At a recent New York State Bar Association panel on artificial intelligence, U.S. District Judge Jesse Furman of the Southern District of New York made an observation that, until recently, would have sounded premature.
“I heard somebody say employers are risking malpractice by relying too much on AI,” he said. "I think there may come a point where it's the opposite—where you're committing malpractice if you don't incorporate AI into your practice."
To make the point concrete, Judge Furman offered the following scenario: a client may challenge an attorney’s fees because “thousands of hours” of work “could have been done in 30 seconds by an AI tool.”
Whether or not his prediction proves true, it captures a shift that legal teams are already confronting.
The Risk Calculus Is Inverting
For much of the past two years, teams approached AI in litigation workflows with caution. And, for good reason. Early tools raised legitimate concerns around hallucinations, confidentiality, bias, and lack of transparency. As a result, treating AI as a liability was a rational response.
Over time, however, legal technology providers moved to address these risks via architectural and infrastructure decisions by constraining models, improving retrieval, facilitating clearer auditability, and tightening controls around data handling. While no product is perfect, today’s legal AI tools are materially more reliable than their early predecessors.
As these changes took (and continue to take) hold, the question facing legal teams shifted from “what could go wrong if we use AI?” to “what risks do we incur by choosing not to?”
In patent litigation, for example, this shift appears to increasingly show up in what clients look for: faster delivery of final work product, portfolio-wide analysis across hundreds or thousands of patents, and strategic insight that compounds across a client’s matters—prompting many legal teams to rethink how they articulate value to their clients.
These expectations, however, place greater, not lesser, weight on human judgment.
Attorneys still win cases by developing claim construction strategies, understanding how prosecution history shapes infringement positions, evaluating which prior art will withstand scrutiny, spotting what opposing counsel missed, and building a winning litigation narrative. AI does not replace this work.
Instead, AI-native workflows change what becomes feasible before attorneys commit to a position. They allow teams to test, iterate, and discard competing theories at a capacity that was previously impractical. That principle guides how we build at &AI.
The Reality
Judge Furman framed the foregoing issue as a potential malpractice question. Even if it never becomes one, it already functions as a competitive constraint.
Patent litigation teams that adopt AI-native workflows move faster. They survey more prior art, pitch with draft claim charts rather than estimates, and identify portfolio-wide patterns that once took weeks to uncover.
The standard of excellent legal service is shifting. &AI supports patent litigation teams as they adapt. Book a demo to try it for free.
Source: Mike Vilensky, “NY federal judge questions if avoiding AI could be malpractice,” Bloomberg Law (New York Brief), Jan. 13, 2026.
Frequently asked questions
Could avoiding AI become legal malpractice?
At a New York State Bar Association panel, U.S. District Judge Jesse Furman of the Southern District of New York suggested there may come a point where you commit malpractice if you do not incorporate AI into your practice. He offered a scenario in which a client challenges an attorney's fees because thousands of hours of work could have been done in 30 seconds by an AI tool.
How has the risk calculus around AI in litigation changed?
For much of the past two years, treating AI as a liability was a rational response given concerns around hallucinations, confidentiality, bias, and lack of transparency. Over time, legal technology providers addressed these risks by constraining models, improving retrieval, facilitating clearer auditability, and tightening data-handling controls. As a result, the question has shifted from "what could go wrong if we use AI?" to "what risks do we incur by choosing not to?"
Does AI replace attorney judgment in patent litigation?
No, these expectations place greater, not lesser, weight on human judgment. Attorneys still win cases by developing claim construction strategies, understanding how prosecution history shapes infringement positions, evaluating which prior art will withstand scrutiny, spotting what opposing counsel missed, and building a winning litigation narrative. AI does not replace this work.
How do AI-native workflows change patent litigation?
AI-native workflows change what becomes feasible before attorneys commit to a position, allowing teams to test, iterate, and discard competing theories at a previously impractical capacity. Teams that adopt them move faster, survey more prior art, pitch with draft claim charts rather than estimates, and identify portfolio-wide patterns that once took weeks to uncover.
What are patent litigation clients increasingly looking for?
In patent litigation, clients increasingly look for faster delivery of final work product, portfolio-wide analysis across hundreds or thousands of patents, and strategic insight that compounds across a client's matters. This shift is prompting many legal teams to rethink how they articulate value to their clients.
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