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        Evisort Incorporates AI for Efficient Contract Life Cycle Management

        Contract life cycle management software is not new, operating since the ‘80s as a document management and process application. It got a boost in popularity from the launch of customer relationship management systems in the ‘90s and the cloud in the 2000s. But the advent of digitizing contracts – as opposed to an electronic version (such as a PDF) attached to an account – gave rise to a new generation of CLM providers, such as Evisort.

        With its roots in Harvard Law School, Evisort was founded in 2016 to use artificial intelligence with digitized contracts to automate the labor-intensive and time-consuming tasks of VR_Office_of_Revenue_2023_Coverage_Logo (3)analyzing and managing contracts. Its initial use case was reducing manual tasks within the legal function, especially in larger firms managed by legal operations. Unlike other vendors focused on document management, workflows and approvals, Evisort’s first development efforts were improving the process of reading and understanding contracts by ingesting the text and using AI to categorize, classify and extract key terms. This is especially important for existing, legacy and historical contracts where information such as terms and liability risk can be hard to discern without manual inspection, which is not easily scalable. Merger and acquisition activity is a major use case for reviewing and assessing existing contracts. But even with electronic documents, the automation of extracting relevant information is not possible without further conversion of the document into machine-readable form. And if these are paper documents, this is a step away from support automation.

        Since the initial launch in 2020, Evisort’s platform has been well received, acquiring diverse customers from smaller growth companies to well-known enterprise logos. Unlike some CLM vendors, Evisort’s AI roots led to a focus on understanding the content within contracts. Vendors focusing on process management tasks such as document process flow, approvals, storage, indexing and search are now developing AI capabilities.

        Evisort has continued to develop its platform, extending core, AI-based capabilities to document processing and more advanced approval flows. This relaunch included a shift in focus from CLM primarily benefitting legal teams to playing a more impactful role in revenue management. It is in the revenue life cycle where efficient, accurate and timely contract generation and cooperation between employees in sales, revenue and legal is of great importance for a successful sales engagement and creating the right customer experience. It is equally important for new customer acquisition.

        This also includes the purchasing side, where efficient processing of contracts ensures timely and accurate purchasing decisions. In general, contract analysis enables understanding of which clauses cause the most redline Ventana_Research_2023_Assertion_SubscriptionMgmt_Integrated_CPQ_67_Srequests and whether there are patterns by type of buyer and seller for different industries, regions, or company sizes. Although exceptions require review by a qualified attorney, the aim should be to standardize as much as possible, reducing exceptions that require manual processes. Stephen Hurrell discusses this aspect of CLM in his Analyst Perspective on this topic. Further, we believe that through 2025, less than one-quarter of organizations will deploy an integrated configure, price, quote and contract life cycle management application within billing systems, leading to a reduced customer experience.

        In 2023, prior to the more headline-grabbing launch of ChatGPT, Evisort announced a new initiative to use AI to generate contracts. This is a logical progression in the use of AI, and like many early applications of generative AI, it is less a fully automated contract process and more a productivity advance for many contracts that need standardization. Based on predetermined parameters, AI can safely generate contracts for specific events with the added step of manual review, reducing manual effort. But it is possible to imagine a time when most contracts are generated by AI and tailored for a specific event. Eventually, redlining could be automated between two AI bots equipped with the relevant knowledge of how to respond, whether as a buyer or seller. Research Director, Robert Kugel, has recently written about the role of AI in contract management.

        Organizations seeking to deploy AI-powered CLM would do well to focus on institutionalized processes and approaches. Adding AI could encounter resistance, either from a participant who believes his or her role is diminished or those suspicious and worried about inadvertent mistakes made by unsupervised AI. These capabilities are not “free” in terms of subscription payments for the application nor the effort to change processes and team organization. Evisort should be considered a potential vendor and partner for organizations looking for a competitive advantage from a more streamlined and effective contract process.

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