Using AI: From Contract Review to Workflow Automation

AI and automation are rapidly gaining traction in the legal sector. From contract review to workflow automation, legal ops teams are adopting these tools thoughtfully, recognizing that strong governance matters just as much as the technology itself. However, legal departments continue to face a persistent imbalance: a rising demand for support paired with a limited amount of time to deliver it, as LawVu writes in a recent article.

In practice, AI in legal operations centers on using machine learning to automate and augment workflows such as contract review, data extraction, intake, and reporting. Traditional tools simply store or route information but AI interprets it, making underlying data quality and process design critical to success. Without that foundation, outputs quickly become unreliable, undermining trust and increasing risk.

The core inefficiency AI aims to address is not a single breakdown, but the accumulation of low-value tasks that consume legal ops’ time. Routine document review, stakeholder follow-ups, and repetitive questions divert attention from strategic work. AI’s most meaningful contribution is reducing this friction. 

The most successful legal ops teams are using AI thoughtfully, from contract review to workflow automation. These teams recognize that AI and automation serve complementary roles. Automation creates structure by standardizing workflows and data capture, while AI generates insight by interpreting text, extracting meaning, and answering unstructured questions. Together, they enable scalable efficiency, but only when built on consistent processes and a reliable source of truth. Automation ensures contract data is captured consistently and reliably, while AI transforms it into searchable, usable, and actionable data.

The article highlights three core lessons for legal ops teams implementing AI. First, before evaluating technology solutions, identify repeatable, high-volume tasks where AI can reduce friction without introducing unnecessary risk. Second, establish a strong data foundation from the outset. Without a single, authoritative source for contract and matter data, AI systems are likely to generate inconsistent or conflicting results. Finally, validation cannot be overlooked. AI may accelerate workflows, but human judgment remains essential to ensure outcomes are accurate, defensible, and aligned with organizational standards.