In the age of Artificial Intelligence (AI), serving customers together in human–machine teams is becoming more common, but optimizing this teamwork is new and increasingly complex. The traditional concepts of Machine Augmentation (MA) and Human-Machine Collaboration (HMC) do not fully realize the full potential of this new technology. This article introduces Human–Machine Symbiosis (HMS) as a dynamic adaptation process between employees and machines through ongoing service interactions with customers. We conceptualize this process as a higher-order system (including MA and HMS) that builds on co-specialization, co-acting, and is uniquely driven by co-learning – a process comprising three interdependent activities – knowledge sharing, assimilation, and calibration, that jointly shape human–machine team performance over time. This research identifies task decomposability and machine trustworthiness as key facilitators of the co-learning process. Additionally, HMS can also influence firm innovativeness in the long run. The framework offers guidance on how service organizations can benefit from HMS and effectively integrate AI into frontline work.
Le et al. (Tue,) studied this question.