Key insights:

• • • Branches too, not just trunks, drive growth 鈥� Across firm segments and US regions, litigation and corporate work still anchor demand, but they are not always the main sources of new hours.

    • Soil conditions vary sharply by region 鈥� The Southwest US and international markets led all regions with 5.2% and 6.1% demand growth, respectively, driven by niche and transactional practices, while the Midwest and Eastern regions grew more modestly.

    • Midsize firms are growing through specialization 鈥� Struggling to compete on volume with the Am Law Second Hundred or on rates with the Am Law 100, Midsize law firms posted 2.6% demand growth powered primarily driven by smaller, specialized practices.

The first quarter of 2026 arrived with law firms still standing on solid ground, although the footing is beginning to feel a little less certain beneath the surface. As the 成人VR视频 Institute鈥檚 recent听Q1 2026 Law Firm Financial Index (LFFI) reported, the score landed at 55 鈥� exactly the historical average since 2006, which is a modest place to be when you consider that Am Law 100 firms pushed worked rate growth to nearly 10%, and overall demand came in at 2.7%, roughly triple the long-run average. Those are not average inputs. Something is absorbing the gains.

The story beneath the headlines is one of diverging strategies, uneven soils, and the quiet question of whether a tree can keep growing by strengthening only its trunk 鈥� or whether it needs to extend its branches.

Reading the soil: Regional demand across the US

Just as trees grow differently depending on the nutrients available in their soil, law firm demand across different regions of the United States reflects the distinct conditions shaping each local market.

The western half of the country set the pace. The Southwest posted the strongest domestic growth at 5.2%, driven not by the dominant practices of litigation or corporate work, but by a constellation of smaller practices (labeled 鈥渙thers鈥�) that collectively delivered the largest single contribution to new hours 鈥� 12 of the 52 additional hours worked per 1,000 compared to Q1 2025. Labor & employment and litigation followed, but the headline is that niche practices 鈥� treated by many firms as secondary concern 鈥� carried the region.

The West grew at 4.8%, with labor & employment as its leading driver, although intellectual property imposed a meaningful drag: Law firms in the West are currently working 27 fewer hours per 1,000 on IP matters than they were a year ago, a loss that鈥檚 partially masking an otherwise healthy broad-based expansion.

International operations led all regions at 6.1% growth, but with an important asterisk. This region, which captures demand generated by US-headquartered firms operating abroad, was recovering from a period of contraction. The surge was powered almost entirely by corporate general and M&A, making it the most transactionally concentrated region in Q1. The flip side 鈥攔eal estate, litigation, and 鈥渙thers鈥� practices all contracted, meaning growth here is reliant on a narrower set of practices than it may appear.

The Eastern and Central regions told a more measured but arguably more durable story. The Midwest grew just 2.0%, but with a notable quality as no practice area contracted. Every discipline contributed at least marginally to new hours worked, with litigation doing the heaviest lifting. The Northeast and Southeast each came in at 2.8%. In the Northeast, growth was similarly broad, with no practice in retreat; while the Southeast offered a small twist as corporate general led for the first time among the regions examined. Litigation followed close behind, and together the two practice areas accounted for 18 of 28 new hours worked. Those two practices 鈥� the trunk of any large firm鈥檚 business 鈥� pulled more relative weight in the Southeast than anywhere else in the country.

The story beneath the headlines is one of diverging strategies, uneven soils, and the quiet question of whether a tree can keep growing by strengthening only its trunk 鈥� or whether it needs to extend its branches.

What stands out across this regional picture is that for most of the US, the new growth is not coming just from the traditional core. Corporate general and litigation remain the largest absolute contributors to demand 鈥� the sturdy trunk 鈥� but in the West and Southwest, it is the branches that are responsible for incremental gains: labor & employment and a diverse mix of smaller practices. In US regions in which the trunk remains the engine 鈥� such as the Midwest, Southeast, and Northeast 鈥� growth is still real but narrower. The more resilient growth stories tend to be the ones in which no single branch bears all the weight.

The tree type matters too: Demand by firm segment

Regional soil explains some of the variation in Q1 demand, but not all of it. The type of firm shapes how growth is structured just as much as geography. And in Q1 2026, the three segments grew in ways that were as different from one another as oaks from aspens.

The Am Law 100 posted demand growth of 1.2%, the lowest of the three segments, but this is consistent with a strategy built primarily on rate power rather than volume. Of the 12 additional hours per 1,000 worked compared to Q1 2025, transactional practices contributed 8 hours, and counter-cyclical practices added 5 among Am Law 100 firms. The one drag came from intellectual property, which contracted by 1 hour. For the largest firms, demand is supplementary to rate growth 鈥� the trunk is wide, and thus, the tree does not need to grow tall to be profitable.

The Am Law Second Hundred grew 4.0%, the strongest demand performance of the three segments, and the composition of that growth is striking. Of 40 new hours per 1,000 worked, counter-cyclical practices 鈥� led by litigation at 15 hours and labor & employment at 7 鈥� contributed 22 hours. Transactional practices added 9. No practice contracted. This is a segment with unusually full canopy coverage: growth is broad, and every branch is pulling upward. The Second Hundred鈥檚 continued 鈥渕oat of demand鈥� in this area remains one of the more durable stories in the legal market.

The most instructive case, however, is the Midsize segment. Midsize firms grew demand 2.6% in Q1, roughly in line with the industry average. However, the source of that growth tells a different story than the numbers suggest. Of 26 new hours per 1,000 worked, the largest contributor was 鈥渙thers鈥� 鈥� a category of smaller, specialized practices 鈥� at 8 hours. Corporate general added 6, real estate and litigation 4 each. No practice contracted.

What that picture reveals is a segment finding its footing not by competing on volume 鈥� where the Second Hundred has structural advantages 鈥� or on rate increases, where the Am Law 100 holds the leverage. Midsize firms appear to be carving out a third path: specialization. The tree is not the tallest, and the trunk is not the thickest, but it is filling out its canopy with branches that larger competitors have left largely unattended.

Growth is in the canopy

As the LFFI showed, Q1 2026 produced broad-based demand growth, but the data is clear on one thing: A healthy trunk is not enough. In some regions, the incremental gains came from practices that many firms still treat as secondary 鈥� labor & employment and a rotating mix of smaller specialties. In most segments, the firms building fuller canopies are outperforming those relying on a narrower set of core practices.

Midsize firms are perhaps the most visible example of a segment adapting to its conditions. Unable to out-volume the Second Hundred or out-price the Am Law 100, they are finding ways to grow through diversification. Whether that strategy can close the widening performance gap against their larger competitors remains to be seen.

However, the Q1 data suggests that for firms at every level, the next phase of growth is likely to come not from further strengthening what is already strong, but from investing in branches that have yet to reach their full height.

You can download a full copy of the 成人VR视频 Institute鈥檚听Q1 2026 Law Firm Financial Index here

Key insights:

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

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

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

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

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

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

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

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

The shift from systems to economic architecture

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

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

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

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

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

Table 1: Legacy architecture versus survival architecture

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

The collapse of compartmentalized operating models

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

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

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

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

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

Table 2: The economics of architectural fragmentation

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

The new core competency: “Federated coherence鈥�

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

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

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

The measurement failure executives must address

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

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

Table 3: Activity metrics versus economic outcome metrics

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

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

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

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

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

Table 4: Characteristics of the adaptive organization

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

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

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Key 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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Key insights:

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

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

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

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

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

Today鈥檚 cloud realities

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

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

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

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

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

Where the model broke

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

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

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

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

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

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

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

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

Figure 1: Cloud accumulation meets enterprise architecture shifts

The data reality beneath the surface

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

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

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

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

Reframing the problem

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

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

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

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

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

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Key takeaways:

• • • Falling behind on worked rates 鈥� Midsize firms grew worked rates by just 5.3% in Q1 2026, roughly half the Am Law 100’s 9.8% growth 鈥� a structural gap that has widened with every passing quarter.

    • Underinvesting in the tools that will define tomorrow 鈥� Midsize firms also invested 6.2% more in tech and knowledge management in the quarter 鈥� the lowest of any segment 鈥� leaving them at risk falling behind as larger peers accelerate their investment.

    • Sitting out the talent race 鈥� With recruiting expense growth at -0.2%, Midsize firms are virtually absent from the lateral market while their closest competitors saw 5.6% growth in their investment.

In Q1 2026, demand growth across all segments landed at 2.7% year-over-year, with 听Midsize firms coming in at 2.6%, essentially in line with the market average and comfortably ahead of the Am Law 100’s 1.2%, according to the 成人VR视频 Institute鈥檚 recent Q1 2026 Law Firm Financial Index (LFFI).

Based on this metric, Midsize firms are not underperforming, as they are capturing work at a pace that outstrips the elite tier; however, a deeper look shows a more nuanced story. The Am Law Second Hundred led all segments with demand growth of 3.9%, posting a notable advantage over the Midsize segment. That growth was enough to make up the ground ceded by the Am Law 100 that the Am Law 200 as a whole still managed to outstrip the Midsize segment in terms of demand growth.

That makes the demand story a very mixed one for Midsize firms. While they are holding their own against the very largest firms, the Am Law Second Hundred 鈥� Midsize鈥檚 most direct competitive set 鈥� is pulling significantly ahead on volume. If that gap persists, it could further shut the gates to demand gains. Of course, that would be made all the more impactful because of how rising demand influences firms鈥� ability to raise rates.

Rates are the most consequential gap in the data

If demand tells a moderately positive story for Midsize, worked rate growth is the point at which the data turns slightly more negative for the segment. In Q1 2026, Am Law 100 firms posted worked rate growth of 9.8%, the highest of any segment by a significant margin. The Am Law Second Hundred recorded 6.9% growth, while the overall market average was 7.0%. Midsize firms, meanwhile, came in at 5.3%.

That is a gap of more than 4.5 percentage points between Midsize and Am Law 100 firms, a magnitude outstripping the entirety of the Midsize segment鈥檚 demand gains.

What makes this especially significant is that the gap is not new 鈥� one year ago, in Q1 2025, the same hierarchy held, with Am Law 100 firms seeing worked rates grow at 9.4%, Second Hundred firms at 7.1%, and Midsize firms at 5.9%. In other words, the rate divergence between Midsize firms and the rest of the market has been consistent and is widening even further. The end result of this is stark: Midsize firms are growing revenue per hour of work at a pace roughly half that of their Am Law 100 counterparts, and that differential compounds over time into a meaningful profitability disadvantage.

Expenses diverge in the wrong direction

On the expense side of the ledger, the pattern reverses in a way that creates a genuine squeeze for Midsize firms. Looking at direct expenses 鈥� the costs most closely tied to delivering client work 鈥� Midsize firms recorded growth of 5.4% in Q1 2026, the highest of all three segments. This compares to 4.8% for the Am Law 100 and just 4.4% for the Am Law Second Hundred. That means that Midsize firms are generating the slowest rate growth while simultaneously growing their client-delivery costs the fastest. That combination reflects a textbook margin compression dynamic.

Overhead expenses per FTE tell a different story. Here, Midsize firms showed lower growth at 4.0%, well below the Am Law 100’s 6.7% and the Second Hundred’s 5.8%. On the surface this looks like cost discipline, but it is worth reading carefully: lower overhead investment, especially when coupled with the market鈥檚 high tech and talent expenditure pressures may actually reflect forced underinvestment rather than efficiency. Midsize firms may simply have less capacity to expand their infrastructure spending, not less need for it.

Making an opposite bet on talent

Indeed, one of the sharpest contrasts in the “Q1 2026 LFFI ” data involves recruitment expenses. The Am Law Second Hundred is investing heavily in lateral talent, seeing recruitment expense growth of 5.6%. The Am Law 100 has sharply pulled back, growing recruitment costs at just 0.3% 鈥� a signal that the largest law firms may be consolidating their existing talent base rather than expanding it aggressively. Midsize firms sit at the opposite extreme, with recruitment expense growth of -0.2%, essentially flat to slightly negative.

This difference is notable because the Am Law 100 and Midsize segments are pursuing fundamentally different headcount strategies. As Am Law firms focus on leaner headcount powered by rates, Midsize firms have finding much more of their revenue growth comes from growing aggregate hours worked by hiring more lawyers. Midsize firms鈥� decision not to leverage as much investment in this area could signal a shift in strategy, simple cost pressures, or perhaps a greater focus on which areas they spend their recruiting money. Whichever the driver, it鈥檚 a sizeable shift across a segment that鈥檚 already feeling pressure across multiple facets of their business.

The compound effect of this divergence

The “Q1 2026 LFFI” data highlights several reinforcing challenges facing Midsize firms: slowing demand and lagging rate growth, the highest direct expense growth but the lowest technology investment, and minimal lateral recruitment investment. While no single factor is critical, together these divergences show a widening gap between earnings and costs.

Of course, this is not to say that Midsize firms are going bankrupt 鈥� far from it. Midsize firms鈥� profitability, on average, is growing at a solid pace as demand and rates continue to power them forward, even as expenses weigh on their numbers.

What may be more concerning is what this means for the future potential of Midsize firms, especially as the market bifurcation grows and the Am Law firms increasingly pull away. As this continues, it鈥檒l become harder and harder for Midsize firms to break into those ranks, compete for talent, and compete for the kind of bet the company work that is some of the most profitable in the legal industry. Reversing this course isn鈥檛 about Midsize firms鈥� 2026 results; rather, it鈥檚 about what they can achieve in 2030, 2040, and beyond.

You can download a full copy of the 成人VR视频 Institute鈥檚 Q1 2026 Law Firm Financial Index here

Key insights:

• • • Law firms have moved from restricting GenAI use (Don鈥檛 use tools that leak client data) to mandating it (Incorporate AI into your practice and market our firm鈥檚 GenAI capabilities)鈥斕齆either phase has given rank and file lawyers what they really need: Guidance on in which instances GenAI actually helps deliver better, cheaper, and faster legal services, where it introduces serious professional risk, and how to tell the difference.

    • GenAI鈥檚 capacity to transform legal work for the better is real, but so is its capacity to degrade it听鈥擥enAI can significantly boost speed and quality on tasks involving breadth, synthesis, or straightforward analysis, but it can weaken performance on complex judgment and revision tasks 鈥� especially for stronger professionals 鈥� by encouraging overconfidence, missed issues, and superficial reasoning.

    • A use-mode framework can close the听gap鈥� A proposed governance framework can give law firm leadership a practical tool for identifying in which situations GenAI enhances legal work, where it introduces serious risk, and where professional judgment is non-negotiable.

This article synthesizes findings from the author鈥檚 paper,

Your law firm undoubtedly has a policy around generative AI (GenAI), which probably tells lawyers to avoid tools that leak client data, admonishes them to look out for hallucinations, and encourages them to incorporate AI into their practice to satisfy client demands.

However, it likely does not tell them which cognitive functions they should delegate to GenAI, which they should not, and where the line between the two is absolute. In the space between restriction and mandate, lawyers are making consequential decisions about GenAI delegation every day. Meanwhile, most law firms have not addressed that space with meaningful governance.

GenAI can make legal work worse

GenAI鈥檚 capacity to transform legal work for the better is real, but so is its capacity to degrade it. Most law firm leaders know that AI can hallucinate; yet far fewer know that it can make expert legal judgment and work product actively worse.

The best evidence of this dynamic comes from a with consultants from the Boston Consulting Group, who were given similar tasks and allowed to use various levels of AI assistance, including no AI. For professional tasks requiring breadth and option generation, GenAI delivered, showing that output quality improved by 40% and consultants worked faster. For tasks requiring judgment and synthesis, however, something unexpected happened. Consultants using GenAI were 19% less likely to produce correct solutions than those working without it.

Governing GenAI鈥檚 uneven performance requires asking a question that most law firms are not asking: What cognitive function is being delegated to GenAI at each step in the workflow?

The same pattern appears in research evaluating GenAI use in legal analysis. An empirical in the Journal of Legal Education confirmed that AI dramatically improves performance on straightforward analysis while producing no measurable benefit for complex reasoning. And in the case of complex reasoning, GenAI use also introduced recurring failures, such as jumping to conclusions, missing less obvious issues, and generating confident prose that masks superficial analysis.

from the University of Minnesota focused on legal tasks showed that GenAI assistance on a synthesis task improved performance by nearly 60% and produced a surprising downstream benefit. Those participants who used AI for synthesis outperformed the control group on the subsequent independent reasoning task even after GenAI was removed. However, when GenAI was introduced at the revision stage, the picture changed. GenAI helped weaker performers, but it actively degraded the work of stronger ones. Indeed, the best lawyers in the study produced worse revised work product when they used GenAI than when they worked without it.

A use-mode governance framework

Given all these findings, governing GenAI鈥檚 uneven performance requires asking a question that most law firms are not asking. Instead of determining whether GenAI is appropriate for a particular deliverable 鈥� such as a brief, a contract, or a board presentation 鈥� the governance question instead should be: What cognitive function is being delegated to GenAI at each step in the workflow?

My proposed framework, outlined below, organizes common GenAI uses into seven recurring modes following the sequence in which lawyers actually use GenAI to produce legal work product. Then, governance controls are calibrated to the risk profile of each mode.

Modes 1 and 2: Retrieval and organization

At the mechanical end of the cognitive spectrum are two distinct functions. In retrieval mode (Mode 1), a lawyer reviewing a merger agreement asks GenAI to identify every representation and warranty in the document. In organization mode (Mode 2), a litigator reviewing 50 depositions asks GenAI to construct a timeline from the testimony. The first locates material that already exists. The second arranges it into a usable structure. No new content is created in either case, and both uses are low-risk and should be actively encouraged, subject to modest verification controls. Firms that unduly restrict these use modes are leaving value on the table.

Mode 3: Summarization

Summarization (Mode 3) introduces selection risk. In this mode, GenAI chooses what to emphasize, include, and omit. Consider a lawyer preparing a board presentation on the results of an internal investigation. GenAI can condense dozens of witness interviews into key points and themes in minutes; however, a summary may focus on procedural detail while missing credibility issues that a lawyer would immediately recognize as material. The appropriate control is to mandate meaningful review by a lawyer with first-hand knowledge of the source material. A lawyer encountering the summary cold has no reliable way to evaluate what GenAI missed.

Mode 4: Candidate generation

Mode 4 is exploratory. A lawyer drafting a brief might ask GenAI to generate a list of potential arguments, propose alternative framings, or identify supporting authority. This candidate material expands options and accelerates iteration. The work product is not filing-ready and must be treated as provisional. GenAI can suggest, but a lawyer must decide.

The authority verification obligation at this stage deserves special emphasis. GenAI will identify cases, summarize holdings, and weave them into an argument structure. Thus, the output will read fluently and cite real-looking cases. However, a lawyer cannot assume the model has accurately characterized the holdings or context, and any authority cited in an external filing must be independently read and verified. GenAI can help find the cases, but a lawyer must read and apply them.

Mode 5: Editing and rewriting

In Mode 5, a lawyer asks GenAI to tighten a dense contract provision or restructure a wordy paragraph, risking, of course, unintended meaning change. An edit may read cleanly while subtly narrowing a representation, softening a covenant, or eliminating a carve-out. The revision risk is not hypothetical. The University of Minnesota study referenced above found that stronger performers produced worse work product when GenAI revised their independently produced memos. In this mode, a lawyer must confirm that the edit produced no shift in meaning and introduced no new factual assertions.

Mode 6: Critique and stress-testing

Mode 6 may be the most underutilized GenAI capability. Before filing a brief or presenting to regulators, a lawyer can ask GenAI to identify weaknesses in their argument. In this way, GenAI finds vulnerabilities before adversaries do; and unlike every other mode, the risk here runs in one direction. Lawyers who skip this step are missing one of GenAI鈥檚 core value propositions. Law firms鈥� governance frameworks should not merely permit it but actually require it in appropriate cases.

Mode 7: Evaluation and decision

The boundary against AI delegation becomes absolute when GenAI is asked to evaluate or decide. A lawyer advising a board on whether an event requires disclosure cannot delegate that determination to GenAI. A litigator assessing settlement value cannot outsource probability judgments because these are core expressions of professional responsibility. In this mode, GenAI may inform background analysis, but it may not substitute for lawyer judgment in making the call. This is a categorical prohibition 鈥� professional judgment cannot be delegated.

Going forward with GenAI

Law firm leaders who have moved their GenAI policy from restriction to mandate without governing the space between have not finished the job. Their lawyers are making consequential decisions about GenAI use every day without the guidance they need and deserve.

The use-mode framework presented above gives firm leadership a practical tool for filling that gap. It identifies the instances in which GenAI enhances legal work, where it introduces serious risk, and where professional judgment is non-negotiable. Firms that govern at that level will capture GenAI鈥檚 value; and those firms that do not will have policies that look serious but govern nothing important.

The views expressed in this article are solely those of the author in his individual capacity and do not represent the views, positions, or opinions of Foley & Lardner LLP, its partners or clients, or the University of Wisconsin Law School.

Key insights:

• • • AI as a force multiplier 鈥� Advanced analytics now reveal financial and behavioral anomalies that traditional monitoring systems routinely miss, giving executives a clearer view of emerging risks.

    • Geospatial and digital intelligence converge 鈥� Intelligent networks like OSINT, ADINT, and location-based data expose hidden networks and movement patterns, improving the detection of money laundering, trafficking, and smuggling operations.

    • Enterprise risk strategies must evolve 鈥� Organizations that integrate AI-driven intelligence across compliance, security, and operations can respond faster, reduce blind spots, and operate with greater resilience during high-risk events.

Illicit financial activity has always evolved faster than the systems designed to stop it. And today, the speed and sophistication of criminal networks are accelerating in ways that traditional compliance processes can no longer match. Major international events, such as the 2026 FIFA World Cup, bring millions of visitors, heightened commercial activity, and a surge in cross鈥慴order movement, all creating fertile ground for exploitation.

AI as an intelligence multiplier

In this environment, financial institutions are on the front lines of detection and mitigation, and corporations must strengthen their ability to detect hidden risks. AI 鈥� particularly when combined with digital intelligence sources, behavioral analytics, and geo-referenced data 鈥� has emerged as the most powerful accelerator of that transformation.

Among all of this high-volume activity, AI is redefining how institutions detect early-stage indicators of illicit activity. Instead of relying solely on manual reviews or rule-based monitoring, organizations are increasingly deploying systems capable of analyzing vast volumes of structured and unstructured data at once. Three capabilities are shaping this new frontier:

Open-source intelligence (OSINT) 鈥� Criminal activity, even when intentionally concealed, tends to leave trace signals online. OSINT tools can examine social platforms, online marketplaces, media sources, forums, and digital discussion channels to uncover suspicious behavioral patterns, potential recruitment or exploitation signals, inconsistencies between official identification and online presence, or clusters of accounts linked by shared attributes. For many executives, OSINT has become an indispensable layer of enhanced due diligence, risk scoring, and early threat detection long before suspicious activity appears in financial records.

Advertising intelligence (ADINT) 鈥� ADINT focuses on metadata produced by mobile applications and digital advertising ecosystems. While it does not expose personal identifiers, it reveals mobility patterns, device behavior, and clustering anomalies. This type of intelligence becomes particularly powerful during large-scale events because of the ability to monitor the movement of devices across high-risk corridors, identify unusual concentrations of activity near event venues or border regions, or detect digital behavior consistent with organized criminal logistics. ADINT introduces a geographic and behavioral dimension to risk that enables institutions to understand not only who a customer appears to be, but where they go, how they behave, and whether those patterns align with legitimate economic activity.

AI-enhanced investigations 鈥� Modern platforms now merge financial data with OSINT and ADINT inputs and then apply descriptive and generative AI (GenAI) to draw connections that would be impossible to detect manually. These systems can classify digital communications by sentiment or intent, identify unusual financial behavior within seconds, convert large datasets into actionable intelligence summaries, translate and interpret foreign-language content, and map networks through recurring metadata or visual similarity. For decision-makers and organizational stakeholders, this shift represents a dramatic reduction in blind spots and a faster escalation pathway when emerging threats surface.

Why financial institutions and corporations must lead

Human trafficking, migrant smuggling, and money laundering cannot function at scale without the financial system. Even when exploitation occurs offline, profits eventually make their way into the formal economy through remittances, structured cash movements, shell companies, digital wallets, recruitment payments, or short-term rental arrangements.

AI enhanced investigations can help institutions identify subtle but meaningful indicators, such as coached or inconsistent customer responses, accounts linked through shared devices or addresses, rapid deposits followed by immediate withdrawals, purchases that do not correspond to a customer鈥檚 risk profile, payments directed to unverifiable recruiters, unusual patterns of short-term housing across multiple individuals, or transaction flows that follow established exploitation routes.

Illicit financial activity has always evolved faster than the systems designed to stop it. And today, the speed and sophistication of criminal networks are accelerating in ways that traditional compliance processes can no longer match.

All this information already exists inside institutional data today; AI simply makes it visible and usable much more easily and quickly.

While financial institutions are central in detecting illicit finance, companies across multiple sectors face heightened exposure during large events. Hospitality, logistics, transportation, construction, real estate, and digital services all see risk intensifying as demand surges and oversight becomes more complex.

Those senior leaders who responsible for operational continuity should integrate AI-powered monitoring into their internal controls. This can help detect unusual workforce recruitment patterns, unexpected badge or access activity, subcontractor behavior that conflicts with declared operations, repeated presence in high-risk zones, or digital communications that hint at coercive or exploitative conduct.

In the fight against illicit finance, technology is no longer optional. Indeed, it is our most powerful ally.

You can find out more about the fight against illicit finance and money laundering here

Key takeaways:

• • • Country-by-country reporting will only increase in complexity 鈥� Australia’s enhanced Country-by-country reporting (CbCR) requirements 鈥� reconciling taxes accrued against taxes credited 鈥� are a preview of where other high-scrutiny jurisdictions are heading, and companies need to build that explanatory analysis capability now, systematically, rather than scrambling later.

    • There has to be a shared narrative from corporate teams 鈥� The EU鈥檚 public CbCR is a reputational event, not just a filing. So that means tax, communications, and investor relations teams need a shared narrative before the data goes public 鈥� inconsistencies create exposure you do not want to manage reactively.

    • Rethink your filing jurisdiction in light of changes 鈥� If EU filing jurisdiction was chosen at initial implementation and never revisited, look again. Guidance has matured, and a more efficient or better-suited option may now be available.

WASHINGTON, DC 鈥� Among the many pressing topics discussed in detail at the recent , country-by-country reporting (CbCR) and its ability to reshape the corporate tax industry, certainly had its place. Between escalating local jurisdiction requirements, the , and for deeper explanatory disclosures, CbCR has quietly evolved from a transfer pricing filing obligation into something far more strategically consequential.

The floor is just the floor

The creation of the by the Organisation for Economic Co-operation and Development (OECD) was intended as a minimum standard for countries. And now jurisdictions are increasingly layering additional requirements on top of the OECD鈥檚 basic template, resulting in a widening gap between the standard requirements and what tax authorities actually want.

Currently, Australia is the most pointed example. Australian tax authorities are now requiring multinational groups to go beyond the standard CbCR data fields and provide explanatory narratives that reconcile taxes accrued against taxes actually credited. This requires corporate tax departments to bridge the gap between financial statement accruals and their organizations鈥� cash tax positions in a way that is coherent, defensible, and consistent with positions taken elsewhere.

At the TEI event, panelists explained that for tax departments this will carry complex timing differences, deferred tax positions, or significant jurisdictional mismatches between booked and cash taxes. Indeed, this additional layer of scrutiny will need dedicated attention.

The broader signal matters: Australia will not be the last jurisdiction to move in this direction. So that means that tax departments should treat Australia’s approach as a leading indicator of where other high-scrutiny jurisdictions could be heading. Building the capability to produce this kind of explanatory analysis systematically 鈥� rather than scrambling jurisdiction by jurisdiction 鈥� would be the smarter long-term investment for corporate tax teams.

Public CbCR in the EU: The transparency ratchet has turned

For US-based multinationals with significant European operations, the EU’s public CbCR directive has fundamentally changed the calculus. Unlike the confidential tax authority filings most corporate tax departments are accustomed to, the EU鈥檚 public CbCR rules put organizations鈥� jurisdictional profit and tax data into the public domain, making it visible to investors, journalists, civil society groups, and organizations鈥� employees and customers.

The EU framework specifies which entities trigger the reporting obligation and which entity within the group is responsible for making the public filing. That scoping analysis is not always straightforward for complex multinational structures and getting it wrong could present both reputational and legal risk.

Choosing a filing jurisdiction is not purely an administrative decision 鈥� it is a choice that affects the regulatory environment that governs the disclosure, the language requirements, the timing, and the interpretive framework that applies to data.

For US-headquartered groups, the implications extend well beyond Europe. Public CbCR data is now being read alongside US disclosures, reporting on ESG activities, and public narratives about tax governance. Inconsistencies, including those technically explainable, could create unwanted noise about the company. This is clearly another reason why the tax function should partner across the business 鈥� in this case with the communications team 鈥� to make they both are aligned to tell the CbCR story instead of being caught off guard by a journalist or an investor during an earnings call.

Questions that US multinationals should be asking

Fortunately, US multinationals with multiple EU subsidiaries are not required to file public CbCR reports in every EU member state in which they have a presence. Instead, under the EU framework, a qualifying ultimate parent or standalone undertaking can satisfy the public disclosure requirement through a single filing in one EU member state, provided the relevant conditions are met. Germany and the Netherlands have emerged as two of the more popular choices for this consolidated filing approach, given their well-developed regulatory frameworks and the depth of available guidance on what compliant disclosure looks like in practice.

The strategic implication is meaningful. Choosing a filing jurisdiction is not purely an administrative decision 鈥� it is a choice that affects the regulatory environment that governs the disclosure, the language requirements, the timing, and the interpretive framework that applies to data. Corporate tax departments that defaulted to a filing jurisdiction early in the EU implementation process should take a fresh look. Regulatory guidance has matured significantly, and there may be a more efficient or better-suited path available than the one originally chosen.

The uncomfortable divergence

There is a notable irony in the current environment. Domestically, the IRS and U.S. Treasury’s 2025-2026 Priority Guidance Plan reflects an explicit focus on deregulation and burden reduction, detailing dozens of projects aimed at reducing compliance costs for US businesses. Meanwhile, the international compliance environment has moved in the opposite direction, adding disclosure layers, explanatory requirements, and public transparency obligations that many US businesses cannot avoid simply because they are headquartered in the United States.

This divergence has a direct implication for how tax departments allocate resources and make the internal case for investment in international compliance infrastructure. The burden internationally is not going down 鈥� indeed, it is intensifying 鈥� and that argument is now backed by concrete examples rather than projections.

3 things worth doing now

There are several actions that corporate tax teams should consider, including:

Audit CbCR data quality with Australia’s enhanced requirements in mind 鈥� If you cannot readily reconcile taxes accrued to taxes credited at the jurisdictional level, that gap needs to be closed before it becomes an authority inquiry.

Revisit EU filing jurisdiction strategy 鈥� If your jurisdictional decision was made at the time of initial implementation and has not been reviewed since, it is worth a fresh look before the next reporting cycle.

Develop an internal narrative around public CbCR data before it circulates externally 鈥� Your company鈥檚 tax story should not be a surprise to the corporate teams involved in communications, investor relations, or ESG 鈥� and in today鈥檚 world, assuming such news stays quiet is no longer a safe assumption.

While CbCR started as a tool for tax authorities, it today has become something more visible, more public, and more consequential than that 鈥� and that trajectory is not reversing any time soon.

You can download a full copy of the 成人VR视频 Institute鈥檚

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

Key takeaways:

• • • There is a widening gap between AI ambition and readiness 鈥� The gap between AI ambition and data readiness is widening, making the adoption of an adaptable data foundry essential for scalable, explainable, and compliant AI outcomes.

    • A data foundry model directly addresses the root cause 鈥� A data foundry model enables organizations to industrialize data production, automate compliance, and ensure consistent data lineage, thereby overcoming the limitations of brittle, legacy data architectures.

    • Incorporate the data core into your AI planning 鈥� Reinventing the data core is now a strategic imperative for those enterprises that aim to thrive in 2026 and beyond, as agentic AI, regulatory demands, and integration complexity accelerate.

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

A defining theme of this year so far is the widening distance between organizational ambition and data readiness. Leaders want the hype and inherent capabilities they believe are instantly contained within agentic AI 鈥� automated compliance, predictive integration for M&A, and decision-intelligence pipelines that reduce operational friction.

Without a data foundry, however, much of that will be impossible. Instead, workflows will remain brittle, AI agents will hallucinate under inconsistent semantics, and data lineage will break down across federated sources. Further, without a data foundry, regulatory mappings involved with the Financial Data Transparency Act (FDTA) and the Standard Business Reporting (SBR) framework cannot be automated, cross-functional insights will require manual reconciliation, and auditability will collapse under scrutiny.

This is not a failure of leadership. It is a failure of architectural design to recognize the congealment of data as a predecessor to technologies and the critical priorities of data security, auditability, and lineage.

For decades, organizations built monolithic systems that were optimized for stability and reporting. Today鈥檚 world demands modularity, continuous adaptation, and agent-driven interoperability. Architecture has shifted from build and operate to build and evolve. This is precisely what a data foundry enables.

Why reinvention can no longer wait

Throughout 2025 and now into the early months of 2026, data and AI have quietly shifted from innovation topics to enterprise constraints. Leaders across regulated markets are starting to recognize that the obstacles limiting their AI ambitions are neither mysterious nor technical 鈥� they are structural. These obstacles sit inside the data core, waiting inside the silent architecture that determines whether any form of automation, intelligence, or compliance can scale beyond a pilot.

The data bears this out. When you examine the work coming from Tier-1 research bodies, supervisory institutions, and global transformation benchmarks, a consistent narrative emerges beneath the headlines: AI is accelerating, regulation is hardening, and integration demands are expanding. Moreover, organizational data remains pinned to assumptions that were forged in static, pre-AI operating environments. This gap is not theoretical; rather, it is measurable, persistent, and directly correlated to business performance.

Let鈥檚 look at the AI results first. Across industries, organizations continue to experience a familiar pattern: early promise, limited adoption, and rapid degradation once the model encounters inconsistent semantics or fragmented lineage. Global studies show that the vast majority of enterprise AI initiatives still struggle to reach full production maturity, and among those that do, many encounter performance drift within the first year.

The driver is remarkably consistent. It is not the sophistication of the model nor the skill of the data science team 鈥� it is the quality, clarity, and traceability of the data that is feeding the system.

Taken together, these signals deliver a clear message. The gap between AI ambition and data readiness is widening, not narrowing. This is why the data foundry conversation matters now. It is not an abstract architectural concept. It is a response to the full stack of quantitative pressures the market has been telegraphing for years 鈥� costs rising, compliance hardening, AI accelerating, and integration straining under inconsistent semantics and fragile lineage.

A data foundry model directly addresses the root cause of this by industrializing the creation of consistent, reusable, explainable data products that can fuel agentic AI, support regulatory defensibility, and accelerate enterprise reinvention.

The numbers point to a simple conclusion. Reinvention is no longer optional, and the window to address the data core before agentic AI becomes standard practice is narrow and closing. The organizations that act now will be the ones that define what compliant, explainable, interoperable AI looks like in the next decade. Those that defer the work will find themselves restructuring under pressure rather than reinventing by choice.

This is the inflection point. In truth, the quantitative signals have made the case more clearly than a multitude of strategy narratives ever could.

The data foundry: A model for continuous alignment

Unsurprisingly, agentic AI introduces new, more demanding requirements, including:

• • • machine-interpretable semantics;
    • context-preserving lineage across federated systems;
    • decomposition of enterprise knowledge into reusable data products;
    • dynamic trust-scoring tied to source reliability and timeliness;
    • automated compliance overlays and regulatory logic; and
    • cross-domain metadata orchestration.

These capabilities are not optional, and they are non-negotiable. Indeed, they determine whether AI elevates risk or mitigates it, whether it accelerates productivity or introduces unrecoverable inconsistencies. And they determine whether AI augments decision quality or produces volatility.

A data foundry shifts organizations from artisanal, one-off data preparation and toward industrialized data production, in which patterns replace pipelines, and building blocks replace custom engineering. This shift will mean that lineage is generated, not documented; semantics are governed, not patched; and compliance is automated, not reconstructed. In this way, reuse becomes the default, not the exception.

In fact, this process is analogous to manufacturing. Instead of producing bespoke components for each need, the enterprise creates standardized, high-fidelity data assets that can be assembled into any workflow, any AI use case, and any reporting requirement.

A data foundry becomes the quiet architecture behind every future capability, making these capabilities systematic rather than ad-hoc. The chart below showcases the progressive build-up using a data factory, beginning with data intake and harmonization and ending with the AI agent orchestration and reusable data products that learn from their deployment.

Unfortunately, organizations are still building increasingly advanced AI decisioning and efficiency solutions on top of an aging and brittle data foundation. The results are predictable: stalled initiatives, compliance exposure, and stakeholder frustration. Additionally, instead of asking why, organizations keep adding more tools 鈥� more dashboards, more cloud services, more AI pilots, and more flavors of transformation.

Clearly, enterprises aren鈥檛 dealing with an AI problem. They鈥檙e dealing with a data alignment problem disguised as progress within fragmented, AI enclosures.

Reinvention starts at the data core

For more than a decade, firms across regulated industries have repeated the same mantra: Data is our most critical asset. When you peel back the layers or when you sit in board review sessions or integration meetings or regulatory remediation audits, however, the evidence does not match the rhetoric.

Reinvention is no longer optional. Instead, it is the starting point for meeting the demands of 2026 and beyond. The institutions that thrive will be those that understand that the data core is not a technical asset 鈥� it is the operational backbone of the enterprise. Indeed, the institutions that succeed will be those that recognize the truth early: AI is an output, and the data core is the strategy. And the organizations able to industrialize their data 鈥� through a foundry model, through AXTent, through repeatable semantic structures 鈥� will be the ones leading innovation, reducing compliance risk, accelerating M&A synergies, and achieving enterprise-wide reinvention.

In the end, the real question isn鈥檛 whether AI will transform business; the question is whether the data foundation will allow it. And the answer is rebuilding your data core so AI can actually deliver the outcomes your organization needs 鈥� and that work begins now.

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