Most Contract Lifecycle Management (CLM) products on the market today are designed with a “Document-1st, Data-Maybe” approach, whereby legal documents take precedence over data. In the rare instance when contract data is accessible, it is typically extracted with “document-scraping” methods based on machine learning (ML) and generative AI tools. Even with an overlay of AI/ML, the underlying architecture is a legacy design that does not address the need to make each data element accessible.
Is there an express-elevator approach to reach the penthouse floor in CLM? Is there a method for generating and managing contract data as a persistent and real-time enterprise system of record?
Why not focus on CLM data first, and build a contract document with legal clauses around it? While this approach may be complex to implement in existing CLM systems that handle large volumes of legacy and third-party contracts, for greenfield use cases, a Data-1st focus to CLM can yield superior results. Over the next 5-7 years, with broader adoption of CLM across departments beyond legal, the Data-1st approach to CLM is likely to gain increasing share of interest and wallets.
CLM solutions emphasizing a Data-1st approach must outline a clear ability to extract data from legacy and third-party paper. AI/ML tools can be utilized to support advanced data extraction. Getting cleaner contract data further catalyzes the business case for advanced AI tools in CLM. In fact, without a Data-1st design approach, it is difficult to envision the use of AI in CLM being broadly adopted in the long term. MGI Research believes that a fusion of a Data-1st approach along with practical applications of AI can drive increased enterprise visibility, especially in organizations with complex B2B relationships.
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For more about CLM maturity and evolution, read The Six Stages of CLM.
