AI in law firms
AI in legal work: drafting and reviewing documents, contract analysis, research and the hallucinated-citation sanctions that followed, technology-assisted review in discovery, what happens to the billable hour when the work takes an hour instead of ten, clients demanding the efficiency be passed on, closed deployments forced by privilege, due diligence at speed, and the duties of competence and supervision that make a partner answerable for a model's output.
AI is moving fast through legal work, from contract review to research to due diligence, but law firms have distinctive constraints: privilege, confidentiality, billable-hour economics, and partners who bear personal liability for output quality. This block builds sector-specific fluency in how AI actually functions inside a firm, not generic AI theory. You will learn where AI genuinely helps across the legal value chain versus where it creates malpractice exposure, how to evaluate vendors claiming legal-specific accuracy, what governance a firm needs before deploying any tool on client matters, and how document review, drafting, research and discovery tools differ in maturity and risk. The goal is judgment: knowing what to trust, what to check, and what to pilot before firm-wide rollout.
What you'll master
- Identify which legal workflows (research, review, drafting, discovery, knowledge management) are genuinely suited to AI versus overhyped
- Evaluate a legal AI vendor's claims on accuracy, hallucination risk and data handling before purchase
- Design a governance checklist covering confidentiality, privilege and supervision duties before deploying AI on client matters
- Run a pilot with fee earners that accounts for billing model friction and partner trust, and assess realistic ROI