Transition to a smart service lifecycle management model with artificial intelligence and data analytics

Abstract

In service-based industries, sustainable competitive advantage is closely related to the continuity, quality, and adaptability to innovation of the services offered to customers. As service systems become increasingly complex due to the impact of digital transformation, Industry 4.0, and increasing competition, traditional Service Lifecycle Management (SLM) systems are insufficient to meet emerging operational and customer demands. Therefore, a different approach to SLM systems using current technologies has become a major necessity. This research aims to examine how SLM can be algorithmically improved through the systematic integration of data analytics (DA), machine learning (ML), and artificial intelligence (AI) into service management stages, and to explore the intelligent management system provided by this transformation. Adopting a conceptual and analytical approach, this research proposes a different approach to the Intelligent Service Lifecycle Management (SSLM) model by integrating intelligent technologies into the fundamental stages of the classical SLM framework. The findings demonstrate that DA, ML, and AI-supported SLM transforms service management from a reactive and static structure into a proactive, predictive, and data-driven system. It also improves decision-making accuracy, risk mitigation, and resource optimization throughout the service lifecycle. Consequently, service-based organizations will be able to achieve higher operational efficiency, improved service quality, and increased organizational agility through SSLM.