AI-Enhanced Management of Standard Operating Procedures: An Innovative Approach Integrating Large Language Models and Knowledge Graphs

Abstract

This paper presents an innovative solution to address inefficiencies in Standard Operating Procedure (SOP) management by integrating Large Language Models (LLMs) and Knowledge Graphs (KGs) to create an intelligent, contextaware digital assistant. Traditional SOP systems lack adaptability, contextual understanding, and dynamic functionality, leading to operational inefficiencies and compliance risks. By leveraging the interpretability of KGs and the generative capabilities of LLMs, the proposed solution offers enhanced functionality, including dynamic SOP chaining, realtime conflict detection, and actionable insights for decisionmaking. Through a specific example of use, we demonstrate how the proposed digital assistant enhances operational efficiency, reduces errors, and fosters adherence to regulatory standards. This work contributes to the evolution of SOP management by building on prominent AI advancements.

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