Data-driven Fixed Asset Management Innovation: Practical Exploration of Artificial Intelligence Applied to Data Analysis and Predictive Maintenance

Abstract

This paper explores the transformative potential of integrating artificial intelligence (AI) into fixed asset management, with a particular focus on its practical application in data analytics and predictive maintenance. Effective internal controls in fixed asset management are paramount for organizations seeking to strengthen competitiveness, minimize risk, and optimize asset utilization. By leveraging AI-driven data analytics, organizations can delve deep into their asset portfolios to gain comprehensive insights that inform strategic decision-making and facilitate proactive maintenance protocols. Through a comprehensive literature review and methodological overview, this study highlights the various approaches and cutting-edge technologies used in fixed asset management, underscoring the critical importance of accurate valuation, efficient asset utilization, and regulatory compliance. In addition, real-world case studies and practical applications underscore the tangible benefits and transformative potential of AI-infused asset management practices, demonstrating their ability to revolutionize conventional methodologies and drive organizational performance to unprecedented levels.