Edith Heroux

The transformation of corporate law practices through artificial intelligence is no longer a futuristic concept—it's happening now, reshaping how firms like Baker McKenzie and Latham & Watkins handle everything from contract lifecycle management to e-discovery. Yet for many legal professionals, the actual mechanics of how AI systems integrate into existing workflows remain opaque. Understanding the behind-the-scenes architecture of these implementations is crucial for firms looking to modernize without disrupting billable hours or compromising client service quality. At its core, Legal AI Implementation involves three foundational layers: data infrastructure, model training on legal-specific datasets, and integration touchpoints with existing case management systems. Unlike generic business automation, legal AI must navigate jurisdictional nuances, interpret precedent-based reasoning, and maintain strict confidentiality protocols. The implementation process typically begins wi...

Three years ago, our mid-sized corporate law practice faced a crisis that many firms know too well: mounting document review costs, associates drowning in due diligence work, and clients increasingly questioning our billable hours. We had reached an inflection point where traditional approaches to contract analysis and discovery management were no longer sustainable. The decision to explore generative AI wasn't born from technological enthusiasm—it came from operational necessity. What followed was a journey that fundamentally transformed how we deliver legal services, though not without missteps, surprises, and lessons that reshaped our understanding of what modern legal practice could become. The initial catalyst came during a particularly complex merger and acquisition due diligence project. Our team was reviewing thousands of contracts under an aggressive timeline, and the pressure was causing quality concerns and associate burnout. A junior partner suggested we investigate Gen...