Key findings:

• • • Workloads grow, while staffing stays flat 鈥� Many government legal department professionals say their work keeps increasing while staffing remains stagnant; and many are turning to AI tools to improve capacity.

    • AI adoption is surging 鈥� Over the past year, AI adoption among government legal departments has spread rapidly, with federal and state agencies leading the way.

    • Unfortunately, AI oversight hasn鈥檛 surged 鈥� Many legal departments report that their AI governance is lagging behind adoption, with 20% of agencies having no AI use policy in place at all.

Government legal departments are facing an all-too-familiar problem: more work, more complexity, and the same number of staff to do the job, according to the 成人VR视频 Institute鈥檚 2026 Government Legal Department Report, which captures the insights from 200 government legal department professionals at varying levels.

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2026 Government Legal Department Report

Threaded through these insights, some clear trends emerged. For example, technology 鈥� especially AI and other advanced tools 鈥� is increasingly serving as an extension of staff, expanding agencies鈥� capacity to manage rising workflow demands.

Increasing pressures across all levels

More than one-third of respondents report that their workload increased by more than 10% in the past year, with many handling between 21 and 50 legal matters per week. At the same time, workloads are becoming more complex, with more than one-third of respondents saying that more than half of the legal issues they face are complex, which is particularly notable at the state and federal levels.

Staffing shortages, a top concern in recent years, continue to persist. Three-quarters of respondents say their agencies experienced staffing shortages over the past two years, and almost two-thirds say they anticipate shortages into 2027.

Indeed, despite an increase in complexity and workload, attorney staffing levels have stayed the same for almost 40% of agencies, the report shows. And at the federal and state level, departments were more likely to have experienced a reduction of more than 10% of their staff.

AI adoption skyrockets, making governance more necessary than ever

More than one-quarter of respondents say their agency or department is now using AI tools, up from a meager 5% last year, with this increase taking hold at the federal and state level much more quickly. Among the different groups of respondents, one-third of federal and state government legal professionals report using AI tools compared to just 19% of those at county and city departments. Resistance to AI is diminishing, too; however, more than one-third of county and city legal departments still report having no plans to use AI.

Optimism toward AI is rising alongside implementation, the report shows. More individuals at the federal and state level feel optimistic than pessimistic about AI technology, which is an inversion of last year鈥檚 sentiment. Among county and city legal professionals, pessimism still remains more common. Among all respondents, confidential data exposure remains the top evaluation criterion when assessing these advanced tools.

The report underscores that this all points to a need for the establishment of strong governance models before adoption. Nearly two-thirds of government agencies and departments have an AI use policy in place or are developing one, respondents say. However, 1-in-5 departments and agencies are still without an AI use policy, risking unofficial use of prohibited AI tools.

Those agencies hesitant to implement AI technology are encouraged to view AI technology as a way to increase staff capacity amid flat staffing, rising workloads, and growing matter complexity. AI tools can help reduce strain on employees, contributing to better-managed workloads while reducing employee burnout. When appropriately vetted, however, AI technologies can reduce administrative burdens, increase legal research efficiency, and help those organizations facing trying to manage more work with the same staffing levels.

An actionable path forward

As the report makes clear, AI is no longer a future challenge; rather, it鈥檚 a present reality in a rising percentage of government legal departments. Indeed, the report outlines ways departments and agencies can move forward in this space, by beginning with lower-risk foundational tools like legal research and case management systems; and then investing time in developing thoughtful AI use policies and evaluation protocols. With responsible staff training and a thoughtful evaluation process, AI technologies can protect the valuable time and work-life balance of government legal professionals.

Increasing workloads are not optional for government legal departments, but how department leaders empower their staff to manage these workloads is becoming the differentiator.

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a full copy of the 成人VR视频 Institute’s “2026 Government Legal Department Report” by filling out the form below:

Key highlights:

• • • AI can help remove interpersonal friction during feedback conversations 鈥� Attorneys have taken naturally to soliciting substantive critique from AI that covers tone, argument strength, and clarity in ways they might hesitate to request from a colleague or supervisor.

    • The role of feedback-giver remains irreplaceable 鈥� The developmental core of feedback still requires someone who knows both the work and the person receiving the feedback, even if AI is used as a sounding board.

    • Feedback culture can be accelerated using human-AI mentorship 鈥� AI makes the conditions for receiving feedback easier and can spark shared human conversations about the work rather than replacing those conversations.

It鈥檚 one of those things upon which most people on any legal team would agree 鈥� feedback matters. It鈥檚 essential to professional development, to mentorship, and to the long-term growth of attorneys at every level. And yet, for all the emphasis placed on its importance, feedback remains one of the more nuanced and inconsistently experienced dimensions of working in the legal profession.

At my firm, Seyfarth Shaw, in particular, there has long been a deliberate focus on building high-performing teams and developing legal talent in a way that is both structured and intentional. We have designed systems that measure and reinforce feedback because we believe it is central to how attorneys grow and how high-performing teams consistently deliver exceptional client service.

Within the legal profession, however, there are dynamics and circumstances that can often make feedback more complex to navigate in practice.

At its core, a lot of feedback still relies on the comfort level of both supervising and junior attorneys to proactively provide it and seek it out, and that is often easier said than done. Time pressures, the structure of legal teams, and the challenge of balancing candor with constructiveness all contribute to making this an often-difficult task.

One of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them 鈥� not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact.

Attorneys are trained, as an actual skill set, to question everything and that can naturally extend to how feedback is processed, depending on how it is delivered and by whom. The amount of effort, intelligence, and judgment that goes into legal work is significant, so when that work is challenged, it can feel personal. And on the receiving end, actively seeking out substantive feedback is not a muscle that gets consistently developed in most educational settings leading up to working within a law firm.

The behavioral shift

This is the backdrop against which something genuinely interesting started happening when generative AI entered the picture. Almost immediately, one of the most common use cases we began seeing was attorneys taking their own drafts and asking AI to critique them 鈥� not just for grammar or formatting, but for more substantive reasons such as tone, argument strength, clarity, and impact. Traditionally, this is the kind of feedback many of these same professionals might hesitate to request directly from a colleague or supervisor.

What struck me was how quickly and naturally asking this of AI became a default behavior. There was an almost instinctive willingness to let AI review and analyze work product in a way that felt qualitatively different from how feedback had traditionally been experienced in interpersonal settings.

Landmark, meta-analysis research on found that feedback interventions actually decreased performance roughly one-third of the time, particularly when that feedback shifts attention to the self and triggers anxiety rather than a renewed focus on the work. More identify three triggers that cause people to reject feedback, including the relationship trigger, in which the reaction is not to the substance but rather to the person delivering it.

And 鈥檚 Dr. Larry Richard鈥檚 adds a profession-specific dimension: Lawyers tend to score lower on resilience, defined as how one reacts to criticism or rejection, while also ranking high in skepticism, an instinct to question assertions rather than accept them at face value. The combination can make feedback both more essential and, at times, more complex to deliver effectively.

Using AI neutralizes many of these dynamics 鈥� there is far less perceived pressure, no interpersonal dynamic to navigate, and no relationship to manage in the moment. We are constantly giving AI feedback about what it did right, what it did wrong, and how to improve. Indeed, we often are already flexing that muscle without thinking twice about it.

What the paradox reveals

This is the part to which I keep coming back. The problem was never that lawyers cannot handle feedback; if that were true, they would not be seeking it so readily from AI. The opportunity lies more in the conditions under which feedback is delivered and received. AI did not make people more open to feedback, rather it removed some of the perceived barriers that can accompany feedback in traditional settings.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better.

However, removing that friction is not the same as providing what lawyers actually need to grow. AI can tell you that your argument has a structural gap, but it cannot tell you why that gap matters in the context of a particular client relationship, a judge鈥檚 known preferences, or the broader strategy of a case. It cannot replace the judgment that comes from a supervising attorney explaining not just what to change, but why it matters to change it, and how to think about it differently next time. The developmental core of feedback still requires a person who knows the work, knows you, and is invested in your growth.

What AI has done, perhaps unintentionally, is create a clearer line of sight into how feedback could work even better. It has identified behaviors 鈥� such as seeking input early, iterating quickly, engaging with critique 鈥� that firms like ours have long been working to encourage, and made such behaviors easier to access in the flow of work.

Yet, there is also something more subtle happening. With AI, attorneys retain a clear sense of autonomy over the feedback itself. They can take it or leave it, focus on it, or set it aside without any interpersonal consequence.

AI creates a different dynamic: It functions more as a sounding board, essentially another set of eyes on the work. That makes it easier to engage with feedback in a more exploratory way by incorporating what resonates, questioning what is unclear, and testing ideas before bringing them back into a human conversation. In that sense, AI can help build the muscle not just of receiving feedback, but of engaging with it more thoughtfully 鈥� including developing the confidence to ask the 鈥渨hy鈥� that is often where the real learning happens.

At Seyfarth, this is where we see a meaningful opportunity to build on an already strong foundation. Our focus has long been on creating a culture in which feedback is expected, measured, and part of how work improves. What AI allows us to do is take that a step further, making feedback more continuous, more immediate, and easier to engage with as part of the workflow.

In the next part of this series, we will look at what we call SEYmultaneous Advancement, our way of intentionally bringing AI into the feedback process 鈥� not as a replacement for human interaction but as a progression 鈥� and in true AI form, iterating with the human-in-the-loop along the way.

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Key insights:

• • • Successful AI implementation requires a solid foundation 鈥� Strategy, leadership, and the impact on operations and individual users are key elements to any successful implementation.

    • AI success is a skills strategy, not a technology strategy 鈥� AI creates a whole new set of skills that are required for both legal department attorneys and department leadership.

    • AI changes how legal work is conducted 鈥� If implemented correctly, AI not only improves the end work product, but it also changes how lawyers perform their jobs.

Corporate legal departments are already experiencing the benefits of AI, including improved productivity, and reduced costs and errors, the 成人VR视频 Institute鈥檚 recent shows. So it鈥檚 not surprising that AI is increasingly becoming a strategic priority for general counsel (GCs).

The report cautioned, however, that success with AI is not a given. AI is not a silver bullet which guarantees improvements across the department. Instead, AI adoption and implementation must be carefully planned in order to realize those benefits.

Crucially, successful AI implementation is not simply about the technology; rather, it鈥檚 a reflection of the department itself and often can signal whether the department has the right elements in place to enable that success.

AI enhances successful legal departments 鈥� it does not create them

AI implementation is like any other law department strategy 鈥� it does not live on its own but instead advances as a direct result of everything that has come before it, including the work of the department鈥檚 attorneys and professionals, its daily operations and processes, and the GCs who are guiding the overall vision.

Overall, it鈥檚 about having a solid foundation upon which to build AI adoption and implementation.

The Pyramid of AI Success

AI may be one of the most impactful and transformational technologies to come on the scene in recent years, but it鈥檚 important to remember that it is still simply one tool among many. And its ultimate success will be determined not only by its capabilities, but by how it integrates with and augments the work that corporate legal department attorneys perform daily.

The technology itself does not perform the work 鈥� it enables more efficient work. This means that the rise of AI creates a whole new set of necessary skills for both legal department attorneys and department leadership.

With that in mind, GCs should focus on a few key areas to improve their department鈥檚 chances of AI success. The essential steps can be viewed as a pyramid 鈥� every step that you take builds, each upon another, creating a solid foundation. Establishing a top-level AI strategy means setting the tone from leadership, which then permeates down through operations and ultimately transforms how individual users work every day.

• • Learning 鈥� Most departments have a basic AI understanding and a culture to encourage change, but they often do not have the depth of understanding to move from AI literacy to AI fluency. Be sure to determine where your team is on this learning curve.
  • Empowerment 鈥� Empowering your professionals is crucial to drive experimentation and identify new use cases. Ask yourself, does my team feel encouraged to explore new ways of working and empowered to make changes?
  • Ownership 鈥� The legal team should feel they have significant input into how AI will be used in the department and throughout the organization. AI can be a major transition, and team members should feel that they can freely share ideas, concerns, and insights.
  • Accountability 鈥� Team members with personal goals that are linked to AI adoption are more likely to become top learners and regular users, our research shows, and this leads to greater overall benefits for the department.
  • Usage 鈥� Regular use drives adoption, so you should build AI into your team鈥檚 daily habits, monitor how many legal team members have tried AI, and how many are using it regularly.
  • Expectations 鈥� Balance encouraging uptake with clear expectations around adoption. Offering open encouragement along with access to tools and training to build momentum can be key first steps. As team members become more proficient, set formal expectations around AI usage. Be clear that when targets are set, usage will be tracked and individuals will be held accountable. Then, follow up with low- or no-usage individuals to determine causes, such as difficulty with training.

For GCs, today鈥檚 top challenge is how the department can develop needed AI skills in a way that will best augment how lawyers work. If implemented correctly, AI will not only improve the end work product, but it will also better enable lawyers to perform the work they do best.

AI success with outside counsel

The same principles of strategy and leadership that contribute to AI success within the department also extend to working with outside counsel. Currently, more than half of corporate counsel say they believe their outside law firms should be using AI, according to the report; however, two-thirds also say they do not know how their outside firms are approaching their use of AI.

This creates a communication gap, in which some GCs attribute to hesitance or caution. 鈥淲e do not ask and they are shy to provide answers because they are already under a lot of pressure because their rates are so high,鈥� reports one GC.

About three-quarters of corporate counsel also say they expect their outside law firms to take the lead in AI conversations between the department and the firm. However, that does not mean that GCs should simply accept a lack of conversation if firms are not forthcoming. Those GCs that want their outside firms to embrace AI should be open and transparent, conveying that they believe AI can assist firms with most work tasks, while placing a strong emphasis on output verification and the authority of attorney expertise. Indeed, GCs need to understand how their outside firms are using AI, especially how and when it is being applied, how it鈥檚 being supervised, and, perhaps most importantly, how it impacts fees.

Without detailed and regular discussions, GCs could develop a blind spot in this area. 鈥淐onversation has been only high level,鈥� another GC explains. 鈥淲e generally know what AI they are using but not how they are using it.鈥� What鈥檚 surprising, the GC adds, is that 鈥渢he billing has remained the same as it did before 鈥� so either they are not using AI tools efficiently, or they are just doing double work.鈥�

You can download a fully copy of the , from the 成人VR视频 Institute here

Key highlights:

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

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

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

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

Establishing a visible AI strategy

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

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

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

The quiet accumulation of human costs

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

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

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

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

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

Recommended actions for employers and professionals

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

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

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

For employers:

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

For professionals:

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

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

You can explore the full听

Key takeaways:

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

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

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

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

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

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

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

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

A good question, poorly tested

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

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

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

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

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

I should know. I鈥檓 one of its murderers.

The most templated genre in America

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

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

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

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

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

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

The template always comes first

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

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

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

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

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

Threading the needle

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

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

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

In this determination, three things matter most:

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

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

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

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

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

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

The old traps didn’t disappear

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

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

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

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

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

Key insights:

• • • AI is now a common facet of the legal landscape 鈥� AI is increasingly a part of legal workflows across aspects of the practice, involving not only work matters, but also interactions with clients, opposing counsel, and the courts.

• • • Foundational understanding of AI in legal is crucial鈥� The guide provides concise, practical information that lawyers and legal professionals can use to get a better grasp on AI use in legal practice

• • • Guidance needed in a fast-changing environment 鈥� AI technology and its uses, its limitations, and lawyers鈥� professional responsibilities in the practice of law are evolving rapidly 鈥� and this guide provides needed guidance and help in navigating today鈥檚 environment.

AI is influencing virtually every corner of the legal profession, impacting how legal research is conducted, documents are drafted, discovery is handled, client expectations are managed, and how courts are addressing questions of professional responsibility. Whether lawyers themselves are using AI or not, they are likely to at least be on the receiving end of AI-assisted work product from opposing counsel or clients.

To help bring clarity to this rapidly changing legal arena, the 成人VR视频 Institute and the have released the 鈥� a resource for lawyers and legal professionals who want to approach AI with clarity, confidence, and professional rigor. This 鈥淔oundational Overview鈥� is the first installment of the 鈥淎I Guideline Series鈥� being published by 成人VR视频 Institute and ILTA, with additional guides to be published within coming months.

You can also access the newly published

For AI-enabled lawyers to be the most effective, it鈥檚 important that they first understand the complex legal and technical terminology related to AI, as well as the different categories of AI, within which legal practice these technologies best fit, and the professional responsibilities that accompany their use.

Practical, concise overviews

The AI Guide is a resource for establishing a solid foundation by using the most current information in this fast-moving environment. Written with contributions from a variety of leading attorneys, legal scholars, and legal technologists, the Guide offers lawyers a practical orientation to today鈥檚 AI landscape and the issues that matter most for their legal practice.

The Guide contains concise overviews on:

• • • the current state of AI adoption across the legal profession
    • essential AI terminology
    • the major categories of AI technologies and platforms
    • the situations in which AI is often used to support legal work
    • the ethical and professional responsibility considerations that lawyers must understand, and
    • the emerging trends likely to shape AI use in legal in the years ahead.

The Guide also offers a collection of additional resources for more in-depth exploration.

As AI shows itself to be remarkably effective at assisting with many routine, time-consuming, and information-intensive legal tasks, it also continues to require careful human judgment, verification, and oversight to be most effective. That鈥檚 why understanding AI鈥檚 strengths and its limitations is becoming an essential professional skill.

A different way of interacting with information

Unlike previous technologies, AI is not simply another software application. It is a fundamentally different way of interacting with information 鈥� one that鈥檚 capable of generating analysis, drafting documents, identifying patterns, and assisting with increasingly sophisticated legal work.

AI鈥檚 application within the legal profession brings forward unique, specific considerations. It also raises questions 鈥� as well as answers that are still evolving around accuracy, trustworthiness, ethics, professional responsibility, and many other issues.

Today, these are no longer theoretical discussions; rather, they鈥檙e practical questions that lawyers are confronting every day, regardless of whether those lawyers are currently using AI in their workflows.

The 鈥�AI Guide for Legal Professionals: A Foundational Overview鈥� can give lawyers a foundational understanding on how they and other legal professionals can integrate AI into their legal practice, better understand their responsibilities, and critically evaluate new AI technologies as they evolve.

You can access the newly published

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 highlights:

• • • AI governance is hard to prove in practice 鈥� While our research shows that 44% of companies publish an AI strategy, 76% of those same companies show no evidence of having policies to evaluate the quality of data used to train AI systems.

    • Workers are being left under-prepared and under-protected 鈥� Only 14% of companies have policies to mitigate the negative impacts of AI on workers, and only 31% offer any reskilling or training programs around adapting to an AI-integrated workplace.

    • Human rights and ethics appear an afterthought in AI governance 鈥� Almost three-quarters (72%) of companies conduct no AI impact assessments, and less than 1 in 10 companies conduct ethical or human rights assessments.

There is a widening chasm at the heart of corporate AI governance, according to a new report, , published by the 成人VR视频 Foundation and the United Nations Educational, Scientific and Cultural Organization (UNESCO).

The Foundation鈥檚 analyzed publicly available information from nearly 3,000 companies across 11 industry sectors, creating the most comprehensive picture yet of how organizations are managing AI.

Beneath the surface of corporate AI governance mechanisms, divergence between the speed of AI adoption and meaningful human oversight is growing. The report’s findings make clear that this is no longer a gap that organizations can afford to ignore, especially when backlash against is growing and are solidifying among consumers in the United States.

Data highlights the illusion of AI governance

Businesses of different sizes and across multiple sectors are adopting AI technology at a rapid pace. When governance exists only in the wording of a strategy or company vision, however, the people most affected by AI systems 鈥� workers, consumers, and communities 鈥� are left vulnerable. According to the report:

• • • 44% of companies publicly communicate having an AI strategy. However, a gap in AI governance is evident as more than three-quarters of those companies (76%) do not seem to have policies to evaluate the quality of data used to train AI systems.
    • 40% of companies report board- or committee-level oversight of AI. At the same time, strategic signals do not necessarily indicate operational capacity or day-to-day governance. In fact, less than one-third of all sampled companies claim to have an additional team or resource dedicated to AI governance. Moreover, limited information is publicly disclosed on the teams, processes, and accountability mechanisms that translate intent into action.

Workers are being left behind

Research by the International Monetary Fund finds almost , highlighting the acute nature of concerns about job displacement and declining opportunities for some groups. Without sufficient oversight, AI can threaten workers’ rights, amplify bias, and increase surveillance and work intensity, which can enable inhumane decision-making at scale.

The TR Foundation/UNESCO report notes that many companies are adopting AI without the safeguards needed to support workers and help them to adapt to the changes this technology brings. Less than one-third of companies were shown to offer training and reskilling programs for employees who may be adapting to an AI-integrated workplace. Even within the 31% of organizations in which these training programs exist, there is a vast variation in the scope and depth of the training offered.

In fact, many company training programs are not enterprise-wide or structured. Instead, they are ad-hoc or limited to leadership roles. This lack of investment in talent risks undermining the significant investment that companies are making in AI.

Despite growing pressure from regulators, policymakers and social justice campaigners, the ethical impact of AI appears poorly governed, with companies sharing limited information publicly.

The picture on worker protections is equally concerning. Only 14% of companies have public policies in place to mitigate the negative impacts of AI systems on workers, the report shows. This means the majority of companies either have no policies in place or do not publicly communicate them.

What is more troubling is that when workers experience harm, there is almost nowhere for them to turn. Only 2% of companies indicated they had a complaints mechanism 鈥� a critical early warning system for potential concerns. The findings suggest many organizations lack a mechanism for AI-related internal complaints beyond the broad generic complaint channel, and this is compounded by low awareness of the areas in which AI systems may infringe employees’ rights and protections.

Ethics and human dignity as an afterthought

Despite growing pressure from regulators, policymakers and social justice campaigners, the ethical impact of AI appears poorly governed, with companies sharing limited information publicly.

Human rights and ethical use of AI are treated as secondary considerations to compliance, according to our research. The majority of companies (72%) do not conduct any impact assessment with regard to AI. Only 7% publicly communicate conducting a fundamental or human rights impact assessment, and just 5% report conducting an ethical impact assessment.

Among those companies conducting some form of impact assessment, the focus skews sharply toward compliance rather than people. The most prevalent assessments are privacy or compliance-focused, with 18% of those companies that conduct some form of impact assessment reporting that they conducted a data protection impact assessment, and 14% reporting they conducted a privacy impact assessment.

How to center people in AI governance

Closing this governance gap is essential for companies in order to adopt AI responsibly and avoid costly legal, ethical operational, talent-related risks.

To support companies in navigating this challenge, offers a free survey to help companies map the areas in which AI is used across products, operations and services, and then benchmark those against peers their sector.

The report also contains case studies from companies that voluntarily shared their responsible practices with us. For example, German software company SAP intentionally designs and deploys its internal AI systems with a human-in-the-loop in which AI automates repetitive tasks and supports decision-making while final judgment and complex problem-solving remain firmly in the hands of employees.

As AI becomes part of core business infrastructure, companies must move beyond statements of intent and toward measurable AI governance.

In another example, BASF, a German chemical conglomerate, has jointly agreed with its workers’ councils on a general reskilling program that covers technical, hard, and soft skills. Finally, Canadian telecom company TELUS’ Indigenous Advisory Council provides guidance on AI ethics issues that directly affect indigenous communities.

Next steps for companies

The TR Foundation/UNESCO report highlights the most impactful concrete commitments that companies can take now to future proof against AI-related risk, including:

• • • investing in structured, enterprise-wide worker-reskilling programs that measure outcomes, not just participation;
    • establishing enforceable human rights impact assessments as a standard part of AI deployment, not as an optional addition; and
    • creating accessible, AI-specific internal grievance mechanisms so that workers and users have a genuine pathway to raise concerns and seek remedy.

As AI becomes part of core business infrastructure, companies must move beyond statements of intent and toward measurable AI governance. While this data demonstrates clear governance gaps, it also presents an opportunity for companies to take the lead on implementing responsible AI that operates openly in the public interest.

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