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Redefining Contract Management: The Fusion of Generative AI and Traditional Methods

As Contract Lifecycle Management (CLM) evolves, we are witnessing a significant transformation, driven by the integration of Advanced Artificial Intelligence (AI) tools into existing CLM and other LegalTech products. This transformation is marked by a strategic collaboration between Generative AI tools, particularly Large Language Models (LLMs), and traditional artificial intelligence techniques. This article aims to explore the interplay of old and new technologies in improving accuracy and enhancing efficiency, while addressing the limitations of both traditional methods and LLMs. It will show how a carefully developed approach maintains the value of traditional methods, while leveraging the power of the new models - highlighting Clearlaw's innovative strategies. In conclusion, the article will highlight the transformative impact this integration has on the future of CLM.


Enhancing CLM with GenAI:


The integration of GenAI in CLM marks a paradigm shift in LegalTech. LLMs excel at producing contextually appropriate and legally coherent content, and can be used to speed up tasks like drafting and editing contracts, especially when LLMs are linked to existing playbooks and clause libraries using techniques such as RAG. LLMs also show a remarkable ability to take the content of existing contracts and answer questions about that content. LLMs also help CLM providers tackle the difficult problem of designing useful human/machine interfaces, by enabling systems that provide answers or perform tasks using a conversational interface. These capabilities are a game-changer in areas traditionally reliant on intensive human labor. By automating and optimizing workflows, GenAI provides a cornerstone for the modernization of CLM processes.


Scrutinizing GenAI's Constraints in CLM:


Despite GenAI's notable benefits, it is imperative to critically evaluate its limitations within the CLM framework. GenAI's reliance on prompt-based inputs, for instance, requires a depth of legal expertise for effective application. Moreover, the accuracy and reliability of GenAI's outputs isn’t sufficient to replace human oversight, even when the best LLM models are used. Vigilant oversight is required, especially given the high stakes in legal contexts. Financial factors, as well as performance speed, also influence its adoption, despite a trend towards decreasing operational costs. Although some CLM providers are tempted to rely on less expensive LLM models to provide GenAI capabilities, this choice comes with an inherent reduction in performance. A nuanced understanding of these constraints is vital for the judicious employment of GenAI tools within a CLM system.


The Persistent Importance of Traditional AI Techniques:


Traditional AI methods, notably machine learning, retain a significant role in CLM. Prior to GenAI taking the stage, they were the engine behind the most capable CLMs. Their relatively low cost and model speed makes them perfect for automating repetitive tasks to enhance efficiency, thereby boosting efficiency and optimizing workflows. Their prowess in data extraction and verification offers a layer of reliability, especially where accuracy is non-negotiable. The ability of traditional AI to normalize varied legal data remains crucial for deciphering complex contractual obligations. Moreover, their efficiency in processing substantial amounts of data cements their indispensability in handling large contract portfolios. The continued relevance of traditional AI underscores its irreplaceable role in maintaining precision and efficiency in CLM.

Integrating GenAI with Traditional AI for Comprehensive CLM:


The essence of an adept CLM strategy lies in the seamless integration of GenAI with traditional AI methods. Clearlaw's approach advocates for a judicious combination of these technologies. GenAI introduces innovation and flexibility, allowing for rapid expansion of AI capabilities within a CLM platform. It allows CLMs to offer improved human interfaces and allows people to interact with the CLM - and their contracts - in new and innovative ways. GenAI has also proved adept at certain information extractions where traditional AI methods had proved deficient. At the same time, traditional AI methods contribute structure and reliability, crucial for a thorough CLM process, and allow for continued speed in accurate, cost-effective, contract processing. Clearlaw’s approach continues to rely on its proven technologies, using experimentation to identify where GenAI can improve on existing capabilities. Clearlaw also deploys high-quality extraction models to underlay RAG instead of relying on lower accuracy semantic search methods. Clearlaw CLM partners layer additional GenAI powered capabilities into their offerings. This synergistic alliance allows a well-rounded toolkit, redefining the approach to CLM and augmenting the competencies of legal professionals.


Conclusion:


The trajectory of LegalTech, especially in CLM, is unmistakably veering towards a future where the combined deployment of GenAI and traditional AI techniques is indispensable. Clearlaw's dedication to this integration marks a progressive stance, placing the firm at the vanguard of a transformative movement in an increasingly fluid industry. This strategic amalgamation is not just a technological convergence; it heralds a significant shift in CLM methodologies, heralding a new age of efficiency, innovation, and agility for legal practitioners.


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