Accelerating CLM Implementation

AI Approach

Our attorney team — including corporate partner Chad Warpula and attorney Reuben Dacher‑Shapiro and the Troutman eMerge team, led by Mike Frankel — partnered to design an AI-powered workflow tailored to the client’s CLM needs.

Our corporate attorneys first defined the business and risk requirements, including what provisions and attributes mattered most for this client’s contracts, such as parties, location, term, renewal options, assignment, and other critical terms.

Working from that framework, eMerge configured an AI-driven extraction process that would produce outputs in the format required by the client’s CLM platform.

We leveraged Hebbia to complete this task, creating AI prompts tied to specific CLM fields. For each contract, we used AI to:

• Pull the exact contract language for a given provision.
• Generate a plain-language summary of that provision.
• Apply standardized coding to support attorney-driven judgments and business decisions.

Reviewers could click directly from each data point to the underlying contract text via hyperlinks, enabling rapid validation and quality control. We first tested this approach on a representative sample of contracts to refine prompts and outputs, then scaled the process across the full set of more than 1,000 agreements with a larger Troutman eMerge review team, under attorney supervision.

Impact

By combining attorney judgment with AI, we transformed what would have been a lengthy, manual effort into a streamlined process.

Key impacts included:

• Significant time and cost savings: We reduced the time required to extract and structure data from the legacy contracts to roughly half of what a traditional manual review would have taken.
• Higher-quality, CLM-ready data: The AI workflow produced consistent, structured data aligned to the client’s specific CLM fields, while preserving links to the underlying contract language for easy verification.