The most important shift I’ve experienced this year hasn’t come from any single product or feature. It’s come from an understanding that AI doesn’t belong to a separate category of work. Gone are the days when a lawyer would walk into her office, open a case file, and methodically work through research, drafting, and review in rigid sequence. AI isn’t a separate technology that lawyers occasionally deploy—it’s now integrated into how professional judgment is developed and applied. These tools work much faster than we do, but they still need us to make sure they get things right.
While legal teams don’t need to engineer these models, they must understand how they perform under legal scrutiny. The practitioners who are thriving aren’t treating AI as an advanced database– they’re dancing with it. Lawyers, who never had to work cross-functionally, are now being challenged to collaborate with machines from an entirely different discipline. In this dance between human and machine, legal expertise isn’t diminishing—it’s finding new rhythms.
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In that process, the lawyers who once dreamed of alternative careers as artists or designers now have another shot at it! Now lawyers need to think like designers—something law school never taught. Working with AI requires the same intuition an artist brings to a blank canvas. Be too vague, and the system creates something unhelpful. Be too controlling, and you miss unexpected, game-changing insights. The skill lies in providing just enough structure to guide the machine while leaving room for it to surprise you with possibilities you hadn’t considered.
A structural shift
This year, I’ve noticed that the most effective legal teams aren’t avoiding AI—they’re architecting workflows that treat it as an accelerant rather than a replacement. AI has been a blessing for those who always despised the rigid linearity of traditional legal thinking, because AI demands iterative, experimental approaches. They still maintain high standards, but now AI helps with first drafts, organizing information, and checking their work against past cases. This breaks up the old linear way of doing things and gives them more time for the strategic thinking that really matters.
There’s also been a shift in how legal professionals talk about risk. When AI tools become integral to daily practice, the boundary between legal advice and technical implementation becomes more fluid. Beyond reviewing contracts and compliance obligations, lawyers now find themselves evaluating how automated systems make decisions and present information to users.
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The lawyer who once spent hours in the law library researching precedent should now spend equal time understanding model behavior and data provenance. The systems generating recommendations, especially legal-grade, must be examined with the same rigor applied to witness testimony.
Learning and unlearning
What I’ve learned is that legal judgment depends on context. Interpretation shifts with scale and timing, and that variability still holds in an AI-enabled workflow. The margin for error has narrowed and outputs arrive faster and in greater volume. But the responsibility for determining what stands – legally and ethically – remains with us.
This year has been one of learning and strategic unlearning across the legal field. The margin for error has narrowed, even as outputs arrive faster and in greater volume. The lawyer who once built her reputation on speed now sees that speed alone doesn’t define value, and precision without perspective leads nowhere. The very structure of legal work has changed—from teams of lawyers to teams of lawyers and their AI tools collaborating. Legal problem solving now begins with design-driven questions:
What matters to the client and stakeholders?
Who has leverage?
Who are we protecting?
Why does this matter?
The tools will keep evolving, but so too will the teams building the infrastructure around them.
That’s where meaningful progress is happening: not in automating expertise, but in redefining how it’s applied.
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