Food Manufacturer Modernises Contract Review

A leading food manufacturer supplying grocery chains and retail bakeries across North America, Europe, and Mexico faced a resource constraint paradox. Their legal team had invested in a contract risk and compliance module to accelerate contract review, but the tool sat largely unused. The primary contracts attorney handling daily redlining across the entire organization simply didn't have bandwidth to configure the extensive playbooks required—each contract type demanded detailed rules-based programming for every clause variation.

The team spent hours on tedious manual tasks like creating mutual obligations and adjusting liability clauses word-by-word in every contract. Each incoming third-party agreement required manual first-pass review from scratch, with no AI assistance to highlight issues or suggest redlines based on company standards. The team didn't reference historical precedent—each contract review started from zero institutional knowledge, creating repetitive decision-making.

A critical incident crystallized broader contract management gaps: When a long-term grocery chain customer was acquired, the parent company challenged the food manufacturer to prove historical pricing terms from their 2008 agreements. Despite extensive searching, nobody could locate the original contracts. The sales representative who secured the deal had stored them in personal email and local files, then left the company. Per policy, all files were automatically purged 90 days after departure. This documentation failure nearly resulted in unfavorable renegotiation of terms accumulated over 16 years of business relationship.

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The food manufacturer needed contract AI that would work immediately without months of configuration work consuming their already-constrained attorney capacity. The evaluation criteria centered on practical deployment for lean teams: comprehensive contract analysis without artificial clause limitations, surgical editing capabilities that matched professional attorney methodology, historical precedent learning to build institutional knowledge, and the ability to empower junior team members to handle routine agreements independently.

The organization was developing enterprise AI governance policies emphasizing verification over blind acceptance—any legal AI tool would need to provide transparency into reasoning and enable attorney judgment rather than attempting autonomous decision-making. The solution also needed to align with their long-term CLM relationship; after seven years of platform investment and institutional knowledge building, switching vendors would mean going backwards.

When legal leadership saw competing CLM vendors approach their IT department without legal consultation, they immediately shut down the evaluation. The contracts team had invested years building their foundation on their existing platform, and unless another solution offered something dramatically superior, disruption wasn't acceptable. The focus turned to evolving their existing partnership rather than replacing it.

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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.

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 the previous tool's limitation to 10 pre-configured clauses, Leah analyzes entire contracts comprehensively. Legal teams need to identify any problematic provisions, 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 the previous tool 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 organization 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 investment, the decision became clear. They questioned why they would continue with their previous tool when Leah could deliver faster value without configuration burden. Legal leadership directed the commercial team to prepare a subscription swap proposal—replacing the underutilized rules-based tool with Leah to manage cost rather than treating it as net-new spend.

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