AI-enhanced digital twins in maintenance: Systematic review, industrial challenges, and bridging research–practice gaps

Abstract

The convergence of artificial intelligence (AI) and digital twin technology is reshaping maintenance strategies in the era of Industry 4.0. However, gaps persist between academic advancements and industrial adoption and expectation. This study systematically investigates the landscape of AI-enhanced digital twins for maintenance by integrating a systematic literature review (SLR) of related studies with in-depth interviews from industry practitioners. Our analysis reveals that while academia demonstrates robust applications of supervised, deep, and reinforcement learning to optimize digital twin models and prescribe data-driven actions, industrial implementation remains limited by challenges such as high scale dimension, data integration complexities, and insufficient workforce readiness. We identified and articulated three critical gap dimensions, scale, data, and model between academic research and industrial implementation and expectation. To bridge these gaps, we proposed a comprehensive five-layer framework for AI-enhanced digital twins, encompassing physical assets, data transmission, digital twins, AI analytics, and maintenance services. Actionable recommendations are provided, including the adoption of modular architectures, standardized data protocols, hybrid edge-cloud solutions, and targeted workforce upskilling. Our findings not only clarify the current state and challenges of AI-driven digital twins in maintenance but also offer a practical roadmap for accelerating their industrial implementation. This work advances the field by integrating insights from both academic research and industrial practice, offering concrete recommendations to support the practical realization of smart and sustainable maintenance practices.