Le, KhanhSajtos, LaszloKunz, Werner2026-05-122026-05-122026-05-12Journal of Business Research, ISSN: 0148-2963 (Print); 1873-7978 (Online), Elsevier, 216, 1-16. doi: 10.1016/j.jbusres.2026.1162610148-29631873-7978http://hdl.handle.net/10292/21061In 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.© 2026 The Author(s). Published by Elsevier Inc. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. You are not required to obtain permission to reuse this article.https://creativecommons.org/licenses/by/4.0/350607 Marketing technology1505 Marketing35 Commerce, management, tourism and servicesHumansMachinesSymbiosisCollaborationTransactive memory systemCo-learningCo-actingCo-specializationFirm innovativenessService frontlineJournal ArticleOpenAccess10.1016/j.jbusres.2026.116261