AI keeps getting faster. Building it well is harder. We wrote about how LexisNexis approaches trust, quality, and collaboration when building AI systems used in legal research and decision-making, drawing on insights from Min Chen, SVP & Chief AI Officer, and Serena Wellen, VP of Product Management. We look at how long-term experience with applied AI shapes their approach today, how quality is evaluated beyond simple accuracy, and why close collaboration across product, AI, and subject-matter experts matters so much in high-stakes domains. Read the article here 👇 https://lnkd.in/eNU7nR4U
Excellent read 🚀
This is a strong step in the right direction—especially the focus on trust, quality, and cross-functional collaboration. But this is also where most of the industry is still underestimating where that trust is actually built. Trust in legal AI is not created at the model level. It’s created at the workflow level. Because by the time an attorney or system is evaluating output, the foundation has already been set—accurately or not—through intake, fact structuring, and issue framing. If those layers are inconsistent, incomplete, or unstructured, even the most advanced AI will produce confident—but unreliable—results. That’s the gap. And it’s also why paralegals and legal operations are becoming the most important control layer in AI-enabled legal work. They are the ones shaping the inputs, structuring the facts, and defining the context that determines whether AI is operating on solid ground or compounding risk. The firms that lead in this space won’t just build trustworthy AI. They’ll build controlled, repeatable workflows that make that trust possible in the first place.