<p>With the advancement of automation in industrial equipment, the need for precise and real-time process monitoring has become increasingly critical. Digital twins and artificial intelligence (AI) have emerged as promising technologies for addressing this need, and their application to industrial process monitoring has been actively investigated. This study proposes an interoperable approach for integrating AI models with physical assets by leveraging the Asset Administration Shell (AAS) and Open Neural Network Exchange (ONNX) standards and demonstrates its implementation as an AI-powered digital twin in a real industrial process. In the proposed architecture, the AAS meta-model provides a standardized information structure for the digital representation of physical assets and the interface between the physical and cyber worlds. In parallel, AI models are integrated in the ONNX format as framework-independent inference modules, enabling the operational status of assets to be predicted across heterogeneous deployment environments. By combining AAS-based asset representation with ONNX-based AI inference, the proposed digital twin moves beyond passive asset representation toward reusable, interoperable, and AI-enabled decision support. The proposed methodology achieved a 95.47% AI-model attribute linkage ratio, indicating the structural coverage of AAS-managed AI-model metadata, and 99.97% output consistency across eight heterogeneous deployment environments. When applied to a remote underwater cutting robot system, the AI-powered digital twin provided the cutting status within one second, achieving 95.39% accuracy in three-class (Cut/Half-cut/Idle) classification, thereby supporting operator decision-making even under conditions where visual feedback is limited.</p>