Why legal transformation starts with the problem, not the technology

Corporate legal departments are under pressure to modernize. Generative AI, workflow automation, and legal technology platforms promise faster contracting, streamlined operations, and greater efficiency. Yet despite significant investment, many organizations still struggle to realize meaningful value from their legal transformation initiatives. 

The problem is not a lack of innovation. It is that too many legal teams begin with the technology rather than the business challenge they are trying to solve. 

According to Jana Blount, Director of Client Solutions and Digital Strategy at Norton Rose Fulbright, sustainable transformation happens when organizations resist the temptation to lead with tools and instead focus on understanding people, processes, and outcomes first. 

“The legal tech teams that win are the ones that know which problems they’re solving and build for the people,” says Blount. 

That is a deceptively simple message. In a market captivated by AI, it is also a timely one. The goal of legal technology should not be simply to help lawyers do existing work faster. It should be to help organizations make better decisions, manage risk more effectively, and create greater business value. 

Why legal tech projects fail 

The legal technology market has never been more sophisticated, yet many organizations continue to accumulate what Blount describes as “shelf-ware”: technology implemented with enthusiasm, then abandoned when it fails to gain traction. 

The reasons are familiar. Organizations automate broken processes. Ownership is unclear. Success is poorly defined. Stakeholders are consulted too late, if at all. Projects are treated as legal department initiatives rather than business transformation programs. 

As Blount puts it: “If you have a bad process, and you don’t address that first, you’re just making that faster.” 

As an example, she cites a general counsel who believed the team needed a legal intake system. On closer examination, the real issue was not intake management. It was work allocation, visibility, and internal team dynamics. No system, however elegant, could resolve that without addressing the underlying problem first. 

This is where many legal technology projects go wrong. A tool is selected before the organization has established what it is trying to change, who needs to change, and how success will be measured. 

Start with the problem, not the technology 

Blount’s experience with an airline contracting project illustrates the point clearly. The organization initially believed it needed document automation. Stakeholders assumed the problem was speed and that the answer was a platform capable of generating contracts more quickly. 

But when Blount’s team interviewed people across procurement, operations, learning and development and other business functions, a different picture emerged. Employees were not primarily frustrated by drafting times. They lacked clarity. They wanted to know when legal involvement was necessary, when they could proceed independently, and how to navigate the process with confidence. 

“The business wanted clarity,” says Blount. “They didn’t need it to be faster.” 

That discovery changed the shape of the project. Automation still had a role, but it was no longer the centerpiece. The greater value came from helping business teams understand what they could do themselves, when to involve legal, and how to engage the legal team more effectively. 

This is the difference between legal as a bottleneck and legal as a business enabler. When guidance is embedded where decisions are made, legal helps the organization move with greater confidence. It becomes part of how the business operates, not simply a function that reviews work after the fact. 

The lesson is straightforward: technology should be selected after the problem is understood, not before. 

Data, AI and the power of curiosity 

Another of Blount’s examples involved a pharmaceutical company seeking to improve intake and triage across legal, compliance, and company secretarial functions. The initial goal was not automation. It was visibility. 

The legal team lacked reliable data about work volumes, demand patterns, resource allocation, and the value being delivered across the organization. Leaders had anecdotes and instincts, but not the evidence needed to make strategic decisions. 

“It’s hard to think about where you can drive efficiency and where you should implement automation if you’re not quite sure of the volumes of work, where they’re coming from, or what they are,” says Blount. 

By designing the intake system to capture meaningful operational data, the organization created a foundation for better decision-making. It could identify demand, understand where work was going, assess where automation might deliver value, and measure return on investment with greater confidence. 

This is one of the most important opportunities for legal departments. Technology should not merely digitize work. It should generate intelligence. When designed thoughtfully, legal systems can provide insight into business demand, risk exposure, workload distribution, and organizational performance. 

That same pragmatism applies to AI. Blount is optimistic about generative AI, but she cautions against treating it as the answer to every question. “Generative AI is not always the answer,” she says. 

Some problems are better solved through document automation, deterministic workflows, machine learning, or simpler process redesign. The objective is not to force AI into every workflow. It is to choose the right tool for the right task. 

That requires curiosity. Why are stakeholders bypassing legal? Why are policies not being followed? Why does a workflow create friction? Why do people avoid a process? Why do we believe a particular technology is the answer? 

By asking those questions first, legal teams uncover root causes that technology alone cannot reveal. 

As legal departments navigate the next wave of AI adoption and digital transformation, the most successful organizations will remember that technology is not the destination. It is an enabler. 

Competitive advantage comes when people, processes, data, and technology work together in the service of a clearly defined business outcome. 

The future belongs not to the organizations with the most technology, but to those that best understand the problems they are trying to solve. 

Continue the AI conversation with the Future of In-house series.