DEVELOPING AI-ENABLED AUTONOMOUS DIGITAL TWIN ARCHITECTURE FOR REAL-TIME PREDICTIVE ANALYTICS, ADAPTIVE DECISION-MAKING, AND INTELLIGENT SYSTEM MANAGEMENT

Abstract

AI-enabled autonomous digital twins represent a new generation of cyber-physical intelligence systems that integrate real-time data, artificial intelligence, and adaptive decision-making capabilities to transform organizational management. Unlike traditional digital models and analytics platforms that primarily support monitoring and historical analysis, autonomous digital twins enable predictive intelligence, scenario simulation, and controlled operational adaptation. However, the successful implementation of these systems requires deeper understanding of their architectural components, organizational capabilities, and governance mechanisms. This study explores the requirements and implications of AI-enabled autonomous digital twins through a qualitative research approach based on expert interviews conducted across multiple industries. The findings identify critical dimensions including real-time sensing, data integration, AI-driven prediction, decision optimization, human oversight, and responsible autonomy. The study contributes by conceptualizing an integrated autonomous digital twin architecture that connects digital representation with artificial intelligence-based intelligence and adaptive management capabilities. Furthermore, it extends Dynamic Capabilities Theory and Organizational Information Processing Theory by explaining how intelligent digital twin systems enhance organizational sensing, information processing, and adaptive response capabilities.