Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants

Industrial maintenance strategies increasingly rely on artificial intelligence to predict asset conditions and prescribe maintenance actions. The related maintenance software and human maintenance actors can form a hybrid-augmented intelligence system where each side benefits from and enhances the other side's intelligence. This system requires optimized human-machine interfaces to help users express their knowledge and retrieve information from difficult-to-use software. Therefore, this article proposes a novel approach for maintenance experts and operators to interact with a predictive maintenance system through a digital intelligent assistant. This assistant is artificial intelligence (AI) that could help its users interact with the system via natural language and collect their feedback about the success of maintenance interventions. Implementing hybrid-augmented intelligence in a predictive maintenance system faces several technical, social, economic, organizational, and legal challenges. The benefits, limitations, and risks of hybrid-augmented intelligence must be clear to all employees to advocate its use. AI-focused change management and employee training could be techniques to address these challenges. The success of the proposed approach also relies on the continuous improvement of natural language understanding. Such a process will need conversation-driven development where actual interactions with the assistant provide accurate training data for language and dialog models. Future research has to be interdisciplinary and may cover the integration of explainable AI, suitable AI laws, operationalized trustworthy AI, efficient design for human-computer interaction, and natural language processing adapted to predictive maintenance.