Key Takeaway: In house legal teams won’t gain ROI from AI tools alone. Real returns come from aligning AI to workflows, data, and governance, then evolving processes and leadership expectations as demand and risk increase.

The debate over AI for in-house legal teams is no longer theoretical. A year ago, many departments were still weighing the technology’s potential against concerns about risk, reliability, and governance. Now, adoption is moving from question to practice. 

Whatever hesitation remains, the question is no longer ‘Should we use AI?’ AI is an operational inevitability, and ‘How do we implement AI?’ is the most pressing question. Those who hesitate will fail to keep pace.

There Is No One-Size-Fits-All AI Strategy for Legal

Despite that consensus, agreement on best practices for adoption is in short supply. As much as in-house teams may pine for industry benchmarks, maturity models, or standard playbooks, the reality is that the wide variations in internal support, budget, technical capability, and enterprise tooling preclude a one-size-fits-all approach. 

Some organisations draw on dedicated internal engineering or innovation resources. Others are taking a more self-directed approach. The core point is that a successful AI strategy begins with clarity of an organisation’s actual circumstances rather than an idealised vision of what legal transformation should look like. 

In practical terms, this entails focusing on relevant AI use cases. These cases may involve legal operations and workflow, contracting and commercial, litigation and investigations, compliance and regulatory, and more. Each of these cases delivers rapid ROI depending on the organisation, its current state, and available resources. 

For example, a litigation and investigations use case may summarise briefs, filings, and transcripts. It may also support early case assessment and pattern anomaly and detection. Among other factors, this results in a 90% reduction of document review hours, shorter ramp-up hours, and an increase in consistency.

In the spirit of ‘start where you are,’ the sensible approach requires understanding what tools are available, identifying realistic opportunities, and building a path that reflects the team’s operating environment.

Stop Chasing the “Best” AI Tools

In operationalising AI, distractions abound. The market is full of impressive demonstrations, ambitious claims, and emerging terminologies, particularly around agentic AI. The idea of a universal “best” is an illusion; what matters is what fits your circumstances and needs.

For some legal teams today, harnessing the capabilities of agentic AI is a reward for disciplined operational maturity. For others, generative AI will remain the better choice because it is easier to adopt and govern, and it is entirely sufficient for the task. The core question is not which technology sounds more sophisticated, but which one really solves the problem at hand.

AI ROI Starts With Workflows, Data, and Governance

That emphasis on the business problem leads to another key learning point: legal teams must think about information architecture before AI. New tools can be compelling, but they only create value when they sit on top of a clear objective, a repeatable workflow, and a fortified data framework. If the process is poorly defined or the underlying information is fragmented, AI will at best produce inconsistent results and at worst create new risks at scale. 

For that reason, adoption is less about software selection than about the disciplines that must underpin success: structured data, integrated workflows, good governance, and clarity of purpose. In practice, these foundations matter more than the tool itself.

This foundation-first mindset also explains why AI presents a genuine investment opportunity for legal teams. A budget for legal operations and technology is often difficult to secure, but AI has opened a window in which investment conversations are easier to start. 

The important point, however, is not simply to buy an AI tool while enthusiasm is high. It is to use the current momentum to strengthen the underlying systems, structures, and governance that legal teams will need in any case. Postponing harder work like fixing broken processes, fragmented data, and unclear ownership to focus on the right tool is a recipe for catastrophe. AI in legal rarely fails quietly. 

In-house leaders need to conceptualize AI funding as a route to improved data architecture and operational design, not merely a means to acquire visible functionality. That may prove to be one of the most durable benefits of the current wave of interest.

AI Expands Legal Demand Before It Reduces Work

Legal leaders are increasingly recognising that AI will not reduce legal work in any simple or linear way. While certain tasks may become faster or easier, AI is equally likely to generate new forms of demand. 

Legal and compliance teams are facing a higher volume of requests relating to acceptable use, procurement, governance, contractual terms, data handling, and regulatory risk. They may also see increases in complaints, disclosure requests, or internally escalated issues because AI makes it easier to create, analyse, and submit information at speed. This means the real challenge is not merely automation, but adaptation. Teams require different skills and workflows, and greater operational agility to manage a changing profile of legal demand.

What AI Adoption Changes for General Counsel

That challenge is particularly acute for general counsel (GCs) who are being pulled in two directions at once. GCs are now accountable not only for improving efficiency within their own function, but also for managing risks outside of Legal. 

This balancing act is one of the defining features of legal leadership in the current moment. As tools become more capable and more autonomous, the governance challenge becomes more complex. Governance failures may surface months after implementation. The danger does not lie only in obvious failures, but in small inaccuracies or weak controls that pass unnoticed and accumulate over time. Effective oversight must be responsive, practical, and capable of evolving as technology changes.

Navigate an Evolving Market With Confidence

Looking ahead, the legal technology market is likely to become less neatly segmented than it is today. Enterprise platforms are developing more legal-specific capability, while specialist providers are expanding across adjacent use cases and workflows. 

At the same time, buying models are likely to change. Long software commitments may become harder to justify in a market evolving this quickly. Data portability will become more important as buyers seek to avoid lock-in, and legal teams will need a much clearer understanding of their own data as volume increasingly shapes pricing and performance. 

Chasing the latest AI trend won’t unlock the real value for legal teams. Success comes from strong foundations, clear objectives, and disciplined adoption. The next phase of legal AI will reward teams willing to move beyond experimentation and confront their own operational weaknesses head-on.

Learn more about Epiq Legal Department Advisory.



Joy Saphla, Senior Managing Director, Legal Solutions
Joy brings 30 years of experience in the legal services industry, innovating alongside some of the world’s most respected corporate legal departments and law firms. She has made a notable impact in the industry, empowering clients to transform their business performance through strategic planning and reimagined approaches leveraging people, processes, technology, and data insights.