AI-POWERED IT ASSET MANAGEMENT: OPTIMIZATION AND LIFECYCLE PREDICTION

Abstract

Effective IT asset management (ITAM) is essential for businesses looking to forecast lifetime outcomes and maximize operational efficiency in the age of digital transformation. With an emphasis on sustainability, cost efficiency, and lifecycle prediction, this study investigates the incorporation of AI-driven approaches into ITAM. This method improves reliability and expedites decision-making at various phases of the asset lifecycle by utilizing sophisticated algorithms, such as digital twin technologies and predictive analytics. Real-time insights into asset performance are made possible by AI, which also lowers downtime and permits proactive maintenance plans. Additionally, by addressing lifecycle energy usage and environmental implications, the use of AI in ITAM is consistent with sustainable business strategies. The suggested paradigm uses AI to control resource allocation bottlenecks and enhance operational scalability, building on well-established frameworks in lifecycle cost design and time-dependent reliability. Industry 5.0 viewpoints emphasize how AI can help ITAM innovate by encouraging hyper-automation and effective service delivery. In order to provide a thorough roadmap for AI-enhanced ITAM, this study synthesizes cross-disciplinary ideas, such as developments in marketing strategies, financial management, and insurance. The results highlight AI's revolutionary potential for attaining sustainability, long-term cost-effectiveness, and strategic adaptability. In addition to reducing the risks connected with resource-intensive procedures, this integration puts businesses in a strong position to prosper in the increasingly competitive and complicated digital environment.

Keywords: AI-powered IT Asset Management, Predictive Analytics, Digital Twin Technology, Lifecycle Cost Optimization, IT Asset Lifecycle Prediction, Real-Time Data Integration, AI for IT Infrastructure