How Yetter Coleman’s Jeffrey Andrews is using AI to rethink litigation, client service, and the practice of law.

Jeffrey Andrews thought he had found a good test for artificial intelligence. Andrews is a partner and intellectual property litigation practice group lead at Yetter Coleman, a Houston litigation boutique of roughly 55 lawyers. His practice spans some of the most complex corners of IP litigation, including patent, trade secret, and Walker Process antitrust matters. If AI was going to earn a place in his practice, it would have to prove itself against work he knew inside and out.

So Andrews uploaded deposition transcripts from a recently completed matter, depositions he had personally taken, and asked Harvey to summarize the testimony and identify the key themes. “I was completely blown away,” he recalls. Because Andrews knew the record so well, he could immediately judge the quality of the output. What surprised him was not simply the accuracy of the summaries, but Harvey’s ability to connect themes across testimony in ways he had not fully appreciated while immersed in the case.

“Those deposition summaries really made an impact on me,” he says. “It really was this big kind of my gosh moment.” That experience changed the way he thought about AI — not because it replaced legal judgment, but because it demonstrated a new way to apply it. Today, Harvey is simply part of how he practices. “It’s like Christmas every day.” Harvey has become part of his daily workflow, something he opens every day alongside Outlook, continually discovering new ways to use it.

Learning to trust the technology

For Andrews, trust wasn’t built through marketing claims or product demonstrations. It came from putting AI through increasingly difficult tests. After the deposition exercise, he turned to a matter that was far more personal: he uploaded a motion to dismiss he had argued and lost, along with the briefing, the court’s order, and the transcript of his own oral argument, then asked Harvey to critique his performance.

The feedback was direct. “Getting the critique from a machine who’s not worried about my feelings, it was helpful, and it did make some very good points,” he says. For Andrews, the lesson was straightforward: if lawyers want to understand what AI can and cannot do, the best place to start is with work they already know well enough to evaluate themselves.

From one use case to everyday practice

Once Harvey earned his confidence, Andrews found himself reaching for it in more places. Some of those applications are substantial, such as organizing case law into structured review tables to develop and pressure-test arguments. Others are smaller, but no less valuable. He points to the challenge of fitting a highly technical brief within a strict page limit — those are “the small things with a huge value that would normally take so much time to refine down the language,” he says.

The same pattern has repeated elsewhere in his practice. Preparing client presentations that once required nearly a week can now be completed in roughly a day and a half, a shift that prompted Yetter Coleman to create its own PowerPoint style guide so presentations retain the firm’s visual identity. On Sunday evenings, Andrews runs a workflow that scans the web for the latest developments and ranks them against a rubric he built, helping him flag issues worth raising with clients before they call him. None of these examples, on their own, changed his practice; together, they changed how he thinks about it.

Practicing law beyond the office

One of Andrews’ more practical habits illustrates how AI has become part of his daily routine: while walking his dog at night, he dictates legal questions and ideas into the Harvey mobile app so they are waiting for him when he returns to his desk. The workflow is simple, but it reflects something larger — legal thinking happens throughout the day, not only while sitting in front of a computer.

Asked whether Harvey has created more free time outside the office, Andrews laughs that it has mostly given him “more time to do more work.” For someone who genuinely enjoys practicing law, that sounds less like a complaint than an opportunity.

Expanding what a lean team can take on

The clearest example of AI changing Andrews’ practice came through a factually intensive contingency-fee matter. He loaded gigabytes of client emails and documents into Harvey and used it throughout the lifecycle of the case, from early document review and claim development to timeline construction, venue analysis, and drafting the initial complaint. He estimates the work would traditionally have required a team working for roughly six weeks; instead, he completed much of the analysis himself over evenings and weekends. “I can’t even quantify how much time it would have taken to do this on the front end without Harvey,” he says.

For Andrews, the experience changed the economics of the matter. Work that once required weeks of coordinated effort from a larger team became feasible for a lean litigation practice. The same approach has strengthened the firm’s business development efforts. On the defense side, when a client has just been sued, Andrews can pull file histories for multiple patents into Harvey to quickly surface relevant arguments, allowing Yetter Coleman to respond to opportunities with greater speed and depth.

Bringing clients along

As AI has become more common in legal practice, Andrews has found that client conversations have evolved as well. Some sophisticated corporate clients want to understand how AI is being used and how it improves efficiency on their matters. Others, particularly privately held companies with heightened data privacy concerns, want to understand how their information is protected; in those conversations, Andrews walks clients through Harvey’s security portal alongside Yetter Coleman’s own AI policies and guidelines. For plaintiff-side clients, the discussion is different: he wants them to understand that what they are paying for is the firm’s legal judgment and skill, not routine work that technology can perform more efficiently.

The result is not only greater transparency, but also greater responsiveness. Instead of promising an answer the following day after receiving a large document production, Andrews is often able to respond within a couple of hours.

Leading by example

As Andrews’ experience has grown, he has naturally become Yetter Coleman’s go-to resource for Harvey. Through the firm’s AI committee, he shares workflows, prompts, and practical examples that colleagues can immediately apply in their own practices. He is equally candid about the challenges. Like many experienced lawyers, he wonders how junior associates who never spent years reviewing boxes of documents will develop the same issue-spotting instincts as previous generations.

At the same time, he sees a familiar pattern. His own career spanned the transition from books to electronic legal research, and today’s associates will build different skills while adapting to a different set of tools. Rather than replacing foundational legal thinking, Andrews believes AI gives younger lawyers an opportunity to “focus on a higher level of thinking” earlier in their careers.

His advice for lawyers who remain uncertain is deliberately practical: start with work you already know well enough to evaluate, exchange ideas with colleagues, and set aside time to experiment without the pressure of a client deadline. His next experiment is already in mind — he would like to moot an upcoming hearing with Harvey playing the role of the judge, trained on that judge’s prior transcripts and opinions. For Andrews, that feels like a natural next step. The real story is not the technology itself. It is the mindset behind it: the willingness to stay curious, keep experimenting, and never quite be finished improving.

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