In the era of digital intelligence, everything becomes part of a business strategy. It happened to accounting, and the modern in-house department isn’t far behind. There is a sharp demand for a transition from simple providing services to partnership that adds to business decision-making, increases visibility, and fuels enterprise growth.
While the legal sector is seemingly open to that change and at least 90% of lawyers are already using AI in legal operations, only 12% of adopters report getting solid returns on their technology investment. As a result, some legal teams find themselves disoriented amidst the wide-scale change and evolution of legal operations. They’re expected to move away from traditional models and metrics, but at the same time they struggle to establish new KPIs and pillars for new frameworks. How can in-house legal leaders successfully fulfill their mandate? This article explores the practices, strategies and approaches that allow in-house legal departments to discover and harness new value of AI in legal profession, transitioning to future-ready strategic enterprise partners.
AI in legal industry: Key applications for in-house legal
Similarly to many sectors, embedding AI in legal operations doesn’t mean removing all previously installed digital platforms and starting anew. Currently, in-house legal departments use AI by integrating it into already existing technology, improving functions, and augmenting results.
Over the past decade, legal services teams have made significant investments in technology to improve efficiency, streamline processes, and enhance compliance. AI doesn’t necessarily require organizations to start from scratch — it can build on what is already in place, amplifying the value of existing legal technology investments and positioning in-house legal functions for the future.
At the same time, this is true only for legal departments that already have a certain level of maturity, consistency, and discipline. AI in legal can build on what is already in place, but it cannot compensate for the absence of structure.
n-house teams need to be very careful not only about which tools they adopt, but also about how and where they apply them. If the legal function does not have clear playbooks, mapped processes, defined procedures, and a shared understanding of how work should move through the team, AI in law and legal practice will often not fix the problem. In many cases, it will simply make the problem bigger. Adding AI to chaos usually does not create efficiency — it creates faster chaos.
That is why the first step should not be technology. The first step should be clarity. Legal teams, and in most cases especially the GC, need to fully understand how the function actually works: where requests come in, how decisions are made, where delays happen, what can be standardized, and where legal judgment must stay fully human.
Once that foundation is in place, using AI in law and legal practice becomes genuinely practical. It can strengthen consistency, improve speed, and make the legal function more scalable without losing control. But if that foundation is missing, AI risks becoming just another layer of complexity on top of existing chaos.
The right starting point is not the technology category. It’s the workflow problem. In legal, the best AI use cases are usually repeatable, text-heavy, and structured enough to standardize. If a team starts with a shiny tool instead of a real pain point, adoption will usually be weak.
Once that foundation is in place, using AI in law and legal practice becomes genuinely practical. It can strengthen consistency, improve speed, and make the legal function more scalable without losing control. But if that foundation is missing, AI risks becoming just another layer of complexity on top of existing chaos.
“The right starting point is not the technology category. It’s the workflow problem. In legal, the best AI use cases are usually repeatable, text-heavy, and structured enough to standardize. If a team starts with a shiny tool instead of a real pain point, adoption will usually be weak.”
Contract lifecycle management (CLM) software
CLM software is crucial for enterprise health. Used for finding relevant contract data on time, tracking contract expiry days, and maintaining compliance, it enables legal teams to fulfill their obligations impeccably.
But just like any software, CLM tools can be too slow or too complex to manage. For example, up to 90% legal professionals reported experiencing difficulties with finding the right contract, not to mention clarifying such vital details as clauses, dates, and payment terms.
Also, manual contract review puts a major burden on in-house lawyers, with 6 in 10 legal professionals believing it to be their biggest challenge. Aside from draining resources, these complications increase the probability of human error and risk of employee burnout, which can weaken and compromise in-house legal departments in the long run.
How does AI in legal tech help?
- Accelerated contract processing
AI makes it possible to automate contract inquires. It also handles sending these contracts to relevant stakeholders, initial draft creation, and draft summarization. Additionally, AI can sift through emails to glean deal terms, providing all necessary information within a short time. - Missing clause identification
When contracts are handled manually, details get lost. An AI-powered CLM software can detect missing clauses and notify teams in-time, preventing a number of reputational, legal, and financial risks. It also can compare different contract versions, ensuring their compliance with enterprise policies and external policies, further strengthening the department’s regulatory practices.
Risk and compliance management
Risk management isn’t just about anticipating the worst outcomes. It includes thinking about both bad and good outcomes. For example, every time an enterprise decides to enter a new market or introduce a new service offering, it’s a risk – a necessary one because the successful outcome means new pools of revenue, new competitive advantages, new options.
In that case, in-house counsel must develop a strategy for evaluating smart risks and their potential impact on business. In other words, modern in-house legal teams help their enterprises navigate consistently changing regulations, laws, ESG requirements, and even geopolitical factors, so they could create new value.
The full picture on risk and compliance management for in-house counsel illustrates a rather monumental and complicated task. In-house legal professionals must stay informed about all the regulatory changes, keep their standards up to date, while providing more strategic assessment and risk-proofing risky enterprise initiatives.
The need for proactive risk management is particularly sharp in 2026 as 44% of legal professionals observe growing demand for data privacy, cross-border transaction moderations, and international arbitration. Despite this change, 80% of in-house legal departments remain confident about their ability to work around these challenges.
On top of all that, however, legal teams are also expected to manage risk at the speed of innovation, which means they must use intelligent tools like AI for greater productivity and accuracy – and they also the ones to keep their governance is up-to-date with the policies on responsible tool use.
Given this context, it may seem that AI in legal tech adds more to the workload. But this is actually an example of a smart risk: yes, in-house legal leaders must contribute to building strong AI governance and securing responsible AI use. However, if they succeed, they augment the essential part of proper risk and compliance management: visibility.
AI in risk management enables teams to look deeper into underlying processes by leveraging automated monitoring and reporting. The latter is particularly important for tracking regulatory changes – if previously legal teams had to check for new requirements personally, smart AI tools can keep them updated in real–time. As a result, legal teams have more time and opportunities to focus on strategic enterprise risks, while staying confident about their compliance.
Enterprise legal management (ELM) systems
Since legal operations involve numerous processes and tons of information, many enterprises integrate ELM systems for their in-house legal teams. These systems are used for a vast range of activities, from tracking legal spending, matter management, and vendor management to enhancing collaboration with other departments (Finance, Procurement, Business) and evaluating value in-house counsel delivers to the organization. In simple terms, ELM systems allow to scope and scale legal operations across the enterprise and convert activity into actionable data and insights.
These capabilities make ELM systems crucial for efficient legal resource optimization and strategic decision-making. However, an AI-driven ELM system makes it possible to evolve these capabilities further by introducing a more proactive approach to resource allocation and data portfolio. Through intelligent spending analysis, AI can reveal which resources can be liberated for more valuable tasks, which investments have the highest potential, and expose patterns that can affect desired outcomes.
Furthermore, AI creates an intelligent layer that encompasses contract lifecycle management, compliance, and digital data, and brings it all in front of the in-house counsel teams, giving them full view of their performance and roadmap. Such transparency enables greater confidence and allows teams to plan more proactively and act upon opportunities immediately.
There is a clear shift we can observe here: AI is moving from a technology that automates and covers repetitive processes to a strategic enabler. It dives through layers and layers of data to help in-house counseling teams build a more comprehensive and realistic picture of their operations. Since AI also takes care of bottlenecks, legal teams are free to work on what’s right in front of them, making the most out of every working hour.
The evolving role of AI in legal departments
Legal service delivery is all about managing complexities. From contract drafting to compliance monitoring, to sifting through tons and tons of digital data, legal teams are expected to handle it all fast, with precision. This is why AI shows such potential in legal: it allows teams to focus on strategies, solutions, and action rather than manual data retrieval and monotonous drafting.
First introduced to in-house legal as process accelerator and time-saver, AI in legal industry is now playing a bigger role, affecting how legal professionals see their future and approach to work. For that reason, it makes sense to dissect several trends highlighting the impact of AI across in-house legal departments.
1) GenAI use is surging
AI in the legal profession is becoming a regular addition rather than a novelty. The majority of legal experts are using GenAI for both individual and organizational purposes. Four-in-ten firms are now working with GenAI tools, which is a 22% increase compared to last year.
Meanwhile, 50% of legal professionals use publicly available GenAI platforms (Claude, ChatGPT, Gemini). Vertical AI tools tailored to legal industry specifically, are also picking up speed. The latter creates a room for opportunity for AI startup: for example, Supio announced its vertical legal AI agent and integrated it with Thomson Reuters Westlaw products within a partnership.
Such news indicates that GenAI for in-house legal is going beyond internal. It’s part of regular workflows, where teams and professionals use it weekly, daily – and this is not going to change. The need for more legal-specific tools shows that legal professionals want AI closely incorporated into what they do and how they work, which requires products that are designed in accordance with their expertise.
2) Agentic AI is in its budding phase
In contrast to GenAI popularity, agentic AI is only about to hit its wide-scale adoption stage. Currently, 15% of in-house legal professionals report using AI agents in their enterprise. Nevertheless, many legal experts believe that agentic AI is going to be vital part of their regular work by 2030, further contributing to the benefits of AI in legal operations.
However, the reason for such a disparity between GenAI and agentic AI adoption rate is much deeper than lack of knowledge or organizational resistance. The true main challenge is autonomy.
Since AI agents are more independent and capable of executing tasks without human intervention, this raises the question of control. How should AI agents and logic be tracked? Who is held responsible? What skills in-house departments are lacking for moderating AI agents? In addition, lawyers admit being uncomfortable about letting AI agents perform actions on their behalf—and this is an entirely different mental barrier to work around.
3) ROI on AI in legal remains elusive
Although AI in legal sector generates lots of positive feedback, only 18% of adopters manage to establish clear ROI metrics. These are mostly internal KPIs (employee usage, money saved), which is still far from the end goal – business-relevant KPIs (increase in new deals, client satisfaction score increase).
There is also the matter of general uncertainty: at least 40% of legal experts don’t know if or how their company measures ROI. This uncertainty signifies that numerous organizations don’t know what a successful AI adoption is all while actively adopting AI.
4) Law firms and clients see AI in legal sector differently
It’s not a secret that demand for using AI in legal comes from inside. Corporate executives are usually the ones evangelizing AI across departments. Their objective is to increase productivity and facilitate work, but it often collides with one important aspect: how to use AI when working with client information. For example, the number of firms mandating AI use is below 20%.
In addition, while clients expect their lawyers to use benefits of AI in legal operations, these expectations come complete with demand for full transparency and low tolerance for delays. 77% of law firms in the UK believe that they’ll be obliged to fully disclose how they work with AI to their clients. In other words, in-house legal departments often find themselves juggling limitations and expectations.
These trends signify one certain upcoming change – the emergence of new legal business models, where artificial intelligence is incorporated into workflows, pricing, client interactions, and mandates. These new models will be the response to ongoing challenges such as ROI issues and AI moderations, removing bottlenecks, and enabling greater synergy between lawyers and technology. However, will everybody be prepared for such a new reality?
Although many legal professionals anticipate this change, some of them are concerned about their place in the industry after the wide-scale transformation.
There is a gap between how things are now and how they will be tomorrow. Not all in-house legal teams know how they’ll get there – or how their enterprise will get them there. To break through this uncertainty, enterprise executives and in-house legal leaders must work together, towards a joined and holistic vision of new, AI-native operation models.
Charting a smart AI roadmap for in-house legal: What should GCs do?
The main takeaway – the legal industry is changing. Clients see the agility and visibility new technologies can bring, so they want to see it incorporated into services they use. Executives see their competitors get better outcomes with new tools – and they want to achieve the same, if not better results. This is what is important: people don’t want to see more AI doing the work. They want to see more high-quality work done by high-value professionals faster thanks to AI. This is a common change for many sectors. But let’s see what in-house counsel can do about it.
Expand the strategic mandate beyond cost
In-house counsel leaders who want to prepare their department for future-ready models, must address the gap between intent and the ability to deliver. Closing this gap requires GCs to provide proof of AI performance by identifying and extracting the right metrics. In order to achieve that, GCs must take a deep look at their department’s mandate – and expand it in accordance with their strategy.
Similarly, when exploring and making AI in legal use cases in search of the right fit, GCs shouldn’t focus on the cost and spending as the only priority because doing so doesn’t illustrate the full value of the solution. Instead, they need to dissect how the technology impacts performance, how well it is integrated with legal operations, and how well it contributes to established key metrics.
Keep the work human
AI in legal sector will change the way departments operate. But it’s not going to change the importance of critical thinking and human judgement. Since AI adoption for in-house legal will scale to agentic AI, GCs should prepare for new workplace dynamics where legal professionals manage several agents to automate and handle routine objectives.
AI is likely to increase the value of experienced lawyers, not reduce it. As more routine work is automated, legal teams will rely more on senior professionals to supervise outputs, define standards, and make final judgment calls. At the same time, this creates a real challenge for the profession: junior lawyers may have fewer traditional opportunities to learn through repetitive first-level work. Legal teams will need to rethink how they train future experts.
The key part here is to establish and maintain accountability by ensuring that the final results are always checked, verified, and validated by assigned human experts. Therefore, educating, upskilling and onboarding in-house legal teams on using AI agents are the objective GCs should start working on already.
Focus on data
It’s important to keep in mind—AI in legal operations is going to be part of in-house counsel infrastructure. From this angle, AI investment must focus on solutions and platforms that solve a specific structural problem. In many cases, it’s the issue of disconnected data from different document systems, e-billing, email exchanges, contract repositories, and matter management. Until this problem is solved and all data is fully AI-ready, it’s impossible to move forward.
Data architecture also raises another important question: where sensitive legal data should live, and who should control it.
Not every legal AI use case should be outsourced to a vendor environment. Where the value depends on internal legal know-how, negotiation history, playbooks, and privileged material, legal teams should think carefully about control. In those cases, the real question is not only functionality, but also whether the chosen environment provides the level of confidentiality, control, and accountability the organization needs.
Make impact visible
Data is the lifeblood of change in more ways than one. Aside from aggregating their work data, GC’s must leverage data to create value. This involves being proactive in communication and collaboration with other internal business groups, aside from CEO and CFO.
After establishing crucial success metrics and ways of monitoring them, GCs should regularly share their observations and reports with cross-functional partners and unit leaders. These insights will provide a clear view of the progress made and milestones scored and potentially bring forward new strategic insights that can be incorporated into decision-making.
When you’re a unit leader, the shift into a new business model is a collective effort. Everybody is a trailblazer, so every detail matter and solutions are found through discussions and conversations. So, when it comes to preparing an AI-ready in-house counsel, you do it from the perspective that this change will impact every function. By staying connected to their feedback and sharing your progress, you stay agile and gain better understanding of what to do and how to move forward.
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