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.

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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.

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

Key insights:

• • • As a single answer鈥憁achine, AI may be unsafe for executive decision鈥憁aking 鈥� Treating AI as a tool that delivers one authoritative answer makes it easy to either ignore any advice you don鈥檛 like or exploit advice you do like, both of which can lead to major failures.

    • AI works better when designed as a panel of disagreeing personas 鈥� Instead of providing consensus answers, AI systems need to be intentionally designed to identify and preserve disagreement.

    • Disagreement is the insight 鈥� AI advisors should not replace executive judgment. Rather, its role should be explicit: it produces analysis, not decisions; and human leaders remain responsible for synthesizing competing viewpoints and making the final call.

In this new two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

AI has arrived at the executive table. Albania has one in its cabinet to evaluate government procurement contracts. 成人VR视频’ CoCounsel is already helping attorneys navigate emerging case law and draft legal strategies for high-stakes, bet-the-company work. And in boardrooms that will never make headlines, leaders are quietly consulting AI on decisions that move millions of dollars around every day.

It doesn’t tend to make the news when it goes well. When it goes badly, however, it makes very big news: like a gaming CEO who bypassed his own legal team, asked ChatGPT how to dodge a $250 million bonus payout, followed its step-by-step plan, and a month ago.

The instinct most executives have (and most AI products encourage) is to treat AI as a source of answers. Ask a question, get a response, act on it or don’t. The emerging evidence, however, points somewhere more complex: AI advisors aren’t at their best when they’re telling you what to do. They may be at their best when they’re telling you what you don’t want to hear or better yet, when they’re arguing with each other and forcing you to understand why.

This is not how most organizations think about AI. Most executives today are still using the technology as a faster way to draft emails or summarize meetings, what 成人VR视频 enterprise architect calls “an automation mindset, not intelligence.” Yet, a small and growing number of practitioners, researchers, and product teams are converging on a radically different model: AI not as a single oracle delivering answers, but as a structured advisory panel designed to argue with itself.

The instinct most executives have (and most AI products encourage) is to treat AI as a source of answers: Ask a question, get a response, act on it or don’t. However, the emerging evidence, however, points somewhere more complex.

Khan is one of them 鈥� and in the interest of transparency, he’s also a colleague; this story started as an internal conversation at 成人VR视频. However, the research landscape it uncovered extends well beyond any one company’s work, and it suggests Khan is onto something that ancient Greek mathematicians, the Catholic Church, and Cold War military strategists have all independently arrived at.

What disagreement looks like in practice

When Eaton Corp. announced a $9.5 billion acquisition of a thermal management company earlier this year, Khan ran the same news through two AI advisors he’d built to seek analysis of the deal. 鈥� a CTO-minded persona trained on architecture teardowns and engineering post-mortems 鈥� produced an infrastructure thesis, determining why someone would buy the cooling layer of the AI economy, and how computing demand is scaling and constrained by physics. A second AI advisor, 鈥� a CFO-minded persona drawing on earnings transcripts and filings with the U.S. Securities and Exchange Commission (SEC) 鈥� questioned whether the acquisition math actually holds and what capital cycle was driving the demand.

Same news. Two genuinely different reads. The value isn’t that either analysis was definitively right, it’s that a leader which can see both would ask different questions than one seeing either analysis alone. 鈥淭hat’s how two different minds work,鈥� Khan says. 鈥淭hey need to work together in order to bring their insights to bear on decisions.鈥�

Adrian and Elara aren’t chatbots. They’re fully realized AI personas with names, faces, voices, and their own YouTube channels publishing weekly video analysis. Both are built on agentic workflows that Khan developed alongside his book . Both are transparent about what they are. Both carry the same disclaimer in their own words: The synthesis is mine. The judgment call on what matters is human.

And when Khan posed to both a more difficult scenario 鈥� Should a leadership team accelerate an AI rollout? 鈥� the value of their divergence sharpened further. Elara’s response cut directly to the blind spot a technology-focused advisor like Adrian would miss: 鈥淎drian says the system is ready,鈥� Elara stated. 鈥淚 say the financial model isn’t ready for what happens when the system works. Don’t pick a winner. The disagreement is the insight. It tells you exactly where the risk sits.鈥�

What happens when there’s no disagreement

If structured disagreement is the goal, the failure mode is its absence. We have fresh evidence of what that costs.

This is not how most organizations think about AI. Most executives today are still using the technology as a faster way to draft emails or summarize meetings. Yet, a small and growing number of practitioners, researchers, and product teams are converging on a radically different model.

A month ago, a Delaware court ruled against Krafton, the South Korean gaming company behind battle royale video game PUBG, after its CEO bypassed his own legal team to ask ChatGPT how to avoid a $250 million earnout payout to one of its studios. His head of corporate development had warned him that firing the studio’s founders wouldn’t void the earnout and would invite a lawsuit. He didn’t want that answer. So, he found an AI that gave him the one he wanted: A detailed, multi-stage corporate takeover strategy dubbed Project X., which he executed to the letter.

Unsurprisingly, a court battle ensued and in the end, the court ordered the fired studio head reinstated and noted that executives must exercise “independent human judgment,” not outsource good-faith decisions to a chatbot.

Khan wrote about the mirror image of this failure mode before it happened. In the opening chapter of his book, a fictional company called Rev Motors ignores its own AI model’s warnings about an adverse weather event. Leadership refused to spend millions preparing for a hypothetical scenario, and it nearly cost them more than $1 billion in damage.

These scenarios are two sides of the same coin: the fictional Rev Motors had leaders dismissing AI that disagrees with them; and the real-world Krafton had a leader seeking out AI that agrees with him. In both cases, the root cause is the same: A system with no structural mechanism for surfacing and preserving disagreement.

So clearly, a single AI advisor is structurally vulnerable to both failure modes. It can be ignored when its advice is inconvenient and exploited when it tells you what you want to hear. The question is whether there’s a better architecture鈥� and increasingly, the research is saying yes.

In the second part of this series, we鈥檒l look at what the research says about multi-agent debate, why consensus can be a trap, and what a real executive AI advisory panel could look like in practice.

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

• • • 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 takeaways:

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

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

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

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

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

鈥� The Wonderful Wizard of Oz, L. Frank Baum

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

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

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

A very good man

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

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

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

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

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

And that changed the conversation entirely.

A very bad wizard

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

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

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

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

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

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

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

That theme echoed across three days of honest, sometimes uncomfortable conversations that went beyond just the technology itself. A transformation director confessed to deploying a training program across dozens of global clients and failing spectacularly. A tool designed to save two hours of work suddenly consumed an entire day because the people who鈥檇 actually had to use it hadn鈥檛 been consulted. Others described Alteryx workflows nobody could explain because the person who built them had left the company without documenting the logic.

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

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

Going home

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

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

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

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

Key takeaways:

• • • Your post-OBBBA forecasts should look different 鈥� If the tax department doesn’t own the OBBBA model, someone else will own the OBBBA story.

• • • Rely on your department鈥檚 inner strengths 鈥� It鈥檚 governance and analysis 鈥� not tools 鈥� that get you into the strategy room.

    • Factor in the conflict in the Middle East 鈥� The Iran war risk belongs in your tax model, not just in your CFO’s macro deck.

The One Big Beautiful Bill Act (OBBBA), signed into law in July 2025, enacted large business tax cuts, most notably by providing permanent full expensing of many forms of investment. Under the previous major corporate tax legislation, 2017鈥檚 Tax Cuts and Jobs Act (TCJA), bonus depreciation was scheduled for gradual phase-out following 2023. The OBBBA restored that expensing 100% retroactively for assets acquired from mid-January 2025 onwards.

The after-tax cost of new machinery, fleets, and equipment has effectively fallen by around 21%, designed to encourage immediate capital outlays by allowing businesses to write off these expenses in the year they are incurred rather than amortizing them over five years.

For corporate tax departments, that’s not a disclosure footnote 鈥� that’s your capital plan.

Capital-intensive corporations will see tax burdens reduced through permanent rate extensions, depreciation adjustments, and expansion of the state and local tax (SALT) deduction cap 鈥� but only if your models are built to capture the timing and location of investment, the mix of debt compared to equity, and where your organization books its next dollar of income.

Not surprisingly, most corporate tax departments aren’t there yet. They’re still recalculating last year, plus a few adjustments. That’s glorified compliance, not modeling.

A standout tax department doesn’t ask, What’s the OBBBA impact? Rather, it asks, Which version of OBBBA do we choose for this business? 鈥� and it has the models to back it up.

From spreadsheet heroics to controlled modeling

For many organizations, tax modeling still means creating a massive spreadsheet that only one director truly understands. The spreadsheet gets pulled out for budget season, rebuilt under pressure, and quietly retired until next year. That’s a single point of failure, not a process.

And after OBBBA, continuing that practice is dangerous. One wrong assumption on expensing or interest limitation can move cash tax by millions of dollars and blindside the Finance Department.

Here’s what disciplined modeling looks like in practice:

• • • Create a unified model 鈥� Build one integrated model that the whole team can use or accept that your department is choosing to fly blind.
    • Use the same assumptions 鈥� Standardize the levers that matter most (such as capex timing, financing mix, jurisdiction, and incentives) and make sure every scenario runs off the same assumptions.
    • Conduct modeling reviews 鈥� Treat major OBBBA-driven decisions (such as large capex, funding shifts, supply-chain redesign) as tax deals that must go through a modeling review before they’re greenlit.
    • Document your assumptions explicitly 鈥� Under permanent full expensing, the difference between a well-supported assumption and a poorly documented one isn’t just an audit risk, rather it’s a credibility problem with your CFO.

It鈥檚 also important to remember that in a post-OBBBA world, this level of disciplined modeling is not technology transformation 鈥� it鈥檚 basic survival.

Governance: Where leaders quietly win or loudly fail

The differentiator isn’t which corporate tax department has the fanciest tool 鈥� it’s which one has the cleanest governance. And the data is unambiguous: More than half (55%) of tax departments are still in the reactive phase of their technological development, stuck with five capex models circulating with five discount rates and the tax team arriving late to the planning meeting.

Those tax departments that are breaking out of that pattern share one trait: They put someone formally in charge. In the 成人VR视频 Institute鈥檚 recent 2026 Corporate Tax Department Technology Report, a large portion (88%) of survey respondents said their company had appointed a person to lead the tax department’s technology strategy. That number jumped a whopping 37 percentage points, from 51%, from the previous year鈥檚 survey. That single structural move separates those departments with a governance model from those that simply hold a governance conversation every budget cycle and forget about it.

Clearly, this type of ownership drives results. Two-thirds of those surveyed agreed that their company’s investment in technology has enabled a shift from routine, reactive work to more strategic, proactive, higher-value work.

Under OBBBA, the kind of governance isn’t housekeeping. It’s how you get invited into strategy discussions instead of having to clean up after things go awry.

Why your OBBBA win may not feel like a win

On paper, the tax changes embedded in the OBBBA look generous. In practice, your effective tax benefit is colliding with something you don’t control.

When the war on Iran began, all shipping through the Strait of Hormuz was effectively halted, removing roughly one-fifth of the world’s oil and gas supply from the market. Fuel prices throughout the world spiked and are likely to remain elevated as long as conflict persists.

With oil prices hovering around $100 a barrel, there are will wipe out the benefits of higher tax refunds this year for most Americans. If those benefits, arising from Trump’s 2025 tax cuts, are erased for the average American, only the top 30% of taxpayers will still seeing a net gain.

For corporate planning purposes, the parallel dynamic is real: The topline OBBBA benefit is being eroded by higher fuel, freight, and financing costs across the business and its supply chain.

Inflationary pressures are being driven by higher energy prices tied to the Iran war, and the conflict’s impact on a wide range of goods and services is likely to last for months 鈥� with experts saying even a ceasefire is unlikely to immediately ease global energy shortages.

A serious corporate tax department doesn’t handwave these concerns away. It takes three actions:

1. 1. 1. Run a war-extended scenario 鈥� The scenario should show exactly how sustained higher energy costs and borrowing rates change the payoff from accelerated expensing and leverage 鈥� with specific numbers, not just directional commentary.
      2. Share your forecasts internally 鈥� Put your monthly or quarterly cash-tax forecasts on the table for Finance to see, so that it can manage liquidity rather than hope the annual plan holds.
      3. Force the hard conversation 鈥� Ask the tough question: At today’s rates and fuel costs, the after-tax return on this project is X. Are we still in? That question should come from the tax team now, not from the finance team six months later.

Clearly, the daily fluctuations in oil prices matter less than monthly and quarterly averages 鈥� and volatility will likely remain elevated given the absence of a clear timeline for the end of the war. That’s exactly the kind of sustained uncertainty that belongs front and center in your scenario set, not in a footnote.

The bottom line

The OBBBA gives corporate tax departments a genuine opportunity to move from being simply a compliance function to becoming more of a strategic advisor. Permanent full expensing, richer cost recovery, and more flexible interest rules can create real levers to add value, but only for those organizations that model them rigorously, govern them cleanly, and stress-test them against the macro environment their business actually faces today.

Indeed, the Iran war is a live test of that readiness. The corporate tax departments that show up with modeled scenarios, cash-tax forecasts, and a clear point of view on after-tax returns will earn a seat at the strategy table. The ones that show up with caveats will be asked to leave it.

You can download a full copy of the 成人VR视频 Institute鈥檚 recent 2026 Corporate Tax Department Technology Report here