Key takeaways:

• • • Agentic AI poised for adoption uptick 鈥� Agentic AI is following GenAI’s rapid adoption in the legal industry, with less than 20% of firms currently implementing agentic systems but half planning or considering adoption in the near future, according to a new report.

    • Adoption depends on human oversight answers 鈥� Legal professionals are generally optimistic about agentic AI’s potential, but successful adoption depends on explicit guidance about human oversight and the lawyer’s role in maintaining ethical standards.

    • Time to retool AI education? 鈥� Agentic AI’s increased autonomy introduces new oversight and ethical challenges for law firms, making targeted education and clear guidance essential to understanding the differences from GenAI.

Over the past several years, law firms and corporate legal departments have turned towards generative AI en masse. At the beginning of 2024, just 14% of all law firms and legal departments featured an enterprise-wide GenAI tool. Just two years later, that number had already risen to 43% of all firms and departments, according to the 2026 AI in Professional Services Report, from the 成人VR视频 Institute (TRI). For large law firms or legal departments, those percentages 鈥� not surprisingly 鈥� are beginning to approach 100%.

With GenAI adoption now this widespread, legal industry leaders are now turning their attention to two primary initiatives. One, of course, is how to get the most out of the AI tools they already have 鈥� a task that is proving a bit elusive. Currently, less than 20% of lawyers say their organizations measure AI鈥檚 return-on-investment, and most corporate lawyers say they have no idea how their outside law firms are approaching AI. Thus, instituting not just AI tools, but also an AI strategy is the second top priority for law firms and corporate legal departments in 2026 and beyond.

However, even as the legal industry reaches a tipping point in adopting GenAI tools, technology innovation still continues unabated. Agentic AI has emerged as the next wave of innovation that could change how lawyers work on a daily basis, offering a way to autonomously complete multi-step tasks. For example, agentic AI systems are already being built for the legal industry that independently researches a regulation or law, drafts a document based on the finding, identifies pitfalls, and revises the document, with stops for human guidance only instituted as desired.

According to the AI in Professional Services Report, the legal industry is already making headway towards implementing agentic AI systems. For agentic AI to truly take hold in legal, however, lawyers still require more education around not only how it differs from the GenAI systems they already have in place, but also when and where human intervention needs to occur within an agentic system.

The early stages of agentic AI

Examining current agentic AI adoption for the legal industry almost takes one back in time 鈥� two years, to be exact. Following the public release of GenAI in late-2022, many legal industry organizations spent 2023 evaluating and experimenting with AI systems, usually with a small working group of interested guinea pigs. As a result, only 14% of survey respondents said their law firms or corporate legal departments were engaged in organization-wide GenAI rollouts at the start of 2024. However, more than half of respondents said their organizations expected to be rolling out large-scale GenAI systems over the next 1 to 3 years. The intervening two years since then have proved that prediction to be largely true.

Agentic AI usage in the first half of 2026 looks largely similar to GenAI in 2024. The legal industry started to experiment with agentic AI at the beginning of 2025, with an eye towards actual implementation in 2026 and beyond (particularly as legal software providers began to integrate agentic systems into their own products). As such, less than 20% of recent survey respondents say their organization is engaged in widespread agentic AI adoption, but with about half of respondents said their organization is either planning to use or considering whether to use agentic AI in the near future.

By and large, lawyers feel positive about the agentic AI movement. When asked about their sentiment towards agentic AI, 51% of legal industry respondents said they felt excited or hopeful, while just 19% said they felt concerned or fearful. Further, about half (47%) said they actively believe agentic AI should be used for legal work, while 22% felt it should not, with the remainder saying they were unsure. These figures largely track with the sentiments expressed about GenAI in 2024, which have only grown over time from about 50% positive two years ago to two-thirds of all legal professionals feeling positive currently.

This all lends further credence to a rise in agentic AI usage similar to what law firms and corporate legal departments experienced with GenAI over the course of 2024 and 2025. Indeed, when asked when they expect agentic AI to be a central part of their workflow, few have baked agentic systems into their daily work currently, but a majority of legal industry respondents expect it to be central within the next 3 to 5 years.

The unique barriers of agentic AI adoption

Agentic AI does differ from GenAI in one crucial area that may limit its growth potential within the legal industry, however 鈥� autonomy. By and large, GenAI systems operate on a back-and-forth basis: Users provide the tool a prompt, receive its output, and then iterate back-and-forth from there. Agentic AI is intended to be more automated by design, only requiring human input at pre-determined points in the process. And that makes some lawyers understandably nervous.

When asked why they might feel hesitant about using agentic AI for legal tasks, the most common answer was a general fear of the unknown, but the second most common answer dealt with the need for careful monitoring and oversight. In fact, some respondents said they were excited about GenAI, but more cautious about agentic AI鈥檚 potential.

鈥淎gentic AI, while exciting, to me removes oversight a step too far,鈥� said one such lawyer from a US law firm. 鈥淚 like the idea of prompting and reviewing a result. It is something else to have a machine have so much autonomy in the actual doing of a thing and potentially acting on my behalf without that very concrete review.鈥�

Please add your voice to 成人VR视频鈥� flagship , a global study exploring how the professional landscape continues to change.听

An assistant GC at a US company also pointed to potential privacy and security concerns, adding: 鈥淭he fact that agentic AI operates in a much more autonomous way, with a lack of control from the user, means there are many unknowns that are hidden beneath the process.鈥�

For law firm and corporate legal department leaders looking to potentially implement agentic AI systems into their practice, this means re-thinking what AI education and training will mean moving forward. Beyond that, however, legal AI educators also will need to make sure to pinpoint and perhaps over-explain those specific instances in which human oversight needs to occur in agentic systems. More autonomous does not mean fully autonomous, and particularly for lawyers with ethical duties to their work product, lawyer oversight will in fact be a necessary part of any agentic system.

For law firm or legal department leaders, that means that finding the right balance between efficient workflows and human intervention will be key to agentic AI adoption. And those organizations that can best communicate human-in-the-loop to their professionals up-front will be rewarded with more increased and reliable adoption.

Clearly, lawyers feel positively about the agentic AI future, after all. They just need it spelled out explicitly as to what the lawyer鈥檚 role will be in this new paradigm.

鈥淎gentic AI is powerful, but its moral compass must come from humans,鈥� one UK law firm barrister noted aptly. 鈥淟awyers are trained to safeguard fairness, rights, and the rule of law 鈥� principles that should guide how AI is designed, governed, and deployed. Hope lies in our ability to shape AI through these values for fairer values for society as a whole.鈥�

You can download a full copy of the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report听here

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:

• • • Employees face contradictory messages or none at all 鈥� Nearly 40% of professionals surveyed report receiving conflicting directives about AI usage from clients and leadership, while half report no client conversations about AI have occurred at all.

    • Workers lack feedback on whether their AI efforts matter 鈥� Professionals who are experimenting with AI tools without knowing if their efforts are valued are left uncertain about whether investing time in developing AI skills is worth it.

    • Job displacement fears are rising 鈥� While employees remain cautiously optimistic about AI usage in their workplace, concerns about job displacement have doubled over the past year.

As generative AI (GenAI) tools flood into legal and accounting workplaces, organizations are deploying powerful technology without giving their employees clear directions on how to use it. Worse, some have received no guidance.

New research that underpinned the recent 2026 AI in Professional Services Report听from the 成人VR视频 Institute (TRI), reveals a disconnect between AI availability and organizational guidance, which is creating confusion that may undermine both employee experience and the technology鈥檚 potential value. (The report鈥檚 data was gathered from surveys of more than 1,500 legal, tax, accounting, and compliance professionals across 26 countries.)

Employees navigate inconsistent AI policies or none at all

Approximately 40% of the professionals surveyed said they received contradictory guidance from clients and leadership about AI tool usage, with directives both encouraging and discouraging their use on projects and in RFPs. This ambivalence is slowing down decision-making at the front lines 鈥� a place in which AI could deliver the most value.

Equally concerning is the fact that half of professionals indicated that no conversations with clients about AI tool usage have taken place yet. And when discussions do occur, concerns about data protection and accuracy are the main topics.

This confusion extends to external relationships as well. More than two-thirds of corporate and government clients remain unaware of whether their outside professional service providers are even utilizing GenAI. And the majority of clients have provided no direction whatsoever to their outside law firms concerning AI use, respondents said.

Organizations often ignore what employees need to know

Perhaps most revealing is how organizations are measuring 鈥� or failing to measure 鈥� whether their AI investments are paying off. Almost half of respondents said their organizations are not measuring return on investment (ROI) at all. Among the minority (18%) of respondents that said their organizations do track ROI, the metrics they use tell a story about organizational priorities. That fact that internal cost savings and employee usage rates lead the list suggests a focus on efficiency over innovation or quality improvements.

This measurement vacuum has consequences for employee experience. Without clear success metrics, employees lack feedback on whether their AI experimentation is valued, discouraged, or even noticed. The absence of ROI frameworks also makes it hard to justify training investments or dedicated time that allows employees to develop AI fluency.

AI usage doubles while support systems fall behind

AI usage among professional service organizations has nearly doubled over the past year, and professionals are increasingly integrating these tools into their workflows, the report shows. Yet organizational infrastructure that could support this adoption surge lags badly. Most professionals said they expect GenAI to become central to their work within the next two years 鈥� but that may be happening without roadmaps from their employers.

In addition, notable barriers in employees鈥� usage of AI remain. When asked what barriers could prevent their organization from more widely adopting GenAI and agentic AI, almost 80% of professionals cited concerns over inaccurate responses. Other concerns included worries over data security, privacy, and ethical use. Most of these suggest an ongoing lack of trust in GenAI.

The tool landscape adds another layer of complexity. Publicly available tools dominate current usage, with more than half of respondents (57%) citing their use, while proprietary or industry-specific solutions remain largely in the consideration phase. This suggests employees are often self-provisioning AI tools rather than working within enterprise-supported ecosystems. This potentially opens organizations to increased risk exposure because of security gaps, compliance risks, and inconsistent quality.

Employees鈥� job displacement fears increasing

Despite these challenges, employee sentiment toward AI remains cautiously optimistic. More than half (57%) of respondents said they are either hopeful or excited about the future of GenAI in their industry. Clearly, employees see AI’s potential to enhance their efficiency, automate routine tasks, and free up their time for higher-value work.

At the same time, hesitation and concern among employees are rising, particularly around accuracy, job displacement fears, and the unknown implications of autonomous AI systems. Notably, concerns about job displacement have doubled over the past year, and this trend demands organizational attention and transparent communication about a workforce strategy to combat this concern.

What organizations need to do now

Organizational leaders who are serious about positive employee AI experiences need to step up their efforts to provide guidance to employees and gain the ROI that AI promises. Specific steps they can take include:

• • • Draft clear and consistent guidance 鈥� Create explicit policies for employees about in which instances AI use is encouraged, required, or prohibited. This includes client communication protocols, data-handling requirements, and escalation procedures in those situations in which AI outputs seem questionable.
    • Develop and implement meaningful ROI metrics 鈥� Organizations must move beyond usage rates and cost savings as key success measurements. Tracking data points that capture quality improvements, time redeployed to strategic work, and client feedback on AI-enhanced deliverables present a more comprehensive picture. Also, leaders need to share these metrics transparently in order to give employees an understanding about organizational priorities.
    • Invest in structured learning 鈥� The survey shows professionals are experimenting with dozens of different tools from ChatGPT to specialized legal tech platforms. Organizations should curate recommended toolsets, provide hands-on training, and create communities of practice in which employees can share effective prompts and use cases with other users.

Our data shows that the employee experience around AI adoption reveals a workforce that is hopeful but hungry for direction and concerned about job impacts. Leaders who implement these actions effectively are more likely to unlock the strategic value that AI promises while building the trust and competence needed for their organizations and its employees to thrive in an automated future.

You can download a full copy of the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report听here

Key insights:

• • • There鈥檚 an AI awareness gap 鈥� Most corporate legal professionals do not know whether their outside legal counsel are using AI in handling their client matters, leaving both law departments and their firms in a state of AI uncertainty.

    • A potential upcoming billing model shift 鈥� Efficiencies from AI usage could have a major impact on how many law firms bill matters; value-based billing may need to replace or supplement hourly billing for matters in which AI is used.

    • Transparency builds trust 鈥� Lack of visibility and ROI measurement could erode trust between law departments and their outside counsel. Dialogue and measurements can strengthen the firm/client relationship and create scenarios in which both sides can reap the benefits of AI usage.

While the use of AI is increasingly widespread for both corporate legal departments and their outside law firms, there is a considerable lack of dialogue and data-sharing between the two sides on usage, guidelines, and expectations regarding AI. This complicates efforts to maximize the benefits of using AI, and it also may be eroding trust between the two sides.

Significant gaps in visibility and measurement

The 成人VR视频 Institute鈥檚 (TRI鈥檚) 2026 AI in Professional Services Report found major gaps in visibility and measurement between law firms and legal departments. The survey found that more than half of law firm respondents said their organizations are currently using or considering using GenAI. And more than half of corporate legal professionals surveyed said they feel that their outside legal firms should use AI on their matters.

However, more than two-thirds (68%) of corporate legal professionals admitted that they currently have no idea if their outside law firms are using AI or not.

In addition, neither side is effectively measuring whether or to what degree their use of AI is improving the delivery of legal services. Indeed, 85% of law firm respondents and 75% of corporate legal department respondents said their organizations are either not collecting ROI data on AI usage or are unsure if they are doing so.

Is your organization measuring the ROI of AI tools?

These visibility and measurement gaps make it difficult for both sides to plan how AI can and should be used in handling client matters. It also raises questions about how potential efficiencies from AI use will affect related factors such as how much firms charge for their services and how much clients are willing to pay. Half of legal professionals surveyed said they feel that AI is either a major threat or somewhat of a threat to billings and law firm revenues. Not surprisingly, the industry continues to wrestle with how to balance efficiency gains from AI against the limitations of the hourly billing model.

Concerns of corporate law departments

For corporate law departments, the lack of AI usage visibility and ROI measurement is producing a wide variety of responses, ranging from mild but growing concern all the way to outright suspicion about how law firms are using AI on their clients鈥� behalf. Law department respondents said that while they generally trust their outside counsel to make the right decisions regarding AI use and maintaining quality, most departments have not yet had conversations on those issues with their law firms, including how AI use will affect billing.

鈥淏illing has remained the same as it did before,鈥� noted one corporate legal department attorney. 鈥淪o, either they are not using AI tools efficiently, or they are just doing double work.鈥�

One corporate CLO was far more blunt in their assessment, especially given the lack of detailed discussions or data from firms: 鈥淚 fear that firms will use AI to cut time, but continue to bill for the hypothetical amount of time a task would have taken without it. It’s dishonest, but so are many firms.鈥�

One encouraging note is that, according to TRI鈥檚 2025 Future of Professionals Report, 56% of law firm respondents said they are highly or moderately confident in their ability to articulate the value of AI to their clients. Despite law firms鈥� confidence in explaining the value of AI, however, the visibility gap illustrated in the 2026 AI in Professional Services Report indicates that law firms are not actually having those conversations with clients. Indeed, some corporate law department respondents suggested their outside counsel may be reluctant to discuss AI with them because of concerns about quality and accuracy. One even suggested that firms may feel threatened by AI.

More & better communication is needed

As difficult and complicated as discussions involving AI usage may be, they are also essential. Absent those discussions, trust between firms and clients may be eroding, potentially jeopardizing long-standing relationships.

Here are a few steps that both sides can take to build confidence around the use of AI:

For law firms 鈥�

• • Communicate with clients 鈥� Hold discussions with clients that allow firms to detail how AI is being or will be used in client matters. Solicit feedback from clients about in which instances they would accept (or even demand) AI usage on different parts of a matter.
  • Develop an AI billing strategy 鈥� Determine not only how AI usage is impacting billable hours, but also how that will interact with the firm鈥檚 billing and pricing strategy.
  • Demonstrate and articulate value 鈥� Be prepared to explain billings in detail and answer client questions in terms of not only time and rates, but of value to the client. This includes both the value that AI brings to client engagement, but also the value that the firm brings above and beyond what technology provides, such as more freed-up time for lawyers to pursue value-added work.

For corporate law departments 鈥�

• • Lead the conversation, if need be 鈥� About three-quarters of both law firm and legal department respondents said it is the firm鈥檚 responsibility to initiate discussions around AI usage. However, corporate law departments should not wait for their outside firms to start the conversation. Take the initiative and make sure firms鈥� delivery models and fee structures are clear regarding AI usage.
  • Set expectations 鈥� Provide guidelines, expectations, or mandates on how and when AI will be used in handling client matters. This includes outlining specific use cases, data security protocols, and the human-in-the-loop oversight mechanisms that are used to ensure accuracy.
  • Build an external-facing metrics program 鈥� Law departments need to accurately measure the efficiency gains their outside firms are achieving to ensure that they, as the client, are receiving a fair price for value received. Baselines can be established for how long various legal matters took historically and how much they cost. The baselines then can be compared against AI-enabled engagements to evaluate ROI and business impact. This also allows legal departments to more thoroughly explain those gains to their own stakeholders.

For both corporate law departments and their outside counsel, it is imperative to engage in thorough discussions and develop data that can inform better decision-making. Such dialogue and measurements can strengthen the firm/client relationship and create scenarios in which both sides can reap the benefits of AI use.

You can download a full copy of the 成人VR视频 Institute鈥檚听2026 AI in Professional Services Report听here

Key takeaways:

• • • Legacy data architectures can’t keep up with modern demands 鈥� Traditional, centralized data cores were designed for stable, predictable environments and are now bottlenecks under continuous regulatory change, rapid M&A, and AI-driven business needs.

    • AXTent aims to unify modern data principles for regulated enterprises 鈥� The modern AXTent framework integrates data mesh, data fabric, and composable architecture to create a data core built for distributed ownership, embedded governance, and adaptability.

    • A mindset shift is required for lasting success 鈥� Organizations must move from project-based data initiatives to perpetual data development, focusing on reusable data products and decision-aligned outcomes rather than one-off integrations or platform refreshes.

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

For more than a decade, enterprises have invested heavily in data modernization 鈥� new platforms, cloud migrations, analytics tools, and now AI. Yet, for many organizations, especially in regulated industries, the results remain underwhelming. Data integration is still slow because regulatory reporting still requires manual remediation, M&A still exposes hidden data liabilities, and AI initiatives struggle to move beyond pilots because trust and reuse in the underlying data remains fragile.

The problem is not effort, it is architecture. Since 2022, the buildup around AI has been something out of science fiction 鈥� self learning, easy to install, displace workers, autonomous, even Terminator-like. Moreover, while AI may indeed revolutionize research, processes, and profits, the fundamental challenge is not the advancing technology, rather it is the data used to train and cross-connect these exploding capabilities.

Most data cores in use today were designed for an earlier operating reality 鈥� one in which data was centralized, reporting cycles were predictable, and governance could be applied after the fact. That model breaks down under the modern pressures of continuous regulation, compressed deal timelines, ecosystem-based business models, and AI systems that consume data directly rather than waiting for curated outputs.

So, why is the AI hype not living up to the anticipated benefits? Why is the data that underpinned process systems for decades failing to scale across interconnected AI solutions? The solution requires not another platform refresh, but rather, a structural reset of the data core itself.

That reset uses data meshes, data fabrics, and modern composable architecture as a single, integrated system, and aligns it to the AXTent architectural framework, which is designed explicitly for regulated, data-intensive enterprises.

Why the traditional data core no longer holds

Legacy data cores were built to optimize control and consistency. Data flowed inward from operational systems into centralized repositories, where meaning, quality, and governance were imposed downstream. That approach assumed there were stable data producers, limited use cases, human-paced analytics, and periodic regulatory reporting.

Unfortunately, none of those assumptions hold today. Regulatory expectations now demand traceability, lineage, and auditability at all times (not just at quarter-end). M&A activity requires rapid integration without disrupting ongoing operations. And AI introduces probabilistic decision-making into environments built for deterministic reporting, with business leaders expecting insights in days, not months.

The result is a growing mismatch between how data is structured and how it is used. Centralized teams become bottlenecks, pipelines become brittle, and semantics drift. Compliance then becomes reactive, and the cost of change increases with every new initiative.

The AXTent framework starts from a different premise: The data core must be designed for continuous change, distributed ownership, and machine consumption from the outset. Indeed, AXTent is best understood not as a product or a platform, but as an architectural framework for reinventing the data core. It combines three design principles into a coherent operating model:

1. 1. 1. Data mesh 鈥� Domain-owned data products
      2. Data fabric 鈥� Policy- and metadata-driven connectivity
      3. Data foundry 鈥� Composable, evolvable data architecture

Individually, none of these ideas are new. What is different 鈥� and necessary 鈥� is treating them as a single system, rather than independent initiatives as conceptually illustrated below:

Fig. 1: The AXTent model of operation

The 3 operating principles of AXTent

Let鈥檚 look at each of these three design principles individually and how they interact with each other.

Data mesh: Reassigning accountability where it belongs

In regulated enterprises, data problems are rarely technical failures. Instead, they are accountability failures. When ownership of data meaning, quality, and timeliness sits far from the domain that produces it, errors propagate silently until they surface in regulatory filings, audit findings, or failed integrations.

A structured framework applies data mesh principles to address this directly. Data is treated as a product, owned by business-aligned domains that are then accountable for semantic clarity, quality thresholds, regulatory relevance, and consumer usability.

This is not decentralization without guardrails, however. AXTent enforces shared standards for interoperability, security, and governance, ensuring that domain autonomy does not fragment the enterprise. For executives, the benefit is practical: faster integration, fewer semantic disputes, and clearer accountability when things go wrong.

Data fabric: Embedding control without re-centralization

However, distributed ownership alone does not solve enterprise-scale problems. Without a unifying layer, decentralization simply recreates silos in new places.

A proper framework addresses this through a data fabric that operates as a control plane across the data estate. Rather than moving data into a single repository, the fabric connects data products through shared metadata, lineage, and policy enforcement.

This allows the organization to answer critical questions continuously, such as:

• • • Where did this data come from?
    • Who owns it?
    • How has it changed?
    • Who is allowed to use it 鈥� and for what purpose?

In this way, governance is no longer a downstream reporting activity; rather, it is embedded into how data is produced, shared, and consumed. Compliance becomes a property of the architecture, not a periodic remediation effort.

And in M&A scenarios, the fabric enables incremental integration, which allows acquired data domains to remain operational, while being progressively aligned rather than forcing immediate and costly consolidation.

Composable architecture: Designing for evolution, not stability

The third pillar of the AXTent model is a modern data architecture that鈥檚 designed to absorb change rather than resist it. Traditional architectures usually rely heavily on rigid pipelines and tightly coupled schemas. While these work when requirements are stable, but they may collapse under regulatory change, new analytics demands, or AI-driven consumption.

AXTent replaces pipeline-centric thinking with composable services, including event-driven ingestion and processing, API-first access patterns, versioned data contracts, and separation of storage, computation, and governance.

This approach supports both human analytics and machine users, including AI agents that require direct, trusted access to data. The result is a data core that evolves without constant re-engineering, which is critical for organizations operating under continuous regulatory scrutiny or frequent structural change. AXTent allows acquired entities to plug into the enterprise architecture as domains while preserving context and enabling progressive harmonization.

The architectural compass

This framework exists for one purpose: to provide a practical, business-oriented methodology for building a reusable, decision-aligned, compliance-ready data core. It is not a product nor a platform. It is a vocabulary that鈥檚 backed by building blocks, patterns, and repeatable workflows 鈥� and it鈥檚 one that executives can use to organize data around outcomes instead of systems.

Overall, the AXTent model prioritizes data clarity over system modernization, decision alignment over model sophistication, continuous compliance over intermittent remediation, reusable data products over disconnected pipelines, and enterprise knowledge codification over one-off integration work.

In essence, organizations should move away from project thinking and toward perpetual data development, in which every output contributes to a compound knowledge base. This is the mindset shift the industry has been missing as it prioritizes AI engineering over business purpose.

In the final post in this series, the author will explain how to shift from 鈥渂uild and operate鈥� to 鈥渂uild and evolve鈥� via a data foundry. You can find more blog posts听by this author here

Key findings:

• • • AI adoption accelerates across professional services听鈥� Organization-wide use of AI in professional services almost doubled to 40% in 2026, with most individual professionals now using GenAI tools, and many preparing for the next wave of tools such as agentic AI.

    • Strategic integration and measurement lag behind usage 鈥� While AI use is widespread, only 18% of respondents say their organization tracks ROI of AI tools, and even fewer measure AI’s impact on broader business goals such as client satisfaction or revenue generation.

    • Communication around AI use remains inconsistent听鈥� While most corporate departments want their outside firms to use AI on client matters, less than one-third are aware whether their firms are doing so. Meanwhile, firms report receiving conflicting instructions from clients about AI use, highlighting a need for clearer dialogue and shared strategy around AI adoption.

Over the past several years, AI usage within professional services industries has come into focus. As we enter 2026 in earnest, the early adoption phase of generative AI (GenAI) has come and gone. Today, most professionals have experimented with some form of GenAI, and many organizations integrated GenAI into their workflows 鈥� and now, a number are preparing for the next wave of technological innovation such as agentic AI.

Given this, the question for professionals and organizational leaders has now become: What will be AI鈥檚 long-term impact on my business?

Jump to 鈫�

2026 AI in Professional Services Report

To delve into this question further, the 成人VR视频 Institute has released its 2026 AI in Professional Services Report, which takes a broad view into the current usage and planning, sentiment towards, and business impact of AI for legal, tax & accounting, corporate functions, and government agencies. Taken from a survey of more than 1,500 respondents across 27 different countries, the report finds a professional services world that has embraced AI鈥檚 use but is continuing to evolve business strategy around its implementation.

For instance, the report shows that to 40% in 2026, compared to 22% in 2025 鈥� and for the first time, a majority of individual professionals reported using publicly-available tools such as ChatGPT. Additionally, a majority of respondents said they feel either excited or hopeful for GenAI鈥檚 prospects in their respective industries, and about two-thirds said they felt GenAI should be applied to their work in some manner.

At the same time, however, many are exploring GenAI tools without much guidance as to how that use will be quantified or measured. Only 18% of respondents said they knew their organization was tracking return-on-investment (ROI) of AI tools in some manner, roughly the same proportion as last year. And even among those tracking AI metrics, most are tracking mainly internally-focused, operational metrics; and only a small proportion analyzed AI鈥檚 impact on their organization鈥檚 larger business goals 鈥� such as client satisfaction, external revenue generation, and new business won.

This slow move to strategic thinking also impacts client-firm relationships. Although more than half of both corporate legal departments and corporate tax departments want their outside firms to use AI on client matters, less than one-third said they were aware whether their firms were doing so or not. From the firm standpoint, meanwhile, confusion reigns: 40% of firm respondents said they have received orders both to use AI on matters and not to use AI on matters from various clients.

Indeed, bout three-quarters of corporate respondents and firm respondents agreed that firms should be taking the lead in starting these conversations around proper AI use. Yet these discussions have not yet happened en masse. 鈥淔irms are reluctant 鈥� they claim it would compromise quality and fidelity,鈥� said one U.S.-based corporate chief legal officer. 鈥淚 think they are threatened by it.鈥�

All the while, technological innovation progresses ever quicker. This year鈥檚 version of the report measures agentic AI use for the first time, finding that already 15% of organizations have adopted some type of agentic AI tool. Perhaps more interesting, however, is that an additional 53% report their organizations are either actively planning for agentic AI tools or are considering whether to use them, indicating perhaps an even more rapid pace of adoption than we鈥檝e already seen with the speedy rise of GenAI.

Overall, the report makes it clear that most professionals do understand that change, driven by AI in the workplace, is undoubtedly here. Even compared with 2025, a higher proportion of professionals said they believe that AI will have a major impact on jobs, billing and revenue, and even the need for legal or tax & accounting professionals as a whole. The percentage of lawyers calling AI a major threat to the unauthorized practice of law rose to 50% in 2026 from 36% in 2025.

Further, this report paints the picture of a professional services world that has embraced AI, begun to see its impact, and realized that it will have broader business and industry implications than previously imagined. As a result, the time for professionals and organizations to begin planning in earnest for an AI future has already arrived.

As a corporate general counsel from Sweden noted: 鈥淲e cannot keep up with the modern-day corporations鈥� demands unless we also develop and adapt our way of working.鈥�

You can download

a full copy of the 成人VR视频 Institute’s 2026 AI in Professional Services Report听here

Key takeaways:

• • • Strategic alignment drives ROI听鈥� Organizations that implement GenAI with a clear, formal strategy aligned to their broader business goals, such as revenue growth or client experience, are able to find stronger ROI measurements than those adopting AI informally.

    • Measuring GenAI requires more than basic metrics听鈥� While many firms currently track simple, internally-focused metrics like cost savings and user adoption, true value from GenAI comes from mapping its use to strategic outcomes such as revenue generation, operational efficiency, and client satisfaction.

    • AI strategy aids measurement capabilities听鈥� Despite increasing adoption of GenAI tools, less than one-quarter of professional services organizations have a visible AI strategy, according to our research, which decreases their ability to properly measure GenAI鈥檚 organizational impact.

At this point of the lifecycle of generative AI (GenAI), most individuals across the professional services world have a conception of what GenAI is and what it can do. Indeed, 96% of respondents had at least a basic understanding of AI principles, according to the 2025 Future of Professionals report, which surveyed corporate, legal, tax & accounting, and government professionals.

With that in mind, most organizations are prepared to take the next step: Making GenAI an integral part of their operations and measuring its direct impact on the organization. It鈥檚 a natural progression, as individual use of publicly available GenAI technologies such as ChatGPT or Claude turns into institutional investment in business-centric tools such as Microsoft Copilot or industry-specific GenAI tools.

Of course, organizational leaders whose teams are using these tools want to see how much these tools really help, and attempt to quantify GenAI鈥檚 return-on-investment (ROI).

However, those that have undertaken the ROI exercise have found that arriving at an answer may be easier said than done for a number of reasons. Many professionals are just beginning with the tools and have not yet fully integrating them into their workflow, which makes the true impact of GenAI harder to measure. Determining the time saved by AI tools requires an intricate knowledge of how these professionals work on a daily basis; and most professional services firms are not yet talking to their outside clients about GenAI, making calculations around business won or client satisfaction next to impossible to compute.

That said, however, there already are some simple ways to start to map GenAI usage to a set of ROI metrics. It starts with knowing what your organization wants to achieve by using GenAI.

Mapping use cases to goals

GenAI, as is the case with all business-oriented technologies, should not be treated as a goal in itself. When determining metrics around AI use, start with the organization鈥檚 primary set of strategic initiatives then extrapolate from there.

For instance, increasing revenue is a way 81% of C-Suite respondents say they measure success, according to the 成人VR视频 Institute鈥檚 recent 2025 C-Suite Survey. GenAI, therefore, should be rolled out with this in mind, with potential use cases for the technology aimed squarely at increasing revenue such as by delivering stronger market analysis and predictive analytics for client issues. If instituted with the larger revenue goal in mind, the ultimate metric for the technology鈥檚 success then is not simply usage, but how well the technology actually contributes to revenue gains.

The chart below from the Future of Professionals Report provides some examples from a law firm perspective of how other organizational goals can lead to ROI metrics, including bolstering the client experience, creating operational efficiencies, and attracting and engaging talent. Other industries such as tax, audit & accounting; government agencies; and courts have their own sets of goals that can be adapted in the same fashion.

GenAI is a powerful tool particularly because of its versatility. While many past technologies aimed at professional services were focused squarely on one or two use cases, GenAI, as demonstrated above, can be adapted to serve a number of different uses and goals. As a result, implementing these use cases 鈥� and crucially, measuring their success 鈥� requires more strategic planning than past technologies.

The importance of strategy

Even with the rate of GenAI adoption continuing to climb, formal AI strategies are not climbing at the same rate. The Future of Professionals report found that just 22% of respondents say their organizations have a visible AI strategy, while 43% say their organizations are moving ahead with adoption despite having no formal strategy in place. About one-third of respondents, meanwhile, say their organizations have no significant plans for widespread adoption.

Unsurprisingly given the above, this lack of strategy has a tangible impact on measurable ROI, particularly as it relates to underlying revenue. The report notes that organizations with a strategic AI plan are almost twice (1.9-times) as likely to already be experiencing revenue growth as a result of their AI investment than those organizations that are adopting AI informally. Similarly, 81% of respondents at organizations with an AI strategy report seeing some sort of positive ROI from AI; only 64% of respondents at organizations adopting AI informally say the same.

Measuring proper ROI from GenAI implementation is not an impossible undertaking, but at the same time, it is not an easy proposition. The 成人VR视频 Institute鈥檚 2025 Generative AI in Professional Services Report from earlier this year found that even of those organizations measuring GenAI鈥檚 impact, the most common metrics were simple and often internally-focused, such as internal cost savings, user adoption, and user satisfaction. Metrics focused on client satisfaction or external revenue generation, meanwhile, were tracked by less than 40% of organizations, according to survey respondents.

That is the wrong way to approach AI measurement, particularly in a professional services landscape that expects GenAI (and soon, agentic AI) to become a central part of the profession鈥檚 workflow within the next five years. If GenAI is becoming so crucial to the organization, then its measurement should be based not on simple technology metrics, but on larger strategic metrics for the organization.

And that means, for organizations without an AI strategy that links to the larger organization鈥檚 overall strategy, the time to begin that planning in earnest for the AI-driven future has arrived.

You can download your copy of the听2025 Future of Professionals Report here

Indeed, some economists have found that GenAI has the in the LatAm region by transforming jobs and industries, but inadequate infrastructure and digital access are problems that hinder these gains. Others have stated that the region鈥檚 large informal sector makes due to limited access to financial and legal opportunities, and the struggle to attract investment.

The TRI survey report complements the few existing studies on GenAI and its impact on Latin America, enabling us to examine the opinions of professionals in Latin America on GenAI and contrast these views with those of their contemporaries around the globe.

Latin America鈥檚 strong optimism for GenAI

A striking 56% of LatAm respondents to the TRI survey report expressed excitement about the future of GenAI in their industries, a figure that significantly surpasses the 27% of global respondents sharing this feeling. Additionally, another 22% of LatAm respondents indicated further positive sentiment towards GenAI, saying they feel hopeful about it. These findings reveal the region鈥檚 enthusiasm to embrace technological advancements and leverage them to promote growth and innovation within their respective industries.

When considering the application of GenAI in professional services work, LatAm respondents showed remarkable optimism. An impressive 85% said they believe GenAI should be integrated into their work, compared to 62% globally. And while 4% of LatAm respondents said they did not think GenAI should be applied in their jobs, this is considerably less than the 13% of global respondents who said the same thing. This positive stance reflects Latin America鈥檚 proactive approach to adopting AI technologies in order to improve efficiency and effectiveness in professional work.

According to LatAm respondents, GenAI will shape the future of professional work in the region in various ways. The most popular view, shared by 20% of respondents, was that GenAI will make an impact on their work through transformative changes. Further, 18% said they believed that GenAI will streamline work processes, making tasks more efficient and reducing the time and effort required to complete them. Similarly, another 18% said they regarded GenAI as a valuable tool for enhancing work-life balance, suggesting that its integration could lead to more flexible and manageable work environments.

GenAI challenges and opportunities in Latin America

Despite the region’s overall positive sentiment towards GenAI, survey respondents in Latin America identified certain challenges and opportunities that must be correctly assessed to ensure its successful adoption and maximize its potential in the region.

• • • Concerns and barriers to adoption 鈥� Despite the overall optimism, LatAm respondents acknowledged significant concerns that could hinder GenAI adoption. Data security (cited by 53% of LatAm respondents), privacy and confidentiality of information entered into GenAI tools (50%), and the cost of these tools (50%) emerged as primary barriers. By contrast, the most popular concern on a global scale was the potential for inaccurate responses (73%).
    • Policy and training gaps 鈥� Interestingly, only 18% of LatAm respondents said their organizations have established policies guiding GenAI use, compared to 36% globally. Further, 77% of LatAm respondents reported a lack of training on GenAI from their organizations. When compared to the global picture, this figure is higher by 13 percentage points (64% globally).
    • Integration and future plans 鈥� While LatAm currently lags behind the global average (22%) in the business integration of GenAI, with only 15% of LatAm respondents saying they already use it, there is a strong inclination towards future adoption. More than one-third (38%) of respondents from the region said their organizations are planning to implement GenAI, and 20% said they are still evaluating its usage within their organizations.
    • Timeframe for wide-scale adoption 鈥� Less than one fifth (18%) of LatAm respondents mentioned that their organizations are already using GenAI on a wide-scale basis, while 26% of global respondents said their organizations were. However, 21% of LatAm respondents said they expect this to happen within their organizations over the next 6 months, 29% said they think it will happen in 6 to 12 months, and 12% said they anticipate this change in 1 to 3 years. Only 20% said they were not sure about their organization鈥檚 adoption timeline.

Making LatAm services more competitive

The survey results reveal Latin American professionals鈥� strong belief in GenAI’s potential to improve productivity and reshape the landscape of professional work in new ways. While the region may currently lag in the organization-wide integration of GenAI compared to other parts of the world, its proactive position is evident in the significant number of respondents who voiced excitement about it and whose organizations are planning to implement GenAI in their processes sooner rather than later.

With structured strategies, well-studied policies, and an investment plan that involves tech tools and personnel training, organizations in Latin America can embrace GenAI successfully and enhance their competitiveness and innovation in the global market.

You can download a full copy of the 成人VR视频 Institute鈥檚 recent 2025 Generative AI in Professional Services Report here

With GenAI becoming an integrated part of regular work so quickly, it鈥檚 understandable if some organizations feel like they are building the plane while flying it. In fact, only about one-third (36%) said they knew their organization had policies governing GenAI usage, and only 20% said they believe their organizations are collecting metrics around the return-on-investment (ROI) of GenAI tools. Still less than three years removed from GenAI鈥檚 public introduction, there remains work to be done surrounding how GenAI will be governed within wider business and operating practices.

There remains one major task, however, that many professional services organizations should be tackling sooner rather than later: training and education. In fact, according to the report, more professionals reported using publicly available GenAI tools like ChatGPT (41%) than have received organizational training on proper GenAI use (31%). The result means that without changes, the potential for both technical misuse and ethical misconduct with GenAI usage remains high.

The problem with less training

Early on in GenAI鈥檚 introduction to professional services, the main concern was around accuracy and hallucinations. The 2023 legal case Mata v. Avianca, in which a lawyer within a court-submitted legal briefing, became shorthand for everything that can go wrong with using GenAI for professional work.

Register now for The Emerging Technology and Generative AI Forum, a cutting-edge conference that will explore the latest advancements in GenAI and their potential to revolutionize legal and tax practices

Two years later, more legal, tax, and corporate professionals are aware that hallucinations can occur and that GenAI needs to be used in concert with professionals鈥� expertise in order to produce accurate results. Now, however, experts believe there is a more hidden, but also costly, issue at hand within GenAI adoption 鈥� Improper use which can limit the value that GenAI tools can actually bring.

Speaking with the TRI last year, Northwestern law school professor Daniel Linna expressed fears that those slow-playing GenAI roll-out or waiting on engaging with the technology be left watching as a golden opportunity passes them by. 鈥淭hat鈥檚 like saying I鈥檓 not going to practice playing the game of golf, I鈥檒l just figure out the best set of clubs to buy next year and then I鈥檒l go out there and I鈥檒l compete in the Tour,鈥� Linna explained.

According to the research, many professional services organizations have not taken that advice to heart. Many have not yet begun training their professionals on GenAI tools, even as they have begun adopting GenAI throughout the enterprise.

This is an issue, because not only do 41% of professionals say they use publicly available GenAI tools such as ChatGPT, but 17% say they use industry-specific solutions that incorporate GenAI technology 鈥� and an additional 50% said they are planning to use or considering whether to use these industry-specific solutions.

Further, even if professionals are receiving training, that education may not keep up with GenAI鈥檚 rapid pace of adoption. More than half of respondents who have gotten GenAI training said that training occurs either annually or less than annually.

With this infrequency of training, professionals cannot truly keep up with new GenAI developments. After all, multiple new platforms have arisen just since ChatGPT became public in late-2022, and a host of pre-existing technologies have received GenAI-enabled upgrades. For many professional service organizations, it is clearly not enough to check the box of AI training. Given the rapid pace of technological change, any GenAI training needs to be a longitudinal program.

4 tips on training

GenAI technology is not simply something that professional services organizations can adopt and forever assume it will function correctly. In order to get the most out of GenAI tools, it takes an integrated plan of people, processes, and technology to extract the most value from these ever-changing tools.

Here are a few tips that can help those organizations that are looking to start 鈥� and continue to iterate 鈥� their own GenAI program.

1. 1. 1. Find the value above and beyond 鈥� GenAI technology鈥檚 use cases are very widespread, with everything from research, discovery and contracts in law, to generating returns in tax, and more. Because of this, it can be easy to become focused solely on how the technology functions and ignore the why. However, by connecting GenAI use cases to the bottom line 鈥� how it鈥檚 helping save money, gain efficiencies, or aid client service 鈥� GenAI trainers can more easily gain buy-in from users.
      2. Have power-users teach peers 鈥� Particularly with professionals such as lawyers, accountants, and others, technology newcomers love hearing from their peers. Establishing a training team that not only includes IT and HR staff, but also professionals鈥� own peers, can help create GenAI use case stories that truly hit home. 鈥淭he best people to tell them what鈥檚 in it for me, is that other lawyer,鈥� recently explained Don Sternfeld, Chief Innovation Officer at law firm Steptoe. 鈥淭hey won鈥檛 listen to my emails, but they鈥檒l listen to their peers in that meeting who says, 鈥極h, this is something out there.鈥欌��
      3. Measure, measure, measure 鈥� Only 20% of all professionals said they know that their organizations are collecting data around GenAI鈥檚 ROI, according to the report, which from a business perspective raises issue of how best to serve clients. Yet, it also can cause issues internally around making sure AI-driven tools are used to the best of professionals鈥� ability. Even simple metrics such as employee usage rates and employee satisfaction can help guide how and in what areas GenAI can be best utilized, while business metrics such as client satisfaction and projected external revenue generation can help make the business case for continued investment in training on tools.
      4. Keep up with changes 鈥� GenAI technology changes rapidly. After all, the public version of ChatGPT, otherwise known as GPT-3.5, was released in November 2022, and already GPT-5.0 is expected to be released in Summer 2025. As a result, some skills such as prompting and hallucination-detection may decrease in importance as GenAI tools become more accurate, while other skills such as ensuring ethical use and proper data governance may increase in importance as GenAI becomes more pervasive. Constant training allows professionals to keep up with these changes and adapt their own usage trends to match their organizations鈥� changing technology stack.

You can download a full copy of the听2025 Generative AI in Professional Services Report听here

For professionals in legal, tax, risk & fraud, and government industries, the shift has been both quick and dramatic. Already more than half of all legal, tax, risk & fraud and government professionals have used GenAI in some fashion, with a wide range of use cases already arising, according to the newly published 成人VR视频 Institute鈥檚 2025 Generative AI in Professional Services Report. In fact, the report shows that professionals from across the United States, the United Kingdom, Canada, Latin America, Australia, and New Zealand are not only expecting GenAI to become a more common part of their work, but they鈥檙e feeling more positive about the impact GenAI will have on their profession.

Register now for The Emerging Technology and Generative AI Forum, a cutting-edge conference that will explore the latest advancements in GenAI and their potential to revolutionize legal and tax practices

However, even with usage skyrocketing, many of the firms, departments, and agencies in which these professionals work still have a way to go to fully extract value from GenAI. The report shows that few organizations are capturing return-on-investment (ROI) metrics, regularly training staff on GenAI updates, or integrating GenAI use into their policies. In addition, few professionals say their firms and their clients are having conversations around GenAI use. And even more worrisome, questions about GenAI鈥檚 impact on billing rates and costs remain unanswered.

This year鈥檚 report reflects a crossroads: GenAI itself is seeing adoption on a wide scale, but professional services industries largely still have yet to discern what it means for the future of their businesses.

Key findings in the report

Some key findings in this year鈥檚 Generative AI in Professional Services Report include:

• • • Steady usage increases 鈥� A large portion (41%) of respondents said they personally use publicly-available tools such as ChatGPT, and 17% said they personally use industry-specific GenAI tools. On an organization-wide level, the percentage of respondents who said their organizations were actively using GenAI nearly doubled over the past year, to 22% in 2025, compared to 12% in 2024.
    • Soon to be central to workflow 鈥� Just 13% said GenAI is central to their organizations, workflow currently, but an additional 29% said they believe it will be central within the next year. Further, 95% of all respondents believe it will be central to their organization鈥檚 workflow within the next five years.

• • • Maintaining positivity 鈥� More than half (55%) of all respondents categorize their sentiment towards GenAI in their profession as excited or hopeful. Meanwhile, the proportion who said they were hesitant, concerned or fearful fell 12 percentage points over the past year.
    • Business questions remain 鈥� Only 20% said they knew their organizations were measuring ROI of GenAI, and many firm respondents remain unsure about GenAI鈥檚 impact on rates or client costs. And while 57% of corporate clients want their outside firms to be using GenAI, 71% of law firms鈥� clients and 59% of tax firms鈥� clients said they did not know whether their firms were using it or not.
    • Policies & training still needed 鈥� More than half of respondents (52%) said they believed their organizations had no policies around GenAI at work, whether a standalone policy or as part of a larger technology policy. Nearly two-thirds (64%), meanwhile, said they had received no GenAI training at work.

At this point, it is evident that GenAI is here to stay. Professionals across multiple industries are adopting it for their own personal use in droves, whether leveraging free tools or, increasingly, business or industry-specific technologies. Organization-wide adoption is beginning to occur as well, albeit slowly.

The question, then, becomes what will professional services industries do with GenAI once it鈥檚 adopted. As the report reveals, many may not yet know 鈥� but they do know they need to begin having those conversations quickly.

鈥淭he next 24 months will be extremely telling on the impact of GenAI on the legal industry and professional work more broadly,鈥� said one Australian law firm attorney. 鈥淎s products move out of development [and in]to production, we will be able to see the actual effects of this technology across various sectors.鈥�

You can download a full copy of the 2025 Generative AI in Professional Services Report here