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Informative Article | Science and Technology | India | Volume 11 Issue 4, April 2022
Service Modelling and Performance Management with AI and Machine Learning
Abstract: In the ever-changing modern business landscape, the need for effective performance management remains an important step for organizational success. It is vital tolook into the transformative impact of Artificial intelligence and machine learning, reshaping traditional modeling approaches and performance management practices in services computing. That is the goal of this paper. In addition, the paper explores the transition from static to dynamic service models facilitated by AI and ML, insisting on the enhanced adaptability and agility brought about by service delivery. The paper redefines the traditional approaches to aligning employees with organizational objectives and optimizing their performance. Traditionally, performance management focused on aligning employees with company goals. However, AI technologies have brought about a shift that allows organizations to utilize extensive datasets to improve performance, data-driven decision-making, and promote employee development. In times when data-driven insights are important, AI can process huge amounts of data, which is a key aspect of performance management. Integrating AI promotes performance management processes, thus enhancing accuracy, objectivity, and efficiency, giving an array of trends and patterns that may remain elusive through traditional methods. On the other hand, conventional approaches such as AI-driven processes facilitate continuous data evaluation and collection, thus ensuring real-time feedback and supporting employee growth via personalized training suggestions. This paper provides a comprehensive exploration of the role of AI and ML in shaping service modeling and performance management practices, thus giving a roadmap for organizations to utilize the full potential of these technologies regarding service computing.
Keywords: Service modeling, performance management, AI in service computing, predictive analysis, data-driven insights, Machine learning applications, automated service optimization
Edition: Volume 11 Issue 4, April 2022,
Pages: 1374 - 1379