Rachel Shields Williams, president of the Legal Marketing Association, draws a striking analogy between navigating legal technology adoption and swimming in the ocean. Her comparison underscores a central tension facing law firms today: the need to harness artificial intelligence and knowledge management systems while avoiding the undertow of implementation complexity and data risk.
The ocean metaphor captures a real problem. Law firms operate in an environment where currents run deep and unpredictable. Legal technology vendors proliferate. Client expectations for AI-driven research and document automation intensify. Meanwhile, knowledge management systems sit underutilized in many firms, containing institutional expertise that remains siloed and inaccessible. Adding AI tools without first organizing that knowledge creates chaos rather than efficiency.
Williams addresses a practice management reality that consultants and legal technologists increasingly recognize: artificial intelligence cannot function effectively without a stable foundation. Machine learning models require clean data. They need structured repositories. They demand clarity about who owns what information and how that information flows through the firm. Knowledge management forms the substrate. AI provides the acceleration.
The LMA, which serves legal marketing and business development professionals across the profession, positions knowledge management as foundational infrastructure. This perspective matters because it reframes the typical vendor sales pitch. Legal technology buyers often hear promises about AI-driven legal research, predictive analytics, and automated contract review. Those capabilities offer real value. But they require prerequisite work: data governance, taxonomy development, information architecture design, and change management.
For in-house legal departments and law firms evaluating technology investments, the implication runs clear. Before purchasing the latest AI research platform or contract intelligence system, organizations must audit existing knowledge repositories. What information exists? Where does it live? Who can access it? How current is it? These unglamorous questions determine whether AI tools will amplify institutional knowledge or amplify institutional chaos.
The ocean swimming analogy also suggests risk. Strong currents can pull swimmers away from shore. Similarly, firms that rush into AI adoption without knowledge management discipline face several dangers. Generative AI systems hallucinate. Without reliable source documents and organized reference materials, these systems become unreliable. Compliance risks emerge when firms cannot track which versions of procedures, precedents, or client information their AI systems trained on. Client confidentiality concerns intensify if firms feed sensitive materials into commercial AI platforms without proper contractual protections and data handling protocols.
Williams frames legal tech adoption as requiring both caution and courage. Firms cannot ignore artificial intelligence. Client competition, lawyer productivity demands, and cost pressures make some level of technology adoption mandatory. But reckless adoption, particularly AI deployment without proper knowledge foundations, creates liability faster than it creates value.
The practical takeaway for legal departments and firms: map your knowledge management landscape before deploying AI. Evaluate what institutional knowledge you possess, where it resides, and how accessible it is to teams who need it. Deploy knowledge management infrastructure systematically. Only then layer AI capabilities on top of that foundation. This sequencing requires patience. It conflicts with vendor timelines and competitive pressure. But it produces sustainable advantage rather than technical debt.
Legal technology adoption accelerates across the profession. The firms that navigate this transition most successfully will be those that understand, like swimmers in the ocean, when to push forward and when to respect the currents.