Autor: Uta Wilkens

The paper reflects the potential and remaining shortcomings of AI-based work systems for exploiting and enhancing individual and organizational learning processes. It especially refers to the use adaptive systems in production and gives examples of good practice for the design of AI-based work systems which promote the interplay between individual and artificial intelligence. The conceptual framework refers to different methods in machine learning which are complemented by insights from individual and organizational learning theory.

Industrial product-service systems (IPS²) represent a combination of interdependent product and service components developed to satisfy highly specific customer needs. One key challenge in the engineering of IPS² pertains to the successful integration of providers’, customers’ and suppliers’ heterogeneous expertise. While previous research mainly addresses this issue through the lenses of organization or automation, little attention has been paid towards integration through interaction. In this paper we therefore ask the question which competencies enable employees and teams in IPS² to successfully deal with heterogeneity thus taking advantage of its innovative potential. We use a a multilevel model of competence and specify it for the IPS² work system on the base of expert interviews. It becomes apparent that experts attribute successful IPS² performance especially to factors which increase perceived stability in flexible or heterogeneous work systems.