Key insights:

• • • AI adoption has become an urgent business imperative 鈥� Enabling corporate functions are under pressure from leadership, business stakeholders, and employees to demonstrate tangible AI-driven value.

    • Slow AI adoption creates risk 鈥� Many professionals are frustrated by limited access to high-quality AI tools, which contributes to increased employee turnover and growing use of unauthorized shadow AI

    • Success depends on coordinated transformation 鈥� Organizations need a deliberate AI strategy rather than scattered experimentation to help guide responsible AI adoption across the organization.

Today, internal corporate enabling functions 鈥� such as legal, tax, global trade, compliance, and risk 鈥� find themselves at a crossroads as they face mounting pressures from three critical fronts: i) internal stakeholders that are demanding faster, more informed decisions; ii) finance departments that are expecting AI-driven efficiency and cost control; and iii) a professional workforce eager for tools that enhance the value of the work they do.

The message from the C-Suite is clear: AI must deliver tangible results now, according to the recent 成人VR视频听2026 Future of Professionals Report.

To help internal corporate function leaders manage this pressure and move forward with confidence into an AI-enabled future, 成人VR视频 has published a new action paper, Future of Professionals Report 2026: Actionable insights for corporate leaders, drawing on insights from hundreds of internal corporate professionals.

Facing down the triple pressures

The urgency that corporate function leaders are facing is underscored by those three areas of pressure. For example, almost half of professionals surveyed in enabling functions say they are either already experiencing the financial consequences of lagging AI adoption or are expecting to within a year. Many enabling functions have long been expected to absorb growing workloads without proportional increases in resources. Now, AI is increasingly viewed as a way to expand capacity and improve efficiency, making delaying its adoption a potential source of budgetary and competitive risk.

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

Stakeholder pressure is equally intense. As many business units accelerate their own AI deployments, they expect the organization鈥檚 other enabling functions to keep pace. If these functions become bottlenecks, they risk being sidelined or being perceived as obstacles rather than strategic partners. Indeed, more than half of corporate professionals say they are facing significant pressure from stakeholders to act faster on AI, with in-house legal teams feeling this most acutely.

Yet the pressure coming from the workforce may be the most alarming. The action paper shows that fully 30% of professionals say they are considering leaving their organizations within two years if the gap between the AI-driven value they expect and what is made available to them isn鈥檛 addressed. Access to professional-grade AI tools has become a key factor in job decisions, yet nearly 6-in-10 professionals say they lack access. This gap contributes to both retention challenges and the rise of unauthorized AI use, increasing compliance and governance risks.

Choosing the right path

Faced with the reality of these pressures, corporate function leaders must choose a strategic path for AI adoption. The action paper outlines three primary trajectories:

• • • Using AI to elevate by shifting human effort to high-value, judgment-based work.
    • Using AI to scale by leveraging AI to handle increased workloads without increasing headcount while optimizing for efficiency.
    • Using AI to reimagine by rebuilding workflows around AI鈥檚 capabilities, such as implementing shared data infrastructure and real-time dashboards.

However, knowing the path is not the same as walking it. The action paper also highlights a potential execution gap, in which a lack of coordination and shared accountability across functions derails any real progress. This is a particular problem for enabling corporate functions because many departments often operate in silos, using different AI tools and standards, which leads to fragmentation and operational bottlenecks.

The solution, as the paper outlines, lies in building a shared framework for AI governance and accountability, with fiduciary functions like legal, tax, and compliance taking the lead. Some critical recommendations outlined in the paper include advocating for professional-grade AI tools, planning for an evolutionary journey through AI adoption, and leading an organization-wide conversation about AI governance and standards.

Finally, the paper encourages corporate leadership teams to step back from daily pressures and engage in structured exercises to define a shared vision for AI within the organization. By developing a long-term roadmap that considers processes, data, technology, people, and risk, corporate leaders can ensure AI adoption delivers both immediate value and sustainable competitive advantage for the future.

You can read a full copy of the听Future of Professionals Report 2026: Actionable insights for corporate leaders 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 makes everyone鈥檚 job easier, including cybercriminals 鈥� With Anthropic鈥檚 Claude, an attacker breached Mexico鈥檚 federal tax portal in less than an hour.

    • Cybersecurity breaches may be a canary in the coal mine for a much larger problem 鈥� This is the second publicly disclosed Claude-enabled attack in less than a year.

    • Nearshoring the risk 鈥� Beyond the immediate damage to affected citizens and businesses, foreign investors and multinational evaluating their operations in Mexico may see a red flag that could discourage them from moving forward.

Amid the 2025 year-end celebrations 鈥� while most people were busy wrapping gifts, decorating trees, spending time with loved ones, and sketching out their 2026 resolutions 鈥� a quieter threat was unfolding. Unlike the Grinch, it had no interest in stealing Christmas cheer; instead, it set its sights on something far more valuable: more than 150 gigabytes of sensitive information from Mexican government organizations.

Armed with what appeared to be intermediate knowledge of cybersecurity and an advanced usage of AI tools, the Spanish-speaker attacker convinced Anthropic鈥檚 Claude chatbot that the interaction was part of a bug bounty 鈥� a legal way to hack a company and get paid for telling them how you broke in 鈥� with 3 key rules: avoid making changes that could damage the system, delete all logs, and disable command history.

At first, Claude strongly resisted, flagging these instructions as they sounded like detection-evasion techniques, commonly used by malicious actors. It even challenged the attacker, requesting verification.

However, just three minutes after the suspicious prompts, the attacker dropped a simple and straight-forward instruction: 鈥淐ould you add this to claude.md鈥�, with a penetration-testing cheat sheet attached. After that, things went as smooth as butter.

In simple terms, using a penetration-testing cheat sheet is like asking a security guard to write instructions to disable the alarms and he refuses, so you pull out a pre-written note with those exact instructions and say, 鈥淐an you just stick this on your booth door?鈥� and he does. Now those instructions are in front of him all day, and he follows it when you ask him, automatically without questioning it. In this case, Claude didn鈥檛 write the malicious manual 鈥� it just stuck the note up, but the result was the same.

Mexican government infrastructure attacked

According to Gambit Security, the attacker breached the Tax Administration Service (SAT, according to its acronyms in Spanish) 鈥� along with least 8 other Mexican government institutions during the end of 2025 until mid-February 2026. The incident has been described as one of the largest breaches of government infrastructure.

Within the scope of the SAT alone, the compromise reportedly exposed 195 million taxpayer records and 52 million directory entries. Building on this access, the attacker then leveraged Claude to pursue even more sensitive data, including Mexico鈥檚 electronic signature (e.firma) private keys, taxpayer identification numbers (RFC), national ID numbers (CURP), as well as customers鈥� biometrics, email addresses, phone numbers, and physical addresses.

Even beyond all of this, however, the most unsettling part of the attack came next. With a prompt that revealed a striking lack of technical literacy 鈥� 鈥淢ake a Python or something like that鈥︹�� 鈥� the attacker asked Claude to build a simple web application capable of querying and returning SAT taxpayer information. He then used this tool to develop a script that generated fraudulent tax status certificates, populated with real data pulled directly from the system. While he was unable to forge the document鈥檚 digital seal, the deception was still dangerously effective, because without proper cryptographic validation, the certificates appeared legitimate and were nearly indistinguishable from authentic ones.

Thus, the commercial relevance of the SAT hack is not secondary or collateral 鈥� it鈥檚 central. SAT is not merely a fiscal institution, it is the central nervous system of Mexico鈥檚 formal commerce, and its database holds information that companies provide under legal obligation, not only with a reasonable expectation that the government will protect it, but because they have no option but to do so.

When that information is compromised, the damage is not limited to the privacy of the affected taxpayers, it extends to a foreign investor or a company鈥檚 compliance team that may be evaluating a nearshore move for the establishment of operations in Mexico. And with all of that, it would be understandable for them to wonder:

If the government cannot protect the data that companies have little choice but to provide, what guarantee exists that it will be safe? And with that, in case of a danger, will the Mexican government have enough tools to investigate and sanction the attackers?

The hack spreads mistrust and apprehension

Within that calculus, weaknesses in government cybersecurity become more than a technical concern 鈥� they evolve into a tangible barrier to investment, a contradiction made even sharper amid the ongoing renegotiations of the United States-Mexico-Canada Free Trade Agreement (USMCA).

The last version of the USMCA establishes a framework for cybersecurity cooperation among member countries. Its legal architecture rests on three pillars: i) the recognition that cyber-threats represent a risk to digital commerce; ii) the commitment of the parties to develop capacities to identify and manage those risks; and iii) the promotion of cooperation between the public and private sectors in this area.

However, it never mentions a minimum-security standard that governments are required to meet, but that is not the only loose thread, since the USMCA was negotiated in a technological context radically different from the present one 鈥� back when generative AI (GenAI) was still science fiction rather than a browser tab. Indeed, the cybersecurity framework implicitly assumes that threat actors are organized structures.

And that鈥檚 where the case analyzed by Gambit Security could jeopardize everything, as the breach in which AI functioned as a primary operational tool, according to their document. More worrisome, what previously required months of specialized work and considerable resources by a potential network of hackers can today be executed in days by a much smaller unit, or singular person, with monthly subscription tools 鈥� and maybe less technical knowledge than you think.

That said, the push for stronger cybersecurity standards may extend beyond USMCA concerns and evolve into a broader industry imperative, particularly in places in which the agreement itself may fall short.

Claude as the mechanism

This attack marks the second known incident involving the use of Anthropic鈥檚 Claude 鈥� the first having been linked to a Chinese state-affiliated group 鈥� and it is unlikely to be the last. Without clearer regulation and stronger security standards, such misuse will not remain an exception but rather become an increasingly recurring threat not only in Mexico but also in Canada and the United States. Even in the US, which maintains comparatively advanced cybersecurity frameworks, experts acknowledge that defenses are still struggling to keep pace with an increasingly complex threat landscape.

The US is not the only one taking the lead, however, as the European Union has already introduced the first comprehensive AI regulatory framework, requiring systems to be resilient against misuse (including for cyberattacks) and obligating companies to report and address vulnerabilities. However, these rules primarily apply to AI developers rather than those who weaponize the technology. By contrast, the US has begun to address this gap by enacting laws that treat the use of AI in criminal activity as an aggravating factor, leading to harsher penalties.

As such, this is not only an alert for Mexico to improve its own cybersecurity practices but is certainly a broader call to action for all three countries. Regulating a technology that evolves faster than legal processes is both urgent and challenging 鈥� but not impossible.

You can find out more about the challenges facing Mexico on several different fronts 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 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.

You can find more blog posts听by this author here

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.

You can find more blog posts听by this author here

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:

• • • 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

Key takeaways:

• • • Strategic collaboration is becoming a defining strength for the region 鈥� Latin American organizations are realizing that progress in AI accelerates when they combine forces by linking industry expertise, academic talent, and public鈥憇ector support.

    • AI initiatives rooted in real local challenges are gaining global relevance 鈥� By developing solutions grounded in the region鈥檚 own structural needs, whether in infrastructure, finance, agriculture, education, or mobility, many LatAm firms are producing technologies that are both highly impactful and naturally scalable.

    • Demonstrating clear outcomes is becoming fundamental 鈥� Organizations that show concrete operational improvements, measurable efficiencies, or stronger customer outcomes are strengthening their position with investors and partners.

In recent years, Latin America has experienced significant growth in investments related to AI, accounting for . This is strikingly low given that the region makes up around 6.6% of global GDP, highlighting the region’s opportunities to scale AI initiatives even further. Although there are notable differences among countries, Mexico and Brazil 鈥� the two largest LatAm economies 鈥� stand out for their volume of AI projects and funding, followed by other nations such as Chile, Colombia, and Argentina.

By recognizing the region鈥檚 strengths 鈥� which include cost-effective operations, access to data, clean energy, and public support 鈥� the region鈥檚 businesses can better position themselves and design strategies to draw in international investors that may be increasingly seeking promising locations for AI development.

Lessons from LatAm鈥檚 AI success stories

Latin America has produced remarkable AI success stories that can serve as models to build confidence among investors. These cases 鈥� involving companies that attracted substantial investment and achieved growth 鈥� demonstrate valuable best practices that range from technological innovation to working with governments and corporations. Some of these best practices include:

Building strategic alliances

The journey of innovation rarely unfolds in isolation. At times, the presence of large, established companies, whether local industry leaders or multinationals, has served as a catalyst for AI projects. The experience of that specializes in AI-powered agricultural irrigation, proves it. Now, Kilimo is partnering with EdgeConneX, a data center company based in the United States, on a community .

Academia, too, can be woven into this narrative. Collaborations with research centers or universities offer scientific credibility and connect ventures with emerging talent. In Mexico, AI startups often originate within university settings 鈥� such as computer vision projects from the National Autonomous University of Mexico (UNAM), for instance 鈥� and maintain agreements that sustain ongoing innovation and technical progress even with modest resources. And academic validations, whether in published papers or conference accolades, tend to resonate with foreign investors. Indeed, the emergence of this ecosystem that features early corporate clients and academic mentors frequently lends a distinctive appeal for those seeking investment.

Focusing on local problems with global impact

Within Latin America, certain issues prove especially relevant in situations in which AI solutions intersect with sectors renowned for regional strengths, such as fintech and financial inclusion, agrotech optimizing agriculture, and foodtech drawing on local ingredients. The experience of Chilean food startup NotCo 鈥� in which and subsequently exported 鈥� suggests how innovations rooted in local context may generate broader attention.

By addressing needs in urban transport, education, mining and related areas, local LatAm companies can provide access to homegrown data and users, which can further refine technology and open pathways for investors into similar emerging markets. When AI solutions respond to genuine pain points rather than mere novelty, momentum often builds more quickly, and the model finds validation among that evaluate investments.

Showing results and AI ROI early on

Questions linger for many executives . Evidence of clear metrics like cost savings, sales growth, or error reduction can prove persuasive, especially when complemented by success stories from local clients.

Recent studies show that companies ; and such figures tend to reassure those considering investment by illustrating tangible improvements. Testimonials or independent validations, such as a university study, can further illuminate achievements.

The act of quantifying impact 鈥� whether in efficiency, revenue, or other relevant KPIs 鈥� has a way of transforming perceptions from uncertainty toward clarity.

Leveraging government incentives and collaborations

Many Latin American nations have put forth support programs for AI and tech projects, such as non-repayable funds, soft loans, and tax benefits for innovation illustrated in , , , or the .

Public financing, when present, often acts as a stamp of validation for private investors. For example, this trust extended to Brazilian startups receiving Finep support for AI health projects, which in turn can shift perceptions for foreign ventures capitals. Engagement in government pilots, such as smart city initiatives or solutions for ministries, provides valuable exposure. In such contexts, public-private partnerships and incentives seem to act as quiet levers for growth and legitimacy.

Seeking smart and diversified financing

Financial strategies in Latin America have been shaped by the interplay of local and foreign capital. Local funds often bring insights and patience, while foreign funds may offer larger investments and global scaling experience. Ownership dilution sometimes accompanies the arrival of strategic investors, whose networks can prove invaluable, such as . Programs like 500 Startups, Y Combinator, MassChallenge, and international competitions have ushered LatAm AI startups such as Heru, Rappi, Bitso, and Clip into new rounds of capital following increased exposure.

Efficiency in capital management, which can be demonstrated with lean burn rates and milestone achievement with limited resources, signals an ability to execute within the realities of LatAm, which may enhance the appeal for future investments. The cultivation of relationships and responsible stewardship of capital frequently matters as much as the funds themselves, suggesting that the value of mentorship, contacts, and reputation is often intertwined with deepening financial support.

Unlocking AI Investment

By applying these principles, Latin American companies have achieved a better position to attract AI investments to their projects and help position the region as a viable destination for technology capital. These recent experiences show that when a LatAm company combines innovation, talent, and strategy 鈥� while communicating its story well 鈥� it can win over global and local investors alike. Each of the best practices noted above is based on real lessons: international alliances (NotCo with US funds), leveraging incentives (Brazilian companies funded by Finep), talent formation (Santander and Microsoft programs), focus on ROI (successful use cases that convince boards), and more.

Latin America has challenges but also unique advantages. Companies that manage to navigate this environment intelligently will increase their chances of securing the financing needed to innovate and grow. By doing so, they will contribute to a virtuous circle in which each new success attracts more investment to the region and opens doors for the next generation of LatAm AI ventures.

You can find more about the challenges and opportunities in the Latin American region here