Why Legal AI Stalls at the Individual Level — and How to Break Through

Shun Yamamoto, CEO of GVA TECH and practicing attorney, walks through the structural reasons legal AI adoption stays stuck at personal experimentation and demonstrates concrete techniques for lifting accuracy through prompt engineering and knowledge bases.

• LLM contract review quality is already good enough — what differentiates results is how carefully you build your prompts and knowledge base
• Most companies are still at the individual-trial stage; organization-wide deployment hasn’t happened at the majority of firms
• Prompts should be structured in order: role, background, workflow procedure — and PDCA cycles should be driven from the desired output backward
• Within the knowledge base, playbooks (scenario-specific handling instructions) are the single most effective lever for controlling output precision
• OLGA keeps contract versioning, business-unit requests, and communication history bundled as one deal package, making the structure easy for AI to consume
• Phase 4 of legal AX has AI doing autonomous review while humans only monitor logs and respond to alerts — AI governance is a prerequisite
• AI accountability follows the same logic as delegating to a subordinate: the person who designed the system bears responsibility

2026-04-07 · Watch on YouTube

Japanese page: 法務AIが企業に刺さらない理由と突破口