Key insights:

• • • AI is now a talent and client expectation, not a differentiator 鈥� A large majority of tax & audit professionals regularly use AI, and many employees and clients now expect their firms to have strong AI capabilities.

    • Firms need to choose a deliberate AI strategy 鈥� There are three primary paths for AI adoption, and no matter which a firm chooses, the key message is that firms should actively define their AI direction rather than delay decisions.

    • Successful AI adoption requires governance and people-focused leadership 鈥� Beyond implementing technology, tax & audit firm leaders must establish AI governance, clearly communicate strategy, and align their AI with employee needs.

As AI adoption within the tax & audit profession accelerates 鈥� 81% of professionals say they are now using AI tools regularly 鈥� firm leaders are experiencing unprecedented pressure from talent, clients, and their firm鈥檚 own financial performance, according to the recent 成人VR视频听2026 Future of Professionals Report.

For example, retaining and recruiting top tax talent remains a critical concern in the profession, and AI has just ratcheted up the pressure even more. More than one-quarter of professionals say they would not accept a job at a firm lacking professional-grade AI, and almost 1-in-3 say they would consider leaving if their expectations for AI are not met within the next two years.

To help tax & audit firm leaders better navigate this fraught environment, 成人VR视频 has published a new action paper, Future of Professionals Report 2026: Actionable insights for tax & audit leaders, that provides practical guidance for navigating talent shortages, rising client expectations, and financial pressures, all within the context of the rapidly evolving technological environment.

You can download your copy of the听2026 Future of Professionals Report听here

Many tax & audit professionals surveyed say client expectations are rising, with AI-enabled quality becoming an important criterion for retaining outside tax & audit firms. At the same time, nearly half of respondents say they feel pressure to generate financial gains from AI, while one-third say their firms have yet to adapt commercial models accordingly. If left unaddressed, these pressures can compound, the paper points out, ultimately threatening a firm’s ability to attract and retain both clients and talent.

Finding your strategic path for AI adoption

Fortunately for those tax & audit professionals who feel overwhelmed by the strictures of advanced technology, the paper identifies three primary strategic paths for AI integration that could fit your firm, including:

• • • Using AI to elevate by leveraging AI to handle routine tasks, freeing professionals to focus on complex, high-value advisory work. Firms adopting this path aim to deepen client relationships and command premium fees that are based on expertise rather than volume.
    • Using AI to scale by focusing on productivity and using AI to increase capacity and consistency without increasing headcount. This path is particularly attractive for managing busy tax seasons and reducing recruitment strain.
    • Using AI to reimagine by rethinking the firm鈥檚 entire business model. Instead of periodic compliance, firms provide clients with continuous, proactive support and real-time insights, shifting from a service provider to a strategic partner.

A minority of respondents say their firms are deferring strategic decisions on AI, but the paper warns that any delay carries significant risks, especially as clients and talent expectations increase.

Universal priorities for firm leaders

Regardless of their chosen path, however, the paper outlines four priorities that every firm leader needs to address in order to succeed, including:

1. 1. 1. Govern the tools being used 鈥� More than one-third of professionals admit to using unauthorized AI tools, which greatly increases firms鈥� liability risks. Establishing clear governance, approving secure tools, and providing usage guidance are essential to mitigate these risks.
      2. Clarify the firm鈥檚 strategic direction 鈥� Firms must articulate their AI ambitions, internally and to clients, even if the path is not yet finalized. Understanding whether the goal is efficiency, expertise, or transformation can help guide decisions on tools, pricing, and hiring.
      3. Align AI with your professionals鈥� needs 鈥� Nearly half of professionals say they value work fulfillment as the primary benefit of AI, and a significant portion say they would consider leaving if their expectations go unmet. Engaging with teams to ensure AI deployment aligns with what they want is critical, whether they want more time, more complex work, or both.
      4. Define the role of early-career professionals 鈥� As AI automates more tasks, tax & audit firms must ensure that junior staff still receive the structured development needed to build professional judgment. Ensuring supervision before automation erodes these opportunities is vital for talent success.

As the paper clearly outlines, those tax & audit firm leaders that govern AI effectively, articulate a clear strategy, and invest in their people will be the ones best positioned to succeed in an increasingly AI-driven market.

You can download a full copy of the听Future of Professionals Report 2026: Actionable insights for tax & audit firm leaders听paper here

Key highlights:

• • The AI strategy-execution gap is an organizational problem, not a tech one 鈥� Among professionals whose firm or department has a stated AI strategy, more than half say that either the strategy is not visible on a daily basis or that the organization has no strategic AI direction at all.

  • The human cost of inaction is building faster than most leaders recognize 鈥� More than 90% of professionals say they are experiencing some degree of this AI-value disconnect, and among them, one quarter is considering leaving their current organization within two years if things don鈥檛 change 鈥� at an estimated replacement cost of $232,000 per employee.

  • Disrupted development compounds talent risk for the next generation 鈥� The flight risk of experienced professionals creates a compounding multiplier effect on the ability of entry-level talent to develop critical skills.

The greatest barrier to AI transformation in professional services is the widening human gap between what organizations promise their people about AI and the spoken and unspoken messages that professionals see and observe in their workplace every day, according to the 成人VR视频 recent , which surveyed more than 1,800 professionals in 62 countries across areas of law, tax, audit, accounting, compliance, risk, and global trade.

Establishing a visible AI strategy

While our research showed that most organizations have a stated AI strategy, the data suggests that it is not translating evenly to employees in their day-to-day work. Among professionals whose firm or department has a formal AI strategy, 35% say that strategy is not visible in their day-to-day experience, and another 17% say their organization has no strategic direction on AI at all. That means that more than half of professionals with fiduciary commitments are working in an environment in which the AI strategy on paper does not match the reality of how their work gets done.

The reasons that professionals say AI strategies are stalling suggest an absence in AI transformation and change agility across the organization. Indeed, professionals say that drivers of their organizations’ struggles to translate AI ambition into measurable results include the fact that the right tools are not yet in place (with 47% of respondents saying this), people are not equipped or trained to work in the intended way (43%), the strategy has not been translated into clear operational priorities (32%), and there is no shared understanding of the AI strategy across the organization (30%).

When strategic clarity around AI exists, it translates into more visible value. In fact, more than two-thirds of professionals in firms and departments with a stated strategy say AI is meeting or exceeding expectations for creating value at work. When there is no understandable AI strategy, less than one-quarter say this.

The quiet accumulation of human costs

The talent consequences of the gap between stated AI strategy and its daily execution are building faster than most leaders recognize. More than 90% of professionals say they are experiencing this gap to some degree. Among them, 1-in-4 is considering leaving their current organization within the next two years if things don鈥檛 change. This can result in an estimated replacement cost of $232,000 per employee, which means the quantifiable financial outlay of this talent flight can add up quickly.

The greatest vulnerability of flight risk sits with mid-career professionals, who often are the most embedded AI users, the most influential in day-to-day operations, and the most impatient with slow adoption. Almost 30% of these professionals would change jobs within two years if AI fails to deliver the value they expect, and 14% say they are considering leaving within the next 12 months.

Of course, the financial risks extend beyond mid-career professionals and could in fact disrupt a generation of early career talent. When experienced professionals leave, they take with them the mentorship and oversight upon which the development of early-career employees depends.

The report shows that 71% of professionals say they believe early-career roles need structured support from experienced peers to develop the skills that are at risk of being displaced by AI. Moreover, nearly half say they are concerned about AI鈥檚 impact on the development of independent judgment and learning through experience. In fact, legal professionals specifically say they expect the timeline for early-career lawyers to develop a level of trusted judgment could be stretched by nearly two years.

Taken together, these factors create a compounding multiplier effect that will almost certainly have a negative impact on organizational performance in the near future, the report suggests.

Recommended actions for employers and professionals

The Future of Professionals Report 2026 details three distinct paths for how organizations can deploy AI:

1. 1. 1. Elevate, which allows AI to handle rote tasks while keeping human expertise at the center;
      2. Scale, which uses AI primarily to increase capacity without increasing headcount; and
      3. Reimagine, which puts AI at the core as it rebuilds operating models and service propositions from the ground up.

The data also makes clear that the organizational and human costs of inaction are multifaceted. There are several concrete steps that both employers and professionals can take now, as outlined in the report, which include:

For employers:

• • • Choose a path and make it visible 鈥� The aforementioned three paths represent genuinely different futures with different commercial models, talent strategies, and definitions of professional value. Organizations must choose one deliberately and make it easily visible at the individual and leadership levels.
    • Close the rift in alignment before it results in talent departure 鈥�听More than one-third of professionals say they are working somewhere where the AI approach does not match their preference. These professionals are almost twice as likely to consider leaving within the next 12 months 鈥� and organizational leaders need to be aware of that.
    • Let strategy drive investments听鈥� Professionals working in organizations with a stated AI strategy are 3-times more likely to say AI is meeting or exceeding expectations for creating value at work compared to those at organizations without a stated AI strategy. This makes the value of establishing a stated AI strategy and ensuring its visible on a daily basis, a clear step for organizational leadership.

For professionals:

• • • Know which future you are working toward听鈥� Almost all professionals say they can see a future in one of the three paths. Understanding which one fits you is the first step to having a productive conversation about where the inconsistency is between your preference and your organization’s direction.
    • Invest in judgment as well as AI tool fluency听鈥� Judgment will always be a human differentiator in regard to AI, and the judgment that professionals apply on top of AI competency is what builds lasting professional value.

Professionals and organizational leaders need to make decisions in regard to their relationship with AI 鈥� and these decisions will determine the future of professional services. Those employers and professionals that choose their path deliberately, work to ensure alignment between strategy and experience, and invest in human judgment as seriously as they invest in technology will be the ones that can turn AI’s promise into a competitive advantage that compounds over time.

You can explore the full听

Key takeaways:

• • • The dulling effect is real, but the root cause is older than AI 鈥� Wherever gatekeeping institutions reward a narrow formula, their output converges long before any chatbot enters the picture. AI then accelerates optimization toward your already selected criteria.

    • Using AI for efficiency alone leaves the creative upside on the table 鈥� While most organizations deploy AI for simple drafting tasks, the bigger payoff comes from using it as a discussion engine 鈥� a sort of sparring partner that pressure-tests ideas and pushes thinking past the first plausible answer.

    • The highest-leverage intervention is reforming what you reward 鈥� The fix for this is upstream of the technology, and it comes from giving people time to make sure unconventional ideas actually survive your organization’s sorting mechanisms.

A tension sits at the center of nearly every serious conversation about AI and organizational strategy, and most leaders can feel it even if they haven’t named it yet.

On one side is the promise that AI can make teams more creative. That it can accelerate brainstorming, provide deeper research, identify hidden connections, and pressure-test ideas before they reach a client or a boardroom. When used well, AI is not a replacement for thinking but an amplifier of it.

On the other side, of course, is the fear that regular AI use is quietly dulling the creativity it’s supposed to enhance. That real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground 鈥� and that the less people work their creative muscles, the more they atrophy without them realizing it. This trade-off, swapping originality for efficiency, is a losing exchange.

Both of these intuitions are reasonable and both are, to varying degrees, correct. However, they aren’t equally weighted. The purely cautious camp is taking the bigger gamble, because any competitor that cracks the problem by figuring out how to capture AI’s creative upside while managing the dulling effect gets both the innovation edge and the efficiency gains. The cautious organization doesn’t just miss the upside, it falls behind on both fronts.

The catch is that cracking the problem requires correctly diagnosing what’s actually killing your creativity 鈥� and a prominent recent essay on this exact topic gets it instructively wrong.

A good question, poorly tested

Rebecca Winthrop, a senior fellow at the Brookings Institution and director of its Center for Universal Education, recently published in The New York Times arguing that AI is constricting creative thinking. Her central claim is that while chatbots produce polished language, they鈥檙e masking a narrowing range of underlying ideas 鈥� and this is especially dangerous for students, whose creative development is still taking shape.

The piece is worth reading, and not just as a foil. Winthrop draws on from Georgetown neuroscientist Adam Green, whose team has been tracking the range of ideas in college application essays before and after ChatGPT’s release. Green’s findings related to the before/after tracking study (which have not yet been peer-reviewed) are striking, finding that while post-ChatGPT essays used more diverse and colorful vocabulary, the ideas beneath that language converged. Human judges rated the AI-era essays as more creative, even though the substance had narrowed. In a separate study by Green’s team, cited by Winthrop, human-written essays contributed up to eight-times more novel ideas than AI-generated ones.

The fear is that regular AI use is quietly dulling the creativity it’s supposed to enhance, and the real fear is that the more people lean on these tools, the more their thinking converges toward the same polished, plausible, and fundamentally safe middle ground 鈥� and the less people work their creative muscles.

And Winthrop flags serious concerns that deserve far more attention than they typically get. For example, AI’s homogenizing pressure falls hardest on those students who sit farthest from the mainstream, including neurodivergent students and those from racial and linguistic minorities. That finding alone should be shaping education policy conversations and acting as a warning for innovation-conscious reformers.

Here’s where Winthrop鈥檚 piece stumbles, however, and where it becomes a cautionary tale for organizations that may be thinking about their own AI and innovation strategies. The evidence Winthrop chooses to build her case on 鈥� the college admissions essay 鈥� is possibly the worst genre in American education for measuring whether AI is killing creativity. Because the creativity in college admissions essays was already dead.

I should know. I鈥檓 one of its murderers.

The most templated genre in America

The college admissions personal statement has been reverse-engineered for decades. Well before any large language model existed, applicants had cracked the code: Be damaged, but not too damaged; be resilient but make it look like you did it yourself; and be whole now, because the institution wants guaranteed successes, not risky projects. And all of this must be delivered in a tone that makes the committee feel good about their institution’s role in a meritocratic society. Deviate from this formula and you’re taking a risk, but hit every beat and you’re in the pile that moves forward.

I know this because I lived it recently enough to still remember the specific frustration of trying to fit my own experiences into that template at the age of 17, twisting and contorting experiences I’d actually lived through into the shape I knew admissions readers were looking for while sanding away the human beneath when it didn鈥檛 fit. The authentic version of my story wasn’t what they wanted, the version that hit the beats was.

And there’s a further detail conspicuously absent from Winthrop’s essay: The college admissions consulting industry. It’s enormous, it’s been around for decades, and its entire business model is teaching applicants to write to the template. Some of these consultants charge $5,000 or more, and their product isn’t creativity, it’s optimization. They teach students to identify what the admissions committee rewards and deliver exactly that, with the rough edges smoothed away and the personal experiences torqued into the right emotional shape.

My family took this seriously enough to invest in help, and I was fortunate enough they had the means to do so. I had one of those consultants. Mine cost $2,000, and my parents had to sell my mom’s pinball machine to pay for it. I think sometimes about what it says that the path to higher education ran through a professional who taught me, essentially, to write to a formula rather than to present myself in a way that would have given the committee a more honest, unique portrayal of just who they were letting into their institution. The consultant didn’t make me less creative; the system that made the consultant necessary did.

And this is the blind spot in Winthrop’s argument. She treats pre-ChatGPT essays as the baseline for authentic creative expression, but that baseline was already shaped by an industry dedicated to template optimization. So when Green’s research finds that post-ChatGPT essays use richer vocabulary but converge on familiar ideas, the question worth asking isn’t just whether AI caused a measurable shift (Green’s controlled experiments suggest it did) but whether the underlying ideas were already converged at a more fundamental level that the metrics don’t capture. In essence, all AI may have done is make that convergence more visible while democratizing the surface polish.

There’s an entirely different version of Winthrop’s essay waiting to be written 鈥� one in which the same data tells a democratization story rather than an erosion story. Where a free chatbot gives a first-generation college student the same surface-level advantage that a $5,000 consultant gave wealthier applicants for years. That’s not a comfortable reframe for institutions already invested in the idea that their selection processes brings forth authentic individuality 鈥� but it’s the reframe the data actually supports.

The template always comes first

This isn’t unique to college admissions. Wherever institution rewards a narrow formula, it gets gamed 鈥� and the gaming predates whatever technology that has made it easier.

The video essayist Sarah Z traced exactly this pattern in , which makes the gap in Winthrop鈥檚 argument clearer. When the publishing industry rewarded a specific shape of trauma narrative in the 1980s and 鈥�90s 鈥� suffering resolved through individual resilience 鈥� the template grew so predictable that fabricators outcompeted honest writers. Laurel Rose Willson sold a satanic-ritual-abuse memoir and, years later, a Holocaust-survival story, citing the same self-inflicted wounds as evidence for both. Publishing houses weren’t fooled just because they were careless, they were fooled because they’d built a machine that searched for formula 鈥� and the system that rewards a narrow pattern is the same one that makes it exploitable.

AI doesn’t create that convergence, it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE 鈥� the bottleneck was there long before the tool arrived.

If your organization has ever received a stack of pitch decks, strategy memos, or RFP responses that all hit the same beats in the same order, congratulations! You’ve built your own admissions committee, but don鈥檛 blame AI.

AI doesn’t create that convergence; it just accelerates the optimization toward whatever you’re already selecting for. Blaming AI for homogenized output in an already-homogenized system is like blaming your GPS for traffic on the BQE. The bottleneck was there long before the tool arrived.

Threading the needle

Of course, none of this means the concern about AI and creativity is unfounded. The dulling effect is real, and anyone who uses these tools regularly has probably felt its subtle gravitational pull toward the center. Or in the way a chatbot’s first suggestion can quietly foreclose any other directions you might have explored on your own, or how it may produce something that sounds polished but carries none of your voice

A different research team 鈥� Anil Doshi and Oliver Hauser, behind the , Winthrop herself points to 鈥� put a name to the mechanism, anchoring. Handed an AI-generated idea, writers locked onto it, narrowing the range of what they produced before they’d really begun.

However, the solution on an organizational level isn’t to restrict the tool; rather it’s to address the institutional and behavioral factors that determine whether the tool narrows thinking or expands it.

In this determination, three things matter most:

First, use AI as a discussion engine, not just an automation tool 鈥� There’s a meaningful difference between asking a chatbot to draft something for you and using it to create something with you. This article is a case in point. I didn’t read Winthrop’s essay and immediately decide to write a response. Instead, I spent almost half an hour talking to Claude about the article, debating the argument, testing my objections, diving into Green鈥檚 research more deeply, and connecting the piece to ideas I’d been thinking about from completely different contexts, such as the Sarah Z essay. This article emerged from that conversation unintentionally, and it would not have existed without it.

Further, the ideas emerged pressure-tested and sharpened through a process that felt more like sparring than delegation 鈥� and that鈥檚 exactly the kind of process organizations should be targeting. Most organizations deploying AI are using it for efficiency 鈥� drafting, summarizing, formatting 鈥� and that’s fine. However, if that’s all you’re doing, you’re leaving the creative upside untouched, and your people are feeling the dulling effect without the compensating benefit. It takes an intentional push from leadership to get teams using AI as a thinking partner rather than a shortcut.

Second, give people time 鈥� This sounds obvious, but it matters specifically because of how AI interacts with time pressure. When people are rushing, they take the first adequate output and move on. With traditional workflows, shortcuts save time at the cost of quality or risk. With AI-assisted workflows, however, shortcuts save time at the cost of originality, because the first output from a chatbot is almost always the most conventional one. It’s the statistically average response, and reaching the edges takes iteration, pushback, and follow-up prompts that challenge the initial direction. That takes time, and if your people don’t have it, they’ll use AI the way a stressed applicant uses a college essay consultant, producing the safest possible version of whatever the system rewards rather than the innovative one which could change the game.

Third, reform what you reward 鈥� This is the intervention that actually addresses the root cause, and it’s the one most organizations will resist because it requires examining their own sorting mechanisms. If your evaluation criteria, your promotion structures, your review processes, and your RFP scoring rubrics all select for the safe and conventional, then AI will only turbocharge that selection.

You’ll get the template faster and more polished than ever, much like the admissions committee that rewards a narrow emotional arc and gets 300,000 identical essays. Or, if your firm rewards the pitch deck that hits every expected beat and takes no risks, AI will produce that pitch deck beautifully 鈥� and you’ll wonder why innovation has stalled.

Again, the intervention is upstream of the tool. What does your organization actually do when someone brings in an unconventional idea? What happens to the proposal that doesn’t fit the template? If the answer is that it gets smoothed out in review or tossed altogether, that’s not an AI problem.

The old traps didn’t disappear

Winthrop is right that creative thinking is something to protect and nurture. She’s also right that AI introduces new pressures that deserve serious attention. And she’s right that the stakes are highest for the people whose perspectives are already farthest from the mainstream.

But the college admissions essay wasn’t homogenized by ChatGPT, it was homogenized by decades of institutional selection pressure that rewarded a single template and penalized everything that didn’t fit. AI didn’t create that problem, it just made the template accessible to everyone, including the families that couldn’t previously afford $2,000 and a pinball machine to get their kid across the threshold.

Similarly, your organization’s creative output won’t be determined simply by which AI tools you adopt. It will be determined by what your leadership rewards, what your processes select for, and whether your people have the time and incentive to push past the first plausible AI-supplied answer.

The technology is new, but the traps are very old. And if you want to use AI to make your organization more innovative, the place to start isn’t the tool 鈥� it’s the template.

You can find more听听from the 成人VR视频 Institute here

Key insights:

• • • AI is creating a growing 鈥渧alue gap鈥� 鈥� A new report shows that while adoption is widespread, most professionals feel AI isn鈥檛 delivering the expected benefits, leading to frustration and additional client pressures.

    • This gap is driving real business risks 鈥� Organizations are seeing growing risks from this value gap, such as shadow AI use, lack of alignment with company strategy, and even potential talent loss as professionals consider leaving if AI value falls short.

    • Success depends on deliberate, well-executed strategy 鈥� Organizations need more than just more AI tools, the report shows, noting they must close the gap between strategy and day鈥憈o鈥慸ay practice, often through structured change management.

As AI adoption becomes more widespread in professional services 鈥� more embedded in professional workflows, offered services, and client expectations 鈥� it鈥檚 actually compounding the kind of challenges that service professionals can no longer ignore.

Indeed, these challenges have moved past the traditional worries of accuracy and security to include the more subtle ways AI is changing how professionals perceive their workplace and their ability to succeed, and it’s even seeping into areas like retention, professional development, and client expectations.

The latest iteration of takes a deep dive into the ways fiduciary professionals in the legal, tax, audit, accounting, compliance, risk, and global trade areas, are managing these changes, as well as how they鈥檙e navigating the pathways their organizations are following.

The 2026 report, distilled from a survey of more than 1,800 professionals across 62 countries, shows that AI adoption, unsurprisingly, is becoming widespread 鈥� with 74% of respondents saying they use AI tools several times a week and 44% saying they rely on those tools multiple times a day.

AI-bred challenges appear in new places

As AI adoption spreads and reshapes how professionals work, learn, and interact with clients, it can be a tremendous opportunity for organizations, as long as the human aspect of AI is brought along in tandem.

鈥淎I is a powerful force multiplier, but the judgment, relationships and accountability remain human, and鈥痶hat鈥痺on鈥檛鈥痗hange,” says Steve Hasker, President and CEO of 成人VR视频.

For more on 成人VR视频’ “Future of Professionals 2026” report,

For example, while 78% of clients say AI-enabled quality improvements are essential, only 6% say they are consistently receiving them, and that can damage a client relationship. Further, this disconnect ramps up the pressure on those fiduciary professionals delivering these services, leading many to move beyond where their organization may be technology-wise.

In fact, the report shows that more than one-third of professionals surveyed admit they use AI tools that their organization hasn鈥檛 sanctioned or in ways it can鈥檛 see, simply because they are frustrated by the quality of sanctioned tools or the lack of a clear AI strategy. This frustration 鈥� often seen as a gap in what they perceive the value of AI to be and what is actually being delivered 鈥� is widespread, with 91% of professionals saying they have felt it to some degree.

Worse yet, this frustration can manifest itself in other ways that can damage firms caught unawares. For example, the report shows that almost 3-in-10 mid-career professionals would change jobs within the next two years if AI fails to deliver the value they expect. This level of exodus could cause a cascading effect as experienced professionals take support staff, operational AI capability, and hard-won industry experience with them when they leave.

At an estimated $232,000 per replacement, this is significant potential liability on the horizon for many organizations, the report notes.

Imagining your professional future

Where fiduciary professionals and service firms go from here depends on the choices firms and corporate departments make about AI, the report states, offering three possible futures that have been drawn from how professionals are describing the current paths their organizations are on.

These three potential futures are based on how organizations choose to apply AI 鈥� from enhancing current work to fully reimagining it. And while each path has its advantages and challenges, the common thread through each is that whichever path is chosen, that choice needs to be made deliberately and with a strong commitment. Because better outcomes won鈥檛 come from arriving at a path by default 鈥� or by ignoring the human element.

Yet, as the report makes clear, no matter which path an organization chooses, it has a difficult road ahead, simply because AI strategy does not easily translate into AI practice. Indeed, almost one-third of professionals whose firm or department has a stated AI strategy say that strategy is not visible on a day-to-day basis; and 18% say their organization has no strategic direction on AI at all.

That means, roughly half of today鈥檚 fiduciary professionals are working in an environment in which a stated AI strategy either doesn鈥檛 exist or doesn鈥檛 match the reality of how their work is actually getting done.

Closing that gap and moving forward

Today, closing this strategy-execution gap is an organizational challenge that needs much more than new AI tools or stated policies 鈥� it demands structured change management that will allow organizational readiness to keep pace with evolving expectations around AI.

The most useful starting point, the report suggests, may not be whether your organization has an AI strategy in place, but whether the conditions for making such a strategy work are actually in place. Because as the report makes clear, professionals can tolerate imperfect strategies, but they cannot accept a gap between what is promised and what is delivered.

You can explore the full

Key takeaways:

• • • Understanding is not preparation 鈥� Most EU businesses are aware of 鈥� but not necessarily prepared for 鈥� the sweeping changes that ViDA is bringing.

    • Few businesses have a solid transition plan in place 鈥� Only 22% of tax and finance professionals surveyed say their organization has a formal, funded ViDA transition program in place.

    • Some key requirements are already changing 鈥� With e-invoice and real-time reporting requirements already shifting, businesses are in danger of falling behind, risking business continuity and non-compliance.

The European Union鈥檚 reforms around its value added tax (VAT) 鈥� known as VAT in the Digital Age (ViDA) 鈥� represent the most significant shift in tax compliance for businesses operating in the EU in a generation. ViDA is more than merely another new compliance requirement or technology upgrade. Indeed, many organizations will need to modernize their entire invoicing and tax reporting systems to get into compliance.

Jump to 鈫�

The new compliance horizon: 2026 ViDA Readiness Report

While ViDA鈥檚 EU-wide mandates for cross-border e-invoicing and digital reporting take effect in 2030, the pressure on organizations is already mounting as individual EU member states roll out a patchwork of national requirements.

Digging deeper on this, a new report from the 成人VR视频 Institute, , reveals a striking paradox in how EU tax and finance professionals are preparing for this overhaul. While awareness is nearly universal, a significant gap remains between awareness of ViDA and tax teams鈥� readiness for its changes.

Indeed, 86% of EU tax and finance professionals say they are familiar with ViDA; however, a deeper look reveals that only 35% possess a detailed understanding of the specific requirements of the regulatory reform package. This creates a state of “comfortable uncertainty,” in which high initial confidence can often mask a lack of preparation for the massive technological and operational changes ahead.

Riding the 鈥淐onfidence Curve鈥�

One of the most compelling findings from the report is the “Confidence Curve” that shows how many organizations often start their journey with high levels of optimism. In fact, even among respondents who say their organization does not yet have a transition program in place or has one that is fragmented across EU member states, 90% say they feel confident in their organization鈥檚 ability to achieve ViDA compliance.

However, the Confidence Curve shows that confidence often regresses during the assessment and planning phase. As teams begin to uncover the complexities of new multi-jurisdictional compliance and real-time reporting requirements, the percentage of respondents who say they are “not very confident” doubles. It is only after a program is funded and embedded into digital transformation strategies that confidence strongly rebounds.

You can learn more about

Despite the high stakes, the majority of organizations are still finding their footing, the report shows. Unfortunately, more than three-quarters (78%) of respondents say their organization has no formal, funded ViDA transition program with central governance in place, meaning that they鈥檙e working in a fragmented country-by-country fashion or are still in the assessment stage.

These delays are risky Many EU member states have already begun rolling out e-invoicing mandates. That leaves those organizations without programs in place at greater risk of falling further behind.

The ViDA-enabled opportunity

Despite the massive changes in VAT requirements that ViDA brings, the reform package also offers corporate tax functions a tremendous opportunity to elevate themselves from a cost center to a strategic business partner. As the report outlines, taking that path forward requires a cross-functional commitment across numerous corporate functions, including tax, finance, IT, and legal departments.

Yet, those organizations that move beyond providing the “minimum viable compliance” and instead take the opportunity to invest in standardized data and central governance will be better positioned to turn these regulatory mandates into a compliance advantage for the tax function and a competitive advantage for the organization going forward.

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Key insights:

• • • Cloud modernization is not enterprise transformation 鈥� Competitive advantage will come from architectures that produce measurable economic outcomes, not just scalable infrastructure or faster deployment.

    • AI success depends on data and governance architecture 鈥� Fragmented data, inconsistent definitions, and weak governance will cause AI to scale instability instead of intelligence.

    • 鈥淔ederated coherence鈥� is the new organizational survival model 鈥� Organizations must balance local agility with shared semantics, governance, interoperability, and economic measurement to compete in the AI era.

In this two-part blog series about the current state of cloud architecture, we previously looked into where this architecture has failed and now, into what the possible remedies might be.

As we noted previously, the argument is not that the cloud failed. The cloud delivered exactly what it promised: scalability, resiliency, and access to computational capability at speeds previously unattainable. The failures emerged downstream 鈥� as implications.

Organizations mistook infrastructure modernization for operational transformation. They accelerated systems without redesigning the economic and data architectures underneath them.

So, that means that the next phase of enterprise survival will not be determined by which organizations possess the most advanced infrastructure, the largest models, or the fastest deployment pipelines. It will be determined by which organizations can produce consistent, measurable, and economically aligned outcomes from fragmented environments that are increasingly dominated by AI-driven decision-making.

This is the point at which the market is beginning to separate into two categories 鈥� those organizations that are scaling capability, on the one hand; and those organizations that are scaling coherence, on the other. The difference between the two will define the next decade.

The shift from systems to economic architecture

For decades, organizational architecture centered on systems. Applications were mapped, integrations were documented, and governance was organized around technical domains. Even data architecture frequently existed downstream from software implementation rather than preceding it. That sequence is now economically inverted.

AI, regulatory transparency, real-time operations, and autonomous decision-making require organizations to engineer their architecture around outcomes first, data second, and systems third. The ordering is no longer optional today because AI amplifies architectural conditions already present inside the organization.

If fragmentation exists, AI operationalizes fragmentation faster. If duplication exists, AI scales duplication. If governance is inconsistent, AI accelerates inconsistent decisions.

The result is that organizations can no longer treat architecture as a technical discipline separated from operational economics. Indeed, architecture has become a measurable business competency that鈥檚 directly tied to the ability to make decisions quickly, respond to regulatory mandates, adapt operations, improve efficiency in the workforce, and enable success financial outcomes.

This is the emergence of what can be defined as AXTent 鈥� an operational model in which systems, governance, and data structures are explicitly engineered around measurable economic outcomes rather than technology deployment alone.

Table 1: Legacy architecture versus survival architecture

The distinction between traditional and AXTent architectures appears subtle, but it is not. Traditional architecture asked, 鈥�How should systems connect?鈥� AXTent asks, 鈥�How should the organization economically behave under constant change?鈥� That shift fundamentally changes design priorities.

The collapse of compartmentalized operating models

One of the least discussed consequences of the cloud era is the normalization of compartmentalized enterprise design. Departments optimized locally, applications proliferated independently, and data pipelines were built for immediate consumption rather than reusable enterprise value.

For a period of time, this appeared economically rational. Cloud economics rewarded speed, experimentation, and decentralized deployment. The hidden assumption, however, was that interoperability could eventually be solved later 鈥� today, with AI, later is now.

Organizations are discovering that independently optimized environments create organization-wide penalties, such as duplication of governance efforts, inconsistent reporting, conflicting analytics, rising costs for storage and processing, and delayed operational response times.

So, the problem is no longer technological debt alone; rather, it is interoperability debt that compounds economically.

Every duplicated data pipeline, inconsistent business definition, or isolated AI deployment can and likely does increase organizational friction. Over time, the organization becomes operationally dense 鈥� not because capability is lacking, but because coherence has deteriorated.

Table 2: The economics of architectural fragmentation

This is why many organizations now experience an architectural paradox 鈥� as technology capability increases, operational agility declines.

The new core competency: “Federated coherence鈥�

The surviving organizations of the next decade will not centralize everything, nor will they allow unrestricted decentralization because both models fail under modern conditions. Instead, organizations are moving towards 鈥�federated coherence鈥�, an operating principle that recognizes the reality that domains must retain operational flexibility, business units require localized agility, and regulatory requirements can differ by function and geography. However, overarching all this, federated coherence recognizes that enterprise semantics, governance, and economic measurement must remain interoperable.

This is the architectural middle ground most organizations have failed to achieve. Federated coherence is not simply a governance model, rather it is an economic design principle that allows organizations to reuse trusted data assets, standardize critical business definitions, reduce reconciliation overhead, accelerate AI deployment confidence, and respond to regulatory changes without widespread disruption.

The key insight is that interoperability is no longer a technical convenience 鈥� it is now a survivability multiplier. Organizations capable of adaptive interoperability will outperform those pursuing isolated optimization.

The measurement failure executives must address

One of the largest barriers to transformation is that most organizations still measure their technology capabilities incorrectly. Traditional metrics remain dominated by such concepts as speed of deployment, size of the infrastructure, utilization, and project delivery times.

These indicators measure activity, but they do not measure organizational improvement.

Table 3: Activity metrics versus economic outcome metrics

The next generation of architectural leadership will require direct alignment between technology and operational economics, including a reduction in decision times, decrease in reconciliation efforts, and an acceleration of regulatory response times. This next gen architecture will also measure reusable data, gains in process flow, and measurable margin improvement.

Without these measurements, organizations will continue operating within what can only be described as modernization theater that features visible technological movement with little to no structural economic advancement.

This is why so many corporate boards and executive teams increasingly struggle to articulate the return on investment for their spending on AI and the cloud. The investments are real and the infrastructure exists, but the measurement systems remain disconnected from economics. Architecture without measurable economic alignment simply becomes overhead.

Those organizations most likely to survive the next economic and technological cycle will not necessarily be the largest or the fastest adopters of AI. They will be the organizations that are most able to reduce complexity while increasing adaptability, govern their data without slowing operations, scale intelligence without scaling fragmentation, and align their architecture directly to measurable business outcomes.

In this environment, enterprise architecture now returns, but not as documentation, committees, or abstract frameworks disconnected from execution. It returns as an operational survival discipline. And those organizations emerging from this transition will increasingly resemble adaptive economic systems rather than static technical stacks.

Table 4: Characteristics of the adaptive organization

The implication is difficult but unavoidable. The future competitive advantage for many organizations will not be determined by what technologies they acquire, but by whether their underlying architecture can absorb continuous change without collapsing into operational friction.

That is the real challenge now unfolding beneath the modernization of the AI process. Moreover, it is why the next era of organizational modernization will not belong to those that simply automate faster; rather it will belong to those that finally learn how to architect survival.

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Key takeaways:

• • • Mandatory compliance mandates are growing 鈥� Pillar 2, DAC6, and other real-time reporting mandates are increasing obligations in dozens of jurisdictions today, and those tax departments without the infrastructure to meet these obligations are already behind.

    • Real-time documentation is critical 鈥� The window between a transaction occurring and a tax authority scrutinizing it is shrinking to near zero in some markets, meaning that documentation must exist at the moment it is generated, not reconstructed afterward.

    • Data quality is compliance quality 鈥� Real-time compliance brings with it heightened pressure to avoid incomplete or inconsistent inputs, because increasingly sophisticated analytics used by tax authorities will find them.

In 2023, a major European manufacturer was hit with a seven-figure penalty not because its tax return was wrong, but because it couldn’t demonstrate how it arrived at the right answer. No documented governance framework, no clear ownership, and no audit trail. The numbers were defensible, but the process wasn’t.

That gap 鈥� between getting the right answer and being able to prove it 鈥� is where corporate tax risk now lives.

Governments and tax authorities worldwide are to self-report accurately. They are building legal frameworks, digital infrastructure, and enforcement mechanisms to verify compliance in real time. And for tax departments accustomed to managing compliance on their own terms, the window for a comfortable transition is closing fast.

A global tightening

Tax governance requirements are intensifying on multiple fronts. In the United States, for example, the IRS’s Large Business & International division has significantly expanded its compliance campaigns, targeting transfer pricing, research & development (R&D) credits, and multinational structures. Section 174 of the 2017 Tax Cuts and Jobs Act now requires companies to amortize R&D expenditures over five or 15 years depending on where research occurs 鈥� a change that many tax departments are still working through while absorbing new obligations on top of it.

Internationally, the pace is faster still. The framework that the Organisation for Economic Co-operation and Development (OECD) created for its base erosion and profit shifting (BEPS) rules has been adopted by more than 135 countries. Pillar 2 鈥� the global 15% minimum corporate tax rate 鈥� is already in effect in dozens of jurisdictions and is actively reshaping how multinationals structure their tax affairs. These are not coming changes 鈥� they are current ones.

Mandatory disclosure regimes have expanded in parallel. The European Union’s DAC6 directive requires intermediaries and taxpayers to report potentially aggressive cross-border arrangements, with penalties in some member states reaching hundreds of thousands of euros. The United Kingdom’s Senior Accounting Officer regime goes even further, placing personal legal accountability on named senior executives for the adequacy of their company’s tax accounting arrangements. Similar regimes are expanding in Australia, Canada, and Brazil.

These are not isolated experiments. They represent that is not going to reverse any time soon.

The real-time reporting challenge

That means, corporate tax departments must respond to this shift because the traditional audit model 鈥� authorities review historical returns and request documentation years later 鈥� is being replaced in a growing number of markets. Spain, Hungary, and South Korea already require taxpayers to submit transactional data directly to tax authorities through mandatory electronic systems. The EU’s Value added tax (VAT) in the Digital Age initiative will extend similar requirements across all 27 member states beginning in 2028.

For tax departments, this reporting compression is the central operational challenge of the next five years. A team that once had 12 to 18 months to reconstruct documentation for an audit now needs that documentation to be accurate and defensible at the moment it is generated. That requires a fundamentally different operating model 鈥� not just better record-keeping, but automated data capture and real-time reconciliation built into core financial systems 鈥� along with the ability to transfer that documentation electronically in real time.

3 actions tax departments must take now

To begin to address this dramatic change, corporate tax departments need to act now, taking steps that include:

1. Building a formal governance framework

Tax departments need written governance frameworks that clearly define what party owns each compliance decision, how decisions are reviewed and approved, and what controls exist to catch errors before filing. This means named ownership of obligations, documented sign-off processes, and regular internal reviews against a compliance calendar.

In the UK, this is already a legal requirement ; and similar standards are emerging in Germany, Australia, and across the EU. A framework should cover at minimum; the ownership of each material filing obligation; the review and approval chain for positions taken; escalation procedures for uncertain tax positions; and a schedule for internal control testing. Without these processes in place, tax departments could face regulatory penalties, personal liability for senior leaders, and reputational damage that may be difficult to recover from.

2. Fixing the data access problem

Tax departments consistently lack reliable, timely access to the financial data they need. This is primarily an organizational problem, not a technology one. Tax functions often sit downstream from finance systems designed without tax requirements in mind 鈥� meaning data often arrives aggregated, reclassified, or stripped of the granularity needed for compliance work.

Solving this requires tax leaders such as finance, IT, and business operations 鈥� not just to request data, but to influence how that data is captured at its source. That means participating in enterprise resource planning implementations, establishing data requirements for new business lines before they launch, and building direct feeds from source systems rather than relying on manual extracts.

3. Treating data hygiene as a compliance control

Tax authorities in the UK, the Netherlands, Germany, and the US are deploying advanced analytics to identify anomalies in corporate filings. Unexplained variances between statutory accounts and tax returns, inconsistencies in intercompany pricing, or mismatches between VAT and corporate income tax data could all trigger closer scrutiny.

Data hygiene must be treated as a compliance control, not an IT issue. In practice that means establishing reconciliation checkpoints between source data and tax inputs, maintaining documented data lineage so any figure in a return can be traced to its source, and conducting data quality reviews before filing deadlines 鈥� not after.

The bottom line

The regulatory trajectory is set, so that means the question for tax leaders whether their department will be ready when tested. Governance, data access, and data quality are no longer back-office concerns 鈥� they are the foundation upon which defensible compliance is now built.

Tax department leaders need to build that foundation now, before the examiner asks.

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Key insights:

• • • Cloud modernization created accumulation, not transformation听鈥� Many enterprises scaled infrastructure faster than they integrated systems, leaving fragmented data, duplicated processes, rising costs, and weak links between IT investment and business value.

    • AI and regulation now expose weak data architecture听鈥� Agentic AI, real-time decision making, and regulatory reporting depend on consistent, traceable, well-governed data 鈥� not fragmented systems or after-the-fact governance.

    • Enterprise architecture must be rebuilt around outcomes and economics听鈥� Instead of treating the cloud as the strategy, organizations should define business outcomes first, structure data as a reusable asset, and measure architecture by revenue, cost efficiency, regulatory accuracy, and decision speed.

In this two-part blog series about the current state of cloud architecture, we look into where this architecture has failed and, in the next part of the series, what the possible remedies might be.

For the better part of the last 15 years, IT enterprise architecture definition and management didn鈥檛 disappear, it was deprioritized and replaced by as-a-Service solutions. The rapid rise of cloud platforms such as Amazon Web Services, Microsoft Azure, Snowflake, and Google Cloud made it possible to stand up infrastructure, deploy applications, create advanced databases, and scale environments without the same level of architectural rigor that was once required. Speed replaced structure, and access replaced integration.

Today鈥檚 cloud realities

The problem is that what was built during this last technological explosion was not architecture 鈥� it was accumulation. Systems expanded, data proliferated, budgets exploded, and organizations convinced themselves that connectivity was the same as coherence, and that data replication was the same as the system-of-record.

These assumptions have now been exposed as false positives. Over the last three years, AI, real-time decision making, and regulatory transparency have fundamentally changed the requirements. These are not technologies that sit on top of fragmented environments, they are data-driven capabilities and outcomes that depend on precision, integration, and sequence. The arrival of agentic AI and its stringent objective-based principles cannot tolerate data ambiguity and fragmented architectural designs.

The problem is that what was built during this last technological explosion was not architecture 鈥� it was accumulation.

AI does not fail at rates exceeding 80% because models are weak, it fails because the underlying data is inconsistent, inaccessible, or economically misaligned. Regulatory frameworks do not struggle because rules are unclear, they struggle because data cannot be traced, reconciled, or produced in real time. What the cloud enabled 鈥� rapid deployment without disciplined integration 鈥� is exactly what now constrains performance. The issue is no longer whether systems can scale, but whether they can produce measurable, consistent, and adaptable outcomes.

This is where enterprise architecture returns, but just not in its previous form. The discipline cannot simply revert to academic frameworks and abstractions that were designed for a different software era. It must be rebuilt around a different sequence that sees business outcomes first, data second, and then systems engineered within those constraints. Today, enterprise architecture must be defined and managed by economic KPIs, value added, and its adaptability to rapidly changing business realities.

Where the model broke

The failures experienced today in cloud architecture are not singularly technological. Cloud platforms deliver exactly what they promise 鈥� scalable, resilient, highly available infrastructure. Rather, the failure is architectural, and more precisely, involves the economics of compartmentalized capabilities.

Enterprise value is not created at the infrastructure layer. It is created where data informs decisions, and decisions drive outcomes. By over-rotating toward infrastructure, organizations optimized the least differentiating component of the enterprise stack, while leaving the highest-value layers largely untouched.

The result is a structural imbalance in which data remains fragmented across domains, business logic continues to operate in silos, governance is applied inconsistently and often retroactively, and measurement frameworks fail to tie technology activity to financial performance.

In this model, the cloud amplifies existing conditions. If fragmentation exists, it scales fragmentation. If inefficiency exists, it scales inefficiency. Modern infrastructure, applied to legacy architecture, produces modernized dysfunction.

What makes the cloud鈥檚 illusion particularly persistent is that its failure is rarely framed in economic terms. Cloud investments are justified through technical metrics such as uptime, latency, migration progress, and consumption efficiency. And while these are necessary, they are not sufficient. They do not answer the only question that ultimately matters: 鈥�Did the investment improve the economics of the business?鈥�

Enterprise value is not created at the infrastructure layer 鈥� it’s created where data informs decisions, and decisions drive outcomes.

In many cases, the answer is no 鈥� at least, not in a way that can be clearly articulated. Instead, organizations experience cost expansion without proportional productivity gains, increased data duplication that drive storage and processing inefficiencies, extended timelines for analytics and reporting despite real-time capabilities, and persistent manual intervention in regulatory and operational workflows.

The absence of a direct line between architecture and outcome creates a vacuum often filled with disconnected KPIs, measurement solutions, and most recently, AI-automation. And with this interoperable vacuum, activity and speed have been mistaken for progress.

Figure 1: Cloud accumulation meets enterprise architecture shifts

The data reality beneath the surface

The cloud did not fail to deliver transformation; rather it exposed why transformation had not occurred 鈥� and at the center of this exposure is data.

Most enterprises operate with data architectures that were never designed for interoperability, reuse, or regulatory-grade consistency. Definitions vary by function, pipelines are purpose-built and duplicative, and governance is layered on after the fact. Automation was designed using business rules, then software architectures, then what the data needed. Therein resides the structural disconnect for enterprise architecture in AI solutions: They are out of order.

When these legacy conditions are moved to the cloud, they do not improve, they accelerate. The organization gains speed without alignment, scale without standardization, and access without coherence. For regulated industries, this creates a compounding risk of inconsistent outputs across reporting channels, increased reconciliation overhead, reduced confidence in data lineage and auditability, and slower response to regulatory changes.

What appears to be a technology issue is, in fact, a failure of data design.

Reframing the problem

To move forward, the premise must change. The cloud is not the strategy; rather it鈥檚 the environment. Transformation does not occur when systems are moved, it occurs when the relationship between data, decisions, and outcomes is fundamentally redesigned.

This requires an organization-wide shift from infrastructure-led thinking to what is defined as value architecture. Simply put, value architecture includes data that is structured as a reusable, governed asset 鈥� not a byproduct of applications, and business outcomes that are defined upfront and used to drive architectural decisions. Its governance is embedded at the point of data creation and distribution, and it replaces redundancy by making reuse the primary scaling mechanism. Finally, measurement is tied directly to financial and operational impact.

This is not a rejection of the cloud; rather, it鈥檚 a repositioning of its role and value proposition.

The implication is both direct and unavoidable. If your current strategy cannot clearly articulate how technology investment improves the economics of your business, then your organization is operating within the cloud illusion. However, this is not a critique of past decisions. It is a recognition that the next phase of transformation requires a different operating model 鈥� one that explicitly connects architecture to economics. Moving forward, what was forgotten in the past is now a future core competency.

Most organizations using as-a-service software had assumed that the cloud provider, vendor, or combination of those dealt with the complex liabilities of making designs interoperable. The implication moving forward 鈥� as well explore more in the second installment of this series 鈥� is that service software architectures using the system ideation approaches within AI silos are failing miserably, and there are few who understand the designs and skills needed to guide enterprises in the future.

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Key insights:

• • • Consensus among AI systems is not the same as correctness 鈥� Agreement between AI models often signals shared blind spots, not truth; and AI errors can be highly correlated across instances and even across model families.

    • Productive disagreement must be explicitly designed into AI advisors 鈥� Multi鈥慳gent AI systems are most effective when they are intentionally built to preserve meaningful disagreement, not just to synthesize a unified response.

    • The future of AI advisory mirrors long鈥憇tanding human decision-making 鈥� Modern multi鈥慳gent AI design has a long historical lineage; yet, across all examples, the same principle holds: The best decision systems are engineered for internal conflict.

In this new two鈥憄art blog series, we explore why AI works best as an executive advisor not by delivering consensus answers, but by being intentionally designed to identify, preserve, and productively leverage disagreement. In the first part, we saw why a single AI advisor is structurally vulnerable; now, in this concluding part, we look at what happens when you design disagreement on purpose.

The academic evidence for multi-agent AI systems has been building rapidly, and the most important findings aren’t about the power of agreement. They’re about the danger of it.

In February, , a product that sends every query simultaneously to three frontier AI models (Claude, GPT, and Gemini) then uses a fourth chair model to synthesize a unified answer. The product’s value proposition isn’t that three models produce a better answer than one; rather, it’s that divergence between models is treated as a signal. When models converge, that indicates confidence, but when they diverge, that indicates the user should slow down.

Studies have borne this out. Multi-agent debate compared to single-model generation, and researchers at the University of G枚ttingen found that , with their voting protocols outperforming other decision structures. However, potentially the most important finding cuts against the hype. In a 2026 paper, , the authors demonstrated that AI model errors are highly correlated both within and across model families. When three instances of the same model agree, it doesn’t mean they’re right, rather it means they may share the same blind spots. Aggregation increases consensus faster than it increases truth.

The future of AI-assisted executive decision-making may look less like a single brilliant oracle and more like a room full of advisors that may often disagree because that’s how the best decisions have always been made.

This finding cuts both ways for practitioners like 成人VR视频 enterprise architect Zafar Khan and his two AI advisors, Adrian and Elara, that were built on the same underlying model but differentiated by their analytical frameworks rather than their architecture. The divergence they produce is real and visible. For example, the analysis the two AI advisors did on a deal undertaken by Eaton Corp., in particular generated genuinely different conclusions because the two advisors were oriented towards different priorities.

Yet, research suggests that same-model divergence, while effective, has a ceiling. Prompt-driven personas can ask different questions, but they share the same training, the same blind spots, and the same failure modes. Khan is candid about this, noting that his current system is in the 鈥渧ery early鈥� stages and is not a finished product. The value right now, he says, isn’t that Adrian and Elara are equivalent to truly independent minds, it’s that even a first-generation version of structured disagreement can identify insights that a single advisor would miss. It鈥檚 a large stride rather than an arrival at the ultimate destination.

The future of AI advisory is in the past

The principle behind this diverging analysis concept isn’t new. Indeed, it might be one of the oldest ideas in institutional design, rediscovered independently by many institutions that had to make decisions under uncertainty. Socrates built a philosophical method around cross-examination; Pope Sixtus V formalized opposition by creating the Devil’s Advocate in 1587; and the RAND Corporation operationalized it during the Cold War with the Delphi Method, using structured anonymous iteration to prevent groupthink.

The through-line across two millennia is simply that the best decision-making systems don’t minimize disagreement, rather, they engineer it.

Today, the developer community now uses production-grade code review tools to assign architecture, security, and functionality analysis to separate agents, using majority voting for routine decisions and unanimous consent for irreversible ones. And what Khan has built and what Perplexity, Microsoft’s Agent Framework, and a growing ecosystem of multi-agent tools are now pursuing, are the latest iterations of the simple concept: Internal conflict is not a system failure, it is a design requirement.

The question is no longer “whether”

Khan’s vision for what should sit at the decision table is specific 鈥� five AI advisors spanning technology, finance, regulation, workforce, and geopolitical risk. Each applies its own analytical framework, with the human executive responsible for integration and final judgment. The guardrails are three: i) transparency about what data the system uses; ii) verifiability that sources are legitimate; and iii) human accountability at every decision point.

“The race towards AGI [artificial general intelligence] is moving faster,” Khan acknowledges, adding that the human needs to be in the loop in order to bring AI to work in a governance fashion and an ethical way.

“I want to show the interaction between human and AI advisor, how they’re thinking through the problem together,” he explains. “Where the human judgment covers the analysis and where it diverges.” In other words, when the AI advisors agree, that’s your green light. When they diverge, that’s the conversation your board should be having.

The future of AI-assisted executive decision-making may look less like a single brilliant oracle and more like a room full of advisors that may often disagree because that’s how the best decisions have always been made. The technology to build that room now exists; however, the question is whether today鈥檚 leaders have the discipline to listen when the room argues back.

For more on AI transformation in the professional services market, you can download the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report

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.

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