arbiter
JournalsAccount

AI in Service Design: A New Framework for Hybrid Human–AI Service Encounters

Marzia Mortati & Giorgia Dall Agnol Teixeira de Freitas

Journal of Service Research2025https://doi.org/10.1177/10946705251344387article
AJG 4ABDC A*
Weight
0.57

Abstract

The integration of Artificial Intelligence (AI) in service provision has prompted a revaluation of how we design service encounters, given the emergent role this technology plays in services. This article introduces the concept of the Hybrid Service Encounter to explore the evolving interplay between humans and AI in service contexts. We propose a 2 × 2 framework that categorizes service interactions into four distinct quadrants based on whether the provider and user are human or AI. Drawing on service design literature and current developments in AI, we analyze the implications of these hybrid encounters for service design roles, processes, and outputs. The article further identifies key areas for future research to guide the development of human-centered and ethical AI-powered services. Our contribution extends service design theory and practice by offering a framework to guide the integration of AI in service encounters.

11 citations

Open via your library →

Cite this paper

https://doi.org/https://doi.org/10.1177/10946705251344387

Or copy a formatted citation

@article{marzia2025,
  title        = {{AI in Service Design: A New Framework for Hybrid Human–AI Service Encounters}},
  author       = {Marzia Mortati & Giorgia Dall Agnol Teixeira de Freitas},
  journal      = {Journal of Service Research},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1177/10946705251344387},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

AI in Service Design: A New Framework for Hybrid Human–AI Service Encounters

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.57

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

F · citation impact0.57 × 0.4 = 0.23
M · momentum0.78 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.