guidance for how different organizations can manage this:

Academic institutions 鈥� Universities must first recognize that AI adoption is a decision rather than an inevitability and make educational need the North Star for decision-making around AI. In her analysis, Dr. Heinsfeld emphasizes that when vendors set defaults, they quietly redefine academic practice. Defaults shape what is made visible or invisible and what becomes normalized. In AI-driven environments, universities often lose control over how models are trained and updated, what data shapes outputs, how knowledge is filtered and ranked, and how student and faculty data circulate beyond institutional boundaries 鈥� especially if decision-making is left to vendors. As a result, the intellectual byproducts of teaching and learning increasingly become inputs into external systems that universities do not govern.

Private entities 鈥� For organizations, Dr. Tanaka calls for feedback loops and other mechanisms that will promote more open discussion about AI use without stigma. In addition, companies need to proactively redesign entry-level roles听to ensure these positions continue to cultivate judgment and foundational skills in an AI-driven environment. Likewise, Dr. Tanaka suggests that companies explicitly provide feedback about cognitive trade-offs to employees, fostering an understanding of possible skill entrophy.

Employees 鈥� Similarly, individuals working for organizations bear much of the responsibility for making sure critical thinking is enhanced by AI. Indeed, strategic decisions about when to use AI while seeking to preserve cognitive capacity and professional judgement are key.

Looking ahead

In today鈥檚 increasingly AI-driven environment, a new paradigm is needed to combat the current operating assumption that optimization from AI is the sole path to progress. And because the current trajectory sacrifices human development for efficiency, the need for universities and companies to choose a different path is urgent 鈥� while they still have the judgment capacity to do so.

You can find out more about how organizations are managing their talent and training issues here

Key takeaways:

• • • Tax departments lack resources and confidence 鈥� More than half (58%) of tax departments are under-resourced, and 59% are not confident that they can upgrade their tax technology over the next two years.

    • Under-resourced departments incur more penalties 鈥� At least half of respondents from under-resourced tax departments say their departments incurred penalties over the past year, compared to only about one-third of those from properly resourced departments.

    • Making the shift to proactive planning and value creation 鈥� For many tax departments, the winning model blends in-house expertise, targeted external support, and a coherent tech/AI stack that allows teams to shift from tactical compliance to proactive planning and strategic value creation.

Under-resourced corporate tax departments spend more of their budget on external support compared to well-resourced teams 鈥� yet they’re more likely to incur penalties and less confident in forecasting, according to the 成人VR视频 Institute鈥檚 .

Given this, the problem isn’t a lack of spending 鈥� it’s the operating model. With respondents from 58% of tax departments saying they are under-resourced, 59% saying they lack the confidence needed to upgrade their existing tax technology over the next two years, and most spending more than half their time on reactive compliance work when they’d prefer to focus on strategic planning, clearly the gap between ambition and reality has never been wider.

The answer isn’t working harder or throwing more money at consultants, however. It’s building a hybrid ecosystem of people, platforms, and partners designed to shift capacity from firefighting to foresight.

The Groundhog Day problem

Every year feels the same: New tax legislation (such as the One Big Beautiful Bill Act or Pillar 2), new compliance burdens, new geopolitical uncertainty 鈥� coupled with the same old constraints. Too much work, not enough time, and technology that lags.

When deadlines hit, under-resourced teams rely on two blunt levers: overtime and reactive outsourcing. Internal staff end up working longer hours, and external providers plug the gaps at short notice. This model is breaking departments and it鈥檚 breaking down itself.

Under-resourced departments are significantly more likely to incur penalties, with 50% of respondents saying their under-resourced department had been penalized in the past year, compared to just 34% of respondents from well-resourced departments that say that, according to the report.

Further, under-resourced department respondents said they were less confident in their ability to forecast accurately, with just 26% saying their ability to forecast accurately was “very likely” compared to 43% of well-resourced department respondents. Ironically, under-resourced departments also spend more on external support as a percentage of budget (44%) compared to 37% for well-resourced departments. Clearly, spending more doesn’t solve structural problems 鈥� it often masks them.

Meanwhile, tax professionals report spending more than half their time on tactical or reactive work, even though they would prefer to spend up to two-thirds of their time on strategic analysis. Not surprisingly, when the team is locked into manual reconciliations and last-minute fixes, it’s nearly impossible to influence business decisions or shape strategy.

Why 鈥渁ll in-house鈥� or “all outsourced” no longer works

When more work is moved onto the plates of the internal tax team, all in-house can often come to mean all heroics 鈥� talented people drowning in compliance volume with no time to use the analytical tools already on their desks. Conversely, all outsourced risks hollowing out the department鈥檚 institutional knowledge and weakening its seat at the table.

A hybrid model asks better questions: What kind of work is this, and where does it create the most leverage? These questions can be used to determine where and to whom work should go. For example, high-volume, rule-based, recurring tasks are prime candidates for automation, shared services, or managed services under strong tax oversight; while complex, judgment-heavy, strategically sensitive work should remain anchored in-house, with external advisors extending capacity and offering specialized insight.

Thus, the best model for a modern corporate tax department is a hybrid ecosystem 鈥� not a fixed organizations chart, but a deliberate blend of internal expertise, enabling technology, and external capability partners.

Four layers of the hybrid ecosystem

This hybrid ecosystem can be delineated into four layers, each bringing their own insight and value:

1. 1. 1. People and roles redesigned 鈥� High-performing tax functions invest in analyst and tax-tech roles that connect tax to enterprise resource planning (ERP) systems, data hubs, and analytics, thus freeing technical experts from manual data work. Senior professionals then become embedded advisors to finance, treasury, and the business, not just compliance reviewers.
      2. Processes segmented into “run” and “change” 鈥� The biggest barriers to strategic work are excessive volume, heavy compliance burdens, limited resources, and time pressure. Modern tax departments respond by explicitly segmenting work in which run the business processes are documented, standardized, and increasingly automated or pushed into shared or managed service models. Change the business work remains tightly linked to senior tax staff.
      3. Technology becomes the data spine 鈥� More than half of respondents say they expect above-normal increases in their tax technology budgets, and more than half say their main resourcing strategy is introducing more automation. The goal isn’t collecting point solutions; rather, it’s building a coherent data spine that includes ERP integration, tax-specific data models, consistent workflow tooling, and strategic platforms that flex as regulations shift.
      4. AI act as an accelerator 鈥� Two-thirds of tax departments aren’t yet using generative AI (GenAI), according to the report. And among the one-third that are, usage clusters around research, document summarization, drafting, and some analytical support. The next step up the AI chain is for departments to move from individual experiments to standardized, governed workflows that scan legislation, prepare first drafts of memos, or interrogate large data sets for anomalies.

What high-performing hybrid tax departments do next

Departments that feel well-resourced, allocate more time for their professionals to conduct proactive work, and invest deliberately in technology and skills are significantly more confident in their ability to forecast accurately, avoid penalties, and minimize tax liabilities, the report shows.

Indeed, these high-performing hybrid tax departments:

• • • invest ahead of crises in people, tech, and processes
    • treat external providers as capability partners, not emergency relief
    • actively protect time for strategic work by automating or outsourcing routine tasks
    • insist on a durable seat at the strategy table, not just one for compliance reporting
    • experiment with automation and AI in focused, repeatable use cases

It is worth noting that smaller companies (those under $50 million in annual revenue) and the largest one (those with more than $5 billion in revenue) are leading the way by securing leadership buy-in early and leveraging specialized external expertise rather than trying to build everything in-house. Midsize companies, by contrast, are more likely to rely on in-house teams to lead automation efforts and less likely to use third-party vendors 鈥� a cautious approach that risks having them fall too far behind to catch up.

The message: Design the ecosystem, don’t just work harder

For corporate tax professionals, the message may be harsh but hopeful: You cannot work your way out of structural constraints by effort alone. Rather, a well-designed hybrid ecosystem can turn those constraints into a catalyst that will allow the department to deliver more value to the business. In fact, the modern corporate tax department is hybrid by necessity; but the question is whether it’s hybrid by design 鈥� or just by accident.

You can learn more about the challenges facing modern corporate tax departments here

Key takeaways:

• • • An emerging employment tribunal crisis听鈥� The UK’s employment tribunal system is facing unprecedented backlogs, long wait times, and unaffordable legal representation, leaving many workers and small businesses unable to effectively resolve workplace disputes.

    • Process-oriented barriers to justice听鈥� Most claims are dismissed not because they lack merit, but due to claimants disengaging from a slow and complex process, with legal costs often exceeding the value of claims and legal aid unable to meet rising demand.

    • A potential role for legal technology听鈥� Mission-driven legal tech platforms are emerging to provide affordable, scalable support and help claimants stay engaged by offering a practical solution to improve access to justice.

When a worker in the United Kingdom is unfairly dismissed or denied wages, their path to resolution runs through employment tribunals, a specialized court system separate from civil courts. As in the United States, many workers and small businesses cannot afford legal representation and must navigate the process on their own.

With backlogs at all-time highs and affordable legal services at all-time lows, this system is coming under increasing pressure. Fortunately, mission-driven technology and data analysis are emerging to level the playing field and increase access to justice.

Current state by the numbers

According to an analysis of the and other data sources,*听in the second quarter of 2025, employment tribunals resolved just 45% of incoming claims, adding 18,000 cases to the backlog alone. In the past year, the open caseload has surged by 244%. This pressure is set to intensify as the inbound Employment Rights Act 2025 鈥� the UK’s most significant overhaul of workplace protections in decades 鈥� is set to extend protection to six million more workers in 2027.

As the backlog increases, so do wait times. In 2025, the average wait for resolution reached 25 weeks, more than double that of 2024, with some claim types like equal pay and discrimination claims reaching up to 37 weeks. Some more complex cases are reported to have their final hearings scheduled as far out as 2029.

With only 8% of cases reaching a final hearing and the majority resolved through settlement or withdrawal, the growing backlog raises concerns about whether lengthy wait times influence how claimants choose to resolve their cases.

In the UK, a common threshold for legal affordability is a salary of 拢55,000, meaning around 65% of workers cannot afford legal representation. Legal aid and pro-bono services exist to support those in need, but with growing funding constraints and rising demand, these services cannot reach nearly two-thirds of claimants.

You can find more insights about how courts are managing the impact of advanced technology from听our Scaling Justice series听here

Tribunal awards are largely calculated from salary. This can result in a claim’s value often being lower than the cost of legal representation to pursue it. In a typical hospitality case, for example, a worker owed 拢1,500 in unpaid wages (equivalent to 3陆 weeks of pay) has a 92% chance of representing themselves and will wait on average six months for resolution 鈥� without pay owed, legal support, or outcome certainty.

The cost, both in time and resources, also falls on employers. In lower-margin industries such as hospitality, default judgments, in which an employer does not engage with proceedings, can reach as high as 37%, compared with a national average of around 6%. For these employers and for smaller businesses more broadly, the cost of legal support may also exceed the value of defending a claim.

With rising costs and growing delays, the risk for both employers and employees is that the system becomes inaccessible, leading to outcomes shaped by who can afford to sustain the process rather than case-by-case strength.

Where justice tech fits

The conventional assumption is that self-represented claimants are at a significant disadvantage when they go to court; yet the data is more nuanced. Self-represented claimants who reach a hearing prevail 44% of the time, compared to 52% for those with legal representation 鈥� a gap of less than eight percentage points.

The greater risk is not losing at hearing but never actually reaching one. Analysis of more than 2,700 struck-out, or dismissed, cases by employment rights platform Yerty found that the majority were dismissed not for lack of merit, but because claimants stopped engaging with the process. Only 6% were struck out for having no reasonable prospect of success. This suggests that the primary barrier may not be the absence of legal representation, but the ability to sustain engagement with a slow, complex, and often opaque process.

Increasing numbers of UK workers turning to AI tools like ChatGPT for legal support highlight not only the demand for affordable access but also the risks of general-purpose tools being used in legal contexts. Fabricated case law in tribunal submissions, for example, harms users and adds further pressure to an already overstretched system.

The conventional assumption is that self-represented claimants are at a significant disadvantage when they go to court; yet the data is more nuanced.

A new generation of legal technology platforms is emerging to fill this gap, with tools purpose-built for the specific circumstances of employment law. Yerty and Valla, among others, offer AI-powered guidance tailored to the UK tribunal process, providing affordable, scalable support previously out of reach for most workers. Government organizations are also moving in this direction. For example, in its recent five-year strategy outlook committed to exploring new digital services that offer faster, more accessible support.

Technology alone cannot address underfunding, judicial capacity, or fundamental power imbalances. However, if the majority of dismissed claims stem from disengagement rather than weak cases, and self-represented claimants prevail at comparable rates to those with lawyers, then the answer isn’t more lawyers 鈥� it’s better support upstream. Mission-driven legal technology can provide consistent, scalable guidance that helps both parties manage the process and avoid falling through the cracks.

The UK government’s own assessment of the Employment Rights Bill forecasts a 15% increase in claims by 2027 due to expanded eligibility. As noted above, the system is already under significant pressure before these reforms take effect, and traditional responses 鈥� more judges, more funding 鈥� too often take years to deliver.

While not a complete answer, justice tech can help address a real, measurable problem, that of keeping people engaged in a process that too often disengages them. For a hospitality worker owed back pay, a healthcare worker facing unfair dismissal, or a retail employee navigating a discrimination claim alone, that support could mean the difference between a case heard and one abandoned 鈥� and justice delayed or justice denied.

*Sources: Ministry of Justice Tribunal Statistics Quarterly (July-September 2025); Yerty analysis of 2,721 struck-out tribunal decisions and 8,761 case outcomes; ACAS Strategy 2025-2030; 2024 UK Judicial Attitude Survey, UCL Judicial Institute / UK Judiciary, February 2025.

Key takeaways:

• • • The real bottleneck for AI is the data core 鈥� AI is advancing rapidly, but most organizations’ data architectures, governance, and legacy assumptions can’t keep up. Without a repeatable, business-aligned data foundation, AI initiatives will struggle to scale and deliver reliable results.

    • AI success relies on explainable, traceable, and reusable data 鈥� For AI to be reliable and compliant, organizations must design data environments that emphasize lineage, semantics, and trust; and that means that compliance and auditability need to be built into the data core, not added on later.

    • Business should shift from tool-centric upgrades to business-driven, data-centric reinvention 鈥� Efforts focused only on modernizing tools or platforms miss the root issue: legacy data structures. Leaders must prioritize building a cohesive, reusable data core that aligns with business strategy.

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

Across boardrooms, regulatory briefings, and strategic off-sites, leaders are asking with growing urgency some variation of the same question: How do we make AI reliable, scalable, auditable, and economically defensible? The surprising answer is not in the AI technology, nor in the cloud stack, nor in another round of system upgrades.

It is in the data. Not the data we store, not the data we report, and not the data we move across our pipelines. It is in the data that we must now explain, contextualize, trace, validate, and reuse continuously as agentic AI becomes embedded in every workflow, every decision system, and every regulatory outcome.

The stark reality across industries then becomes what to do as AI matures faster than our data cores can support. For the first time, technology is not the bottleneck 鈥� architecture is, organizational assumptions are, and governance strategies are. More importantly, the lack of a repeatable, business-aligned data foundry has become the strategic inhibitor standing between today鈥檚 operations and tomorrow鈥檚 autonomy-ready enterprises.

The realities of 2026

As 2026 gets underway, the pressures of regulation, AI adoption, data lineage requirements, and cross-system consistency have converged into a single strategic reality: We can鈥檛 keep modernizing data at the edges. The data core itself must be reimaged and compartmentalized.

For leaders across highly regulated industries, the challenge is recognizing that our data architectures were never designed for the world we鈥檙e moving into. Historically, solutions were built for predictable siloed-data systems, linear programmatic processes, and dashboard reporting. Today鈥檚 demands are continuous, variable, cross-domain, and machine-interpreted and not bound by traditional methods and techniques of process efficiency and system adaptability. Tomorrow鈥檚 systems will be comprehensively trained by data. To properly frame these realities, leaders must understand:

• • • Agentic AI exposes weak data architecture immediately 鈥� Models may scale, but data debt does not. This is a new, priority constraint.
    • Lineage, semantics, and trust scoring 鈥� not models 鈥� will determine enterprise readiness 鈥� AI will only be as reliable as the meaning and traceability of enterprise data.
    • Compliance cannot be retrofitted; rather, it must be designed into the data core 鈥� Compliance no longer ends in reporting, it must exist upstream and be addressed continuously.
    • Return on investment in AI is impossible without composable, modular, and reusable data products 鈥� Data that cannot be composed, traced, and made consistent cannot be automated.
    • The bottleneck is not talent or tools, it is the absence of a data foundry 鈥� Without robust, industrial-grade data production, AI will remain fragmented and experimental.

By delivering a practical, business-first path integrated with a data-centric design, organizations enable reuse, compliance, and measurable ROI. AI is accelerating, but data readiness is not. This mismatch is where many transformation efforts die.

Agentic AI demands a data environment that simply does not exist with most legacy solutions. It requires decision-aligned semantics, federated trust scoring, cross-domain lineage, dynamic compliance overlays, and consistent interpretability. No model, no matter how advanced, can compensate for data environments that have been engineered for static reporting and linear process logic. We are entering a cycle of reinvention in which data becomes the organizing principle.

The business need, not the engineering myth

Executives are rightfully fatigued by transformation programs. They have seen modernization initiatives expand scope, escalate cost, and ultimately underdeliver. They have heard the promises of clean data, enterprise data platforms, microservices, cloud migration, and AI-readiness. However, when agentic AI begins interacting with these ecosystems, the fragility of the entire operation becomes instantly visible.

Why? Because most data modernization initiatives have been driven by tool-centric solutions rather than architecture-centric capabilities. Prior data governance is about oversight, not enablement and reuse, as is being demanded by emerging AI designs. Often, legacy methods kept their audit and lineage contained within siloed processes, bridging bridged them with replicated data warehouses, extract, transform, load systems (ETLs), and application programming interfaces (API) protocols.

However, this tool-centric, legacy-enabled approach is the problem. We keep optimizing the wrong layers, and we keep modernizing the components.

As a result, we too often see that AI pilots succeed, but enterprise scaling fails. Or, that regulatory reporting improves marginally, but compliance costs increase. Or M&A integrations appear straightforward, but post-close data convergence drags on for years.

The gap between ambition and reality

As a solution, a data foundry approach corrects that imbalance by formalizing the factory-grade patterns required to support agentic AI systems. It becomes the production line for reusable data products, compliant semantics, and decision-aligned datasets. It also eliminates reinvention by institutionalizing repeatable structures; and, most importantly, it restores business leadership over AI outcomes, rather than relegating decision logic to engineering workstreams and emerging technologies.

As illustrated below, AI requirements and realities need to be tempered with business demands, organizational risks, and data agility capabilities (including skill sets) to achieve realistic roadmaps of action 鈥� not strategic aspirations.

Today, the question isn鈥檛 whether organizations understand the importance of data, it鈥檚 whether leaders know how to build environments in which data becomes reusable, trustworthy, and ready for agentic AI. The issue, however, continues to be that our data cores 鈥� the architectural, operational, and standards ecosystems beneath all this 鈥� were not designed for continuous change.

Before they mobilize and execute against AI plans, business leaders need to answer the question: What business decisions are we trying to improve 鈥� and what data do these decisions actually requires today, and for tomorrow?

The organizations that will lead in the coming decade will do so not because they found the perfect technology stack, but because they built a reusable, continuously improving data foundation that can support AI, regulation, risk, and innovation simultaneously.

The question for leaders then becomes: Are we prepared to reinvent?

The work begins now 鈥� quietly, deliberately across the data core where tomorrow鈥檚 competitive advantages will be created. The chart below illustrates the business-driven AI elements that must be addressed, and how the old sequence of system provisioning must be replaced, beginning with outcomes and ending with engineered AI tools.

AI is an output 鈥� a capability that鈥檚 unlocked after the underlying data foundation becomes coherent, traceable, explainable, and aligned with business decisions. For leaders, the data core is no longer a back-office concern or one-off IT initiative. It is a strategic asset that can shape speed, resilience, and trust across the organization.

In the next post in this series, the author will explain how to architect an integrated data core, particularly through the AXTent architectural framework for regulated organizations. You can find more blog posts听by this author听here