Integrating artificial intelligence into business processes: how to enhance customer–machine interactions amid industry convergence

Purpose – This research illustrates how companies can leverage Artificial Intelligence (AI) within the context of industry convergence to foster stronger customer-machine interaction. We aim to unveil how convergence occurs within industries and what AI-enabled mechanisms help exploit these developments. Design/methodology/approach – AI is increasingly adopted within convergent service industries. Such integrations are employed to shape customer–machine interactions across business processes. Yet existing research rarely examines AI use under conditions of industry convergence while also tracing effects across the full input–process–output (IPO) chain. To address this gap, we conduct a qualitative, multi-case study inductive design, set in the MedTech industry. This approach enables a holistic, end-to-end examination of AI outcomes across the IPO chain, while preserving the organizational and industry context in which convergence occurs. Findings – Results reveal industries face different pathways amid industry convergence, leading to the following types of output: non-convergence, symmetric convergence, asymmetric convergence. Furthermore, we identified stage-specific AI mechanisms and classified them according to their impact on customer acceptance of machines. Originality/value – This research introduces an industry convergence input–process–output (ICIPO) model, reframing the original framework around this phenomenon. Moreover, it provides mapping for AI mechanisms amid industry convergence, allowing managers to improve customer-machine interactions across business processes.