Adoption of AI-Based Systems in Industrial Maintenance: Empirical Evidences from an Action Research in the Maintenance Service Business

The adoption of AI-based systems in industrial contexts is a hot topic in the scientific and technical arena. The present paper aims to bring a contribution to this regard. It sheds light on the adoption of AI-based systems starting from the evidences gathered during an action research in the maintenance service business. The action research deals with the adoption of AI in Predictive Maintenance within the service offering of an Original Equipment Manufacturer that takes advantage of Digital Servitization. The maintenance data-driven decision-making is studied based on the triad of three related entities: technologies, humans and organizations. Moreover, the inclusion of advances potentially available from AI is discussed by means of two selected use cases where advanced data analytics embedding Machine Learning (ML) and Transfer Learning (TL) techniques are considered. As AI is infused along the Predictive Maintenance process implemented with the digital transformation, the use cases are presented as meaningful examples in order to deduce strategic considerations in terms of strengths, weaknesses, opportunities and threats as implied by their deployment in Predictive Maintenance processes at full scale.