
Redlining Automation: Safer Drafts with Playbooks
Published 2025-08-26
Automated suggestions should be scoped by playbooks and approval limits. We show patterns that keep humans in control.
Successful teams combine retrieval‑augmented generation with clause libraries and audit trails. Rather than chasing perfect automation, they aim for dependable assists—drafting first passes, surfacing fallback language, and highlighting the deviations that matter.
This article shares practical checklists: how to pick pilot use cases, set acceptance thresholds, and measure outcomes like cycle time, error rates, and adoption. The goal is simple: tools that help legal and business teams reach agreement faster without increasing risk.
Related reading: Detecting Contract Risk with AI: Signals that Matter · Integrating AI into Legal Ops: Start Small, Measure Big