AI has been less of a slow burn and more of an … overnight sensation. In the legal data industry specifically, there’s been consistent speculation around legal AI tools, from their risks to their sheer potential. Yet somewhere beyond the hype, pomp, and circumstance, are tangible—and invaluable—insights on how to navigate this thing.
As a global specialist risk consultancy, Control Risks is no stranger to the ways in which legal data professionals are actually using AI in practice. Their podcast, In Re:, provides an accessible platform for real experts across the world to share insights on complex issues facing legal and compliance professionals today.
In a recent podcast, Sushmit Bhattacharya (Principal, Forensics Practice) and Stuart Hall (Principal, Discovery + Data Insights, Asia Pacific) sit down to share Control Risks’ outlook on the future of legal generative AI and pull back the curtain on practical use: from adoption to strategic mastery.
The History of AI in Legal Data and Technology
The advent of AI is not the legal industry’s first technological rodeo. Over a decade ago, this community witnessed TAR transform document review and set the stage for modern e-discovery. Today, generative AI has arrived on an even larger scale, making its place in the “engine room” and powering tools like Relativity aiR.
As AI moves from differentiator to baseline, embracing it matters. The question is no longerwhether to adopt AI, but how to deploy it for maximum impact. Stuart Hall sees adoption as increasingly necessary: “It’s really important in order to remain relevant and provide the outcomes that clients expect.”
Generative AI: a Survival Tool
In a landscape defined by mounting data volumes and relentless deadlines, AI has become a survival tool. As the Control Risks team observes, “More and more, even over the last six months, what we see a lot is investigations have to be completed under extremely tight deadlines. And we’re seeing an increasing amount of data that needs to be analyzed as part of these investigations. Using AI at a few different points can really help speed up the investigation significantly and get the case team information as quickly as possible.” The most advanced legal teams are leveraging AI’s capabilities to stay ahead, turning it into a critical advantage. They highlight a few areas (among many) where Control Risks uses AI for increased efficiency.
How legal AI creates efficiencies:
- Early case assessment: AI accelerates strategy by surfacing patterns and insights beyond traditional filters (such as date restriction and search terms) enabling smarter decisions before review begins.
- First-pass review: Replacing or supplementing human first-pass review with AI, accelerates workflows and adds rationality and transparency that demystifies the process.
- Privilege and issue tagging: AI can identify privileged content and classify documents by issue or topic at scale, improving accuracy and defensibility.
- Contract and clause analysis: LLMs streamline contract review by extracting key clauses, spotting anomalies, and supporting compliance checks across large volumes.
Perhaps what makes generative AI so remarkable—aside from improving repetitive tasks—is its sheer consistency. Humans are naturally inconsistent. AI, on the other hand, has limitless inhuman speed and pinpoint accuracy, but lacks one crucial thing: human expertise.
Stuart notes the massive advantage of blending the two: “Humans have some fallibility and inconsistency in terms of when, where, and how consistently they make their decisions. Sometimes at the start of the day you work more effectively than at the end of the day, which is sort of human nature. AI doesn’t experience those same lapses in concentration or judgment.”
What Concerns Do Legal Data Professionals Have About AI?
Despite AI proving valuable throughout everyday legal work, the promises of AI must be balanced with the realities of regulatory scrutiny, data complexity, and risk. Legal and compliance professionals are right to be concerned about the looming issues of cost, privacy, or hallucinations.
For Control Risks, avoiding this kind of risk looks like asking the following crucial questions of any potential technology:
- Is it transparent and defensible?
AI outputs should be explainable and backed by clear reasoning. Look for tools that provide natural language rationale and statistical validation of accuracy to avoid hallucinations and ensure decisions can withstand scrutiny. Avoid those that generate claims without citations or fail to show their work.
- Is it worth the investment?
Before adoption, legal teams should quantify ROI, factoring in downstream savings, efficiency gains, and risk reduction to ensure the investment is defensible.
- Is it secure?
Using closed models—and knowing where your data resides—helps teams avoid risk and meet regulatory standards. Stuart adds: “The point is to avoid models that send your sensitive data back up to the broader LLM and instead look for AI that draws inferences based on what’s present on the face of the document itself.”
Navigating the New Terrain in Partnership
Bespoke Uses
Control Risks stands at the forefront of AI innovation—not just theorizing its potential but actively engineering bespoke solutions that solve real pain points. The team regularly builds custom dashboards, wrangles global data sets, and delivers practical tools that transform their daily workflows. One example of this is Control Risks’ custom chronology dashboard, which enables investigators to visualize timelines and relationships across massive document sets, dramatically accelerating case strategy.
These kinds of customizations wouldn’t be possible without advanced generative AI solutions like Relativity aiR. As a standard, Control Risks prioritizes AI tools that are transparent and defensible. The team looks for solutions with strong prompt design, built-in safeguards, and risk mitigation strategies.
“It’s important to work with groups that you know have validation,” Stuart stresses, which is why Control Risks leverages RelativityOne worldwide—a tool with statistical validation baked into the product design.
Partnerships like Relativity and Control Risks empower legal teams to move beyond one-size-fits-all technology and drive meaningful results with innovation at the steering wheel.
Expanding Use Cases
Just as new opportunities continue to emerge within the nitty-gritty of e-discovery, the value of AI is actively reaching far beyond document review. While e‑discovery remains a core proving ground for AI, legal and compliance teams also apply generative AI to broader data challenges—from proactive risk monitoring and internal investigations to large‑scale contract intelligence and operational analytics.
AI is fast becoming a strategic tool for anticipating issues, uncovering hidden patterns, and driving smarter decision‑making across the entire legal data lifecycle.
A Future of Harnessing AI
In an industry with increasing pressure and scrutiny, success belongs to teams that can balance innovation with defensibility—but also to the teams who are willing to say yes.
Control Risks’ approach—grounded in transparency, expert oversight, and tailored technology—shows what’s possible when generative AI is deployed thoughtfully. With the right tools in the right hands, legal professionals can move beyond the noise and harness AI as a true competitive advantage.
Celia Stevenson is a member of the marketing team at Relativity where she serves as a copywriter.
