Why Legal Is Adopting AI Faster Than Engineering

In regulated enterprises, legal teams are outpacing engineering on AI adoption. The driver is not confidence. It’s fear, and understanding why reshapes how transformation should be led.

In regulated enterprises, legal teams are outpacing engineering on AI adoption. The driver is not confidence. It’s fear, and understanding why reshapes how transformation should be led.

The largest cohort of ChatGPT Enterprise adopters at Endava was not engineering. It was legal. Joe Dunleavy, Regional CTO Europe, Endava, mentioned this at Canvas, Miro’s annual conference, in May, almost in passing, as if he had already stopped being surprised by it. The legal team had outpaced engineering in adoption rate. The CLO had publicly championed it. His explanation was direct: they felt most threatened, so they leaned in hardest. When you hear that for the first time, it sounds wrong. Engineering is supposed to lead on technology adoption. Legal is supposed to slow things down. That is the mental model behind most corporate AI strategies, and it is producing the wrong transformation results.

The Strategy Built on the Wrong Assumption

The standard AI governance posture says: move carefully in regulated functions, accelerate in technical ones. Legal carries risk. Compliance is a constraint. Engineering is where things happen. That assumption produces a transformation program with a predictable shape. Engineering gets the tools, the sandboxes, the early access programs. Legal gets guardrails, review queues, and the final word on what is permissible. The architecture of AI adoption mirrors the architecture of AI caution. The assumption feels sensible. Most transformation leaders have built their programs on it. And across the practitioner circuit, the same pattern keeps appearing: legal, compliance, and regulatory functions are outpacing engineering in adoption rate, not trailing it. Not because they were told to. Because they had more reason to. \[DOUGLAS: Add CLO/GC voices from the dinner network here. Jamie Gardner, Bob Taylor, or others who have described this pattern in their own organizations would anchor the cross-company signal. The Endava data point is the lead; these voices add the regulated-enterprise confirmation layer.\]

Why Fear Moves Faster Than Curiosity

Tomer Cohen runs product at LinkedIn. He has been rolling out AI inside one of the world’s largest professional networks while simultaneously watching how AI is reshaping the labor market it tracks. At a working session in May, he described how he approaches organizational change. “I started treating the organization as an emotional beast,” he said. “It’s afraid. I had to find a different way to get to people.” He drew on his experience rolling out LinkedIn mobile in 2012 as the template. The pattern was the same: people do not respond to transformation through information about the upside. They respond through their relationship with what feels at stake for them personally. Getting to people means getting to the stakes, not the facts. The emotional beast frame matters here because legal is not slow to adopt AI for lack of curiosity. Legal is fast to adopt AI because it is afraid. And fear turns out to be a more reliable adoption driver than enthusiasm, when it is channeled rather than suppressed. Most AI transformation programs are designed to build curiosity. Find the early adopters, show visible wins, create the positive pull. That is the right strategy for a technology that looks like a productivity upgrade. It is the wrong strategy for a technology that looks like an existential threat. For engineering, AI mostly looks like an upgrade. More capable tools for the same job. The core identity, building things, remains intact. Individual productivity goes up and the job title stays the same. For legal, AI does something different. It does not upgrade the job. It redraws it. Contract review, document analysis, legal research, discovery review: the knowledge-acquisition work that defines the first decade of a legal career is collapsing in time and cost. A first-year associate who spent three years learning to do contract review is watching a model do it in seconds. That is not a productivity gain narrative. That is an existential one.

What Existential Stakes Do to Behavior

The Perception Gap research from Gartner captures a related dynamic from a different angle. Executives who authorize AI investments are four times more likely to report high productivity gains. Individual contributors are five times more likely to say AI made no difference at all. Executives have cognitive skin in the game in one direction: the investment has to be working, because they staked credibility and budget on the claim that it would. Individual contributors have skin in the game in the opposite direction: if AI really does work as well as leadership claims, the implications for their own role are uncomfortable. Legal professionals are living inside a specific version of that second dynamic. They read the same news their executives do, but they parse it differently. When a major firm announces it has automated contract review, the partner sees efficiency gains. The associate sees the on-ramp to the partnership track disappearing. Here is what the pattern across the dinner and practitioner network tells us: that fear, when it is acknowledged rather than suppressed, produces faster adoption, not slower. Legal professionals at organizations that are leaning in are not waiting to be trained. They are self-deploying, experimenting, building fluency, often ahead of the functions that the transformation program designated as the leaders.gart They are doing this because not learning feels more dangerous than learning. The calculus of the emotional beast is simple: if this technology is coming for my work, then not knowing how to use it is riskier than the awkwardness of learning it under pressure.

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The Governance Inversion

Most organizations have built their AI governance posture around the assumption that legal needs to be protected from AI. The standard risk framing holds that legal AI deployment carries higher stakes and therefore needs more oversight, more review gates, more safeguards. This framing is not wrong about risk. Legal work does carry high-stakes consequences, and governance matters. Moffatt v. Air Canada, 2024 BCCRT 149, and the EEOC’s 2023 settlement with iTutorGroup establish real precedent for what happens when AI governance fails in regulated contexts. But the governance-as-protection posture has a side effect that most transformation leaders miss. When you position legal as the brake and engineering as the accelerator, you systematically apply the most friction to the function with the most adoption motivation. The people most willing to change are the ones working against the most resistance. The people with the least existential urgency are the ones getting the most runway. The move Joe Dunleavy described at Endava cuts the other way. The legal team’s outpacing of engineering was not the result of a lighter governance touch. It was the result of a CLO who decided to publicly champion AI adoption rather than treat it as a risk to manage. The signal from the top of the function changed the emotional calculus for everyone in it. Fear of obsolescence became permission to act rather than reason to wait. That is a different governance posture than most AI leaders are running. It is not removing guardrails. It is removing stigma. It is the function’s own leadership standing in front of its people and saying: we see what is coming, and we are going to lead through it.

What This Means for Running Transformation

Most AI transformation programs are designed around the willing. Find the early adopters, give them tools, make the wins visible, use those wins to pull the skeptics. This is a reasonable strategy for functions where the dominant emotional posture is curiosity or mild interest. It is the wrong strategy for functions running on existential stakes. When the dominant emotional posture is fear, early adoption is not about enthusiasm. It is about self-preservation. You do not need to find the willing in legal. You need to create the conditions where the fear becomes productive action rather than paralysis or quiet non-compliance. Three patterns show up consistently in organizations where this is working. The first is naming the stakes publicly. When the CLO at Endava championed AI adoption, they were not pretending the displacement risk was not real. They were acknowledging it and choosing to lead through it. The acknowledgment is what made it safe for the team to engage rather than protect territory. The second is giving the function genuine design authority over the deployment. Most transformation programs make legal a reviewer rather than a designer. The function approves or objects; it does not build. This is both a governance failure and a motivation failure. The legal professionals with the most at stake in how AI gets deployed in their function should be co-designing the deployment, not signing off on it after the fact. The third is connecting the individual adoption story to the function’s future. Training programs that lead with efficiency miss the emotional layer entirely. The frame that lands in regulated functions is not “here is a tool that will save you time.” It is “here is how you stay relevant in a function that is being redrawn.”

What Engineering Gets Wrong

This is not an argument that engineering is approaching AI transformation badly. Engineering is doing AI transformation the way you do it when the primary stakes are capability, not survival. What engineering gets wrong, in most organizations, is assuming that its own relationship to AI is the default relationship for everyone else. The enthusiasm that comes from seeing AI as a force multiplier for your core skill does not transfer automatically to functions that see AI as a force replacement for their learned work. Transformation leaders who come primarily from product and engineering backgrounds tend to design programs that assume curiosity as the starting point. Legal, compliance, and other regulated functions often arrive at the same transformation program with a fundamentally different starting point, and those programs do not serve them well. The smarter move is to design different entry points for different stakes. The emotional beast responds to different things in different functions. Acknowledging that is not a concession to fear. It is a recognition that transformation is a human problem before it is a technology problem.

The New Friction in Action

Tomer Cohen’s emotional beast frame is not a metaphor for organizational irrationality. It is a description of how people respond to change that carries personal stakes. You cannot get to the other side of that fear with information, training curricula, or pilot result dashboards. You have to work with the fear, not around it. Legal’s faster-than-expected adoption rate is not an anomaly. It is the emotional beast behaving exactly as it should: moving fast when the stakes are real, when the leadership of the function gives it direction, and when the alternative to moving feels clearly worse than the discomfort of moving. If you are running an AI transformation program and your legal function is in the slow lane, the question worth asking is not “how do we get legal moving faster?” It is “have we made it safe for legal to run toward what it is afraid of rather than away from it?” The answer to that question is not a training program. It is a facilitated conversation, with the right people in the room, about what the function wants to become in a world where its historical on-ramps are contracting. That conversation is the work. That is the entire work. If your organization is ready to have it, the New Friction framework is where we start. Read the primer or reach out if you want to talk through what this pattern looks like in your specific context.