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lies. That’ s the difference between a legal department that transforms itself and one that transforms the business.
The warning signs show up everywhere. AI tools are in place, but no one owns the AI strategy. Adoption happens reactively, not proactively. The department does the same work faster, but its value proposition to the business hasn’ t changed. When legal measures AI’ s impact mainly by cost savings and hours reclaimed, it’ s speaking the wrong language for the C-Suite. When executives say legal contributes little, they’ re not ignoring the work. They just don’ t see how it connects to the business.
At Thomson Reuters, the GC’ s office acts as what we call“ customer zero” for our AI products, testing them in real workflows, on real matters, before anyone else. One project gathered scattered antitrust guidance into a single resource that lawyers could draw on consistently. The payoff wasn’ t speed. It was more consistent decisions from lawyers, and a legal team the business trusted more, not just one that moved faster.
AI doesn’ t fit all legal work
There’ s another issue that doesn’ t get enough attention: Not all AI is designed for high-stakes professional work, and treating all tools as interchangeable brings its own risks. Legal work demands a standard that most AI can’ t meet. When a GC signs off on a risk assessment, a regulatory opinion, or a contract analysis, every conclusion must be traceable and defensible. What matters in these situations isn’ t how fluent the output sounds. It’ s whether the reasoning is clear, the sources are verifiable, and a subjectmatter expert reviews the work before it reaches a decision-maker. That’ s a much higher bar than most consumer-facing AI can clear, and treating the two as the same doesn’ t just risk the work product. It risks professional credibility. This difference will only matter more as AI matures.
What strategic clarity looks like
The legal departments that come out ahead will be the ones who can answer the C-Suite’ s real question. Not“ How efficient are you?” but“ How are you helping us grow, compete, and win?”
That means treating AI as a business strategy, not just an efficiency play. Success isn’ t only about time saved; it’ s about risk avoided, revenue enabled, and better decisions made faster. It also means building AI infrastructure on accuracy and accountability, making it truly worthy of the work it supports. Most importantly, it
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