GenAI-Infused Service Delivery: Micro-Level Augmentation Patterns at the Service Frontline

Philipp Reinhard et al.

Journal of Service Research2026https://doi.org/10.1177/10946705251414283article
AJG 4ABDC A*
Weight
0.50

Abstract

The infusion of generative AI (GenAI) is already disrupting established services. This technology’s generative and agentic nature challenges the design and management of service routines, which have been previously handled primarily by frontline service employees. Guided by organizational routines theory, our longitudinal study (2020–2024) examines how the infusion of GenAI changes routines in customer support services. We gathered interview data from 41 employees, managers, and AI experts in two phases, pre- and post-GenAI . Based on the analysis of the qualitative data, we revealed seven recurring micro-level augmentation patterns, illustrating how GenAI-infused service routines function. The results show that GenAI is primarily embedded in the backstage of knowledge-intensive services, from which it then permeates the frontstage. We contribute to the literature on hybrid human–AI service delivery by identifying augmentation patterns and conceptualizing service permeation via two mechanisms: (1) simultaneous service permeation , which unfolds as employees leverage GenAI in real-time and integrate GenAI’s responses, recommendations, and adaptations into the frontstage; (2) sequential service permeation , which emerges as employees perform new routines of documentation and AI feeding to facilitate GenAI’s adaptability in frontstage and backstage operations. The MAPs and service permeation mechanisms guide practitioners in integrating GenAI into service routines and managing novel employee-GenAI collaborations.

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https://doi.org/https://doi.org/10.1177/10946705251414283

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@article{philipp2026,
  title        = {{GenAI-Infused Service Delivery: Micro-Level Augmentation Patterns at the Service Frontline}},
  author       = {Philipp Reinhard et al.},
  journal      = {Journal of Service Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1177/10946705251414283},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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