During the August 2024 Leah demonstration, the primary contracts attorney immediately recognized that the AI's surgical editing approach matched her professional redlining methodology—changing specific words to preserve counterparty language while achieving legal objectives, rather than replacing entire paragraphs. This collaborative technique facilitates faster negotiation acceptance.
"I do like this part especially because this is how I redline contracts—I'm not gonna nix their whole paragraph and put in mine. I think it's easier for the person looking at my redlines to accept if I'm just changing words not the whole entire paragraph."
— Contracts Attorney, a global bakery ingredients supplier
The automated mutual obligations feature addressed a specific pain point the attorney articulated as "a very tedious process to make a paragraph mutual"—the repetitive, time-consuming task of converting one-sided obligations to mutual obligations word-by-word. Leah would handle that transformation automatically while attorneys verify the output.
Unlike CRNC's limitation to 10 pre-configured clauses, Leah analyzes entire contracts comprehensively, enabling legal teams to identify any problematic provisions and not just the handful they anticipated in advance. The capability to review all contract language rather than being constrained to pre-selected clauses represented a major differentiator.
Configuration simplicity proved decisive. Where CRNC required building detailed playbooks with rules-based programming for each contract type variation, Leah works with natural language understanding of legal concepts. The attorney recognized this immediately: "I think this would be a lot easier to use or quicker to get set up than the previous tool."
The Ask Leah feature would empower junior team members to understand complex legal terms and handle straightforward contracts without constant escalation to the bottlenecked attorney. Historical precedent capabilities meant the system would reference how the company had handled similar agreements previously, building institutional knowledge rather than starting fresh every time.
Legal leadership saw strategic value beyond individual contract efficiency. The reporting functionality could identify clauses the team consistently agrees to change—like governing law provisions they always modify from California to Michigan—enabling proactive template updates rather than repeatedly negotiating the same points.
When the team compared generative AI capabilities to continuing rules-based CRNC investment, the decision became clear. They questioned why they would continue with CRNC when Leah could deliver faster value without configuration burden. Legal leadership directed the commercial team to prepare a subscription swap proposal—replacing the underutilized CRNC with Leah to manage cost rather than treating it as net-new spend.
Leah transparency into AI reasoning aligned with the company' emerging AI governance policy framework. The system shows why it recommends specific redlines, providing explainability required for responsible AI adoption in legal contexts. The solution augments attorney capabilities rather than attempting to automate legal decision-making—exactly the verification-focused approach the organization's policy emphasizes.
