Artificial Intelligence in Contract Work: Legal Risks, the Limits of Automation, and Models of Responsible LLM Use in Contract Management

Abstract

Large language models (LLMs) now draft, review, and summarize contracts inside corporate legal and procurement teams, yet their statistical design produces confident errors that carry real legal consequences. This article asks where LLM automation in contract management is safe, where it is not, and how organizations can capture efficiency without transferring risk to signatures and filings. The method combines a structured review of peer-reviewed studies, a comparative reading of United States and European Union governance instruments, and a practice-informed analysis drawn from the author's work in procurement and contract management. The evidence shows a wide distance between benchmark performance and field reliability: general models fabricate legal content in a majority of tested settings, retrieval-augmented legal tools still err in seventeen to thirty-three percent of queries, and adoption in corporate legal departments roughly doubled between 2024 and 2025. In response, the article proposes a Risk-Tiered Autonomy model that assigns each contract task a mode of use and a verification gate. The findings will interest general counsel, procurement leaders, legal operations managers, and contract technology vendors.