Will Robot Lawyers Upend How We Practice Law?
A landmark study from the University of Minnesota demonstrated AI’s untapped potential to supercharge productivity and access to legal services. When students paired chatGPT-like tools with routine legal work, analysis improved as completion times rapidly plunged. As courts and firms race to keep pace with tech, how might this disrupt conventional lawyer education, training and hiring down the line?
- AI dramatically sped up task completion for student lawyers across experience levels
- Tools moderately boosted quality of legal briefs and filings
- AI’s gains were higher for less skilled students
- Participants reported satisfaction collaborating with legal AI
- Lawyers may someday be trained and hired based on AI fluency
A recent study from the University of Minnesota Law School demonstrated how AI tools like chatGPT could significantly boost productivity for lawyers-in-training. Students completed typical legal tasks with and without AI assistance – and across skill levels, AI dramatically reduced task completion times while modestly improving quality. So how might this impact lawyer training and hiring down the line?
Currently, aspiring lawyers must complete a rigorous 7-year education including an undergraduate degree, law school, and passing the bar exam. This intense process aims to equip them with the analytical skills to write legal briefs, research case law, and offer sound counsel. However, much of a lawyer’s day also involves more routine tasks like drafting procedural documents.
The Minnesota study suggests AI could assumption such routine work, freeing up humans to focus on higher-value analysis and client counseling. Students reported being able to identify where within the legal workflow AI delivered the greatest efficiency gains and final product improvements.
As such, law schools could train students in AI utilization just as MD programs teach medical technologies. Apprenticeship-based training with mixed human-AI teams may gain favor over lecture-based doctrinal instruction. Students too poor for university could learn via AI-assisted law apprentice programs.
Similarly, law firms may soon view AI fluency as crucial as a juris doctor degree when hiring. Partners will assign document review and drafting to AI, with human lawyers approving output. Paralegals and junior associates would transition to roles training and supervising legal AIs. We may see creation of “technology-enhanced lawyers” – approved to wield AI tools in client work.
Of course, years of adaption lie ahead before widespread integration of AI by established institutions. But the Minnesota trial provides a glimpse how machines can expand legal accessibility and capability for citizens and businesses should we rebuild systems to responsibly take advantage.
Wide integration of legal technology remains years away given institutional resistance, Minnesota’s trial run paints a picture of technology-enhanced lawyers leveraging AI to expand and enrich services. Rethinking lawyer training and partnerships to responsibly take advantage could drive overdue innovation in legal accessibility and productivity.