As autonomous systems become integral to various industries, effective strategies for fault handling are essential to ensure reliability and efficiency. Transfer of Control (ToC), a traditional approach for interrupting automated processes during faults, is often triggered unnecessarily in non-critical situations. To address this, we propose a data-driven method that uses human interaction data to train AI models capable of preemptively identifying and addressing issues or assisting users in resolution. Using an interactive tool simulating an industrial vacuum cleaner, we collected data and developed an LSTM-based model to predict user behavior. Our findings reveal that even data from non-experts can effectively train models to reduce unnecessary ToC events, enhancing the system's robustness. This approach highlights the potential of AI to learn directly from human problem-solving behaviors, complementing sensor data to improve industrial automation and human-AI collaboration.