Engaged to a Robot? The Role of AI in Service
Abstract
This article develops a strategic framework for using artificial intelligence (AI) to engage customers for different service benefits. This framework lays out guidelines of how to use different AIs to engage customers based on considerations of nature of service task, service offering, service strategy, and service process. AI develops from mechanical, to thinking, and to feeling. As AI advances to a higher intelligence level, more human service employees and human intelligence (HI) at the intelligence levels lower than that level should be used less. Thus, at the current level of AI development, mechanical service should be performed mostly by mechanical AI, thinking service by both thinking AI and HI, and feeling service mostly by HI. Mechanical AI should be used for standardization when service is routine and transactional, for cost leadership, and mostly at the service delivery stage. Thinking AI should be used for personalization when service is data-rich and utilitarian, for quality leadership, and mostly at the service creation stage. Feeling AI should be used for relationalization when service is relational and high touch, for relationship leadership, and mostly at the service interaction stage. We illustrate various AI applications for the three major AI benefits, providing managerial guidelines for service providers to leverage the advantages of AI as well as future research implications for service researchers to investigate AI in service from modeling, consumer, and policy perspectives.
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AI-driven intelligent embodied conversational agents may become more popular and, with increasing advancements, blur the lines between conversational agents and robot artifacts.
“For instance, AIdriven intelligent embodied CAs may become more popular and, with increasing advancements, blur the lines between CAs and robot artifacts (Huang & Rust, 2021).”
From Charting the Evolution and Future of Conversational Agents: A Research Agenda Along Five Waves and New Frontiers (2024) · cites this paper as Huang & Rust, 2021
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- Authors
- Huang, M.-H., & Rust, R. T.
- Affiliations
- National Taiwan University (TW)
- University of Maryland, College Park (US)
- Corresponding author
- Huang, Ming-Hui
- Venue
- Journal of Service Research
- Publisher
- SAGE Publications
- Published
- 2020-02-21
- Volume
- 24
- Issue
- 1
- Pages
- 30-41
- ISSN
- 1094-6705 (ISSN-L)
- 1552-7379
- OpenAlex
- W3008828973
- Cited by
- 1,185
- References
- 32
- Citations by year
- 2026: 298 · 2025: 283 · 2024: 239 · 2023: 161 · 2022: 118 · 2021: 66 · 2020: 20
- FWCI
- 35.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Service Research (publishedVersion)
- Funders
- Ministry of Science and Technology, Taiwan (MOST 104-2410-H-002-142-MY3, MOST 106-2410-H-002-056-MY3, MOST 107-2410-H-002-115-MY3)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Customer Service Quality and Loyalty
- Social Sciences › Business, Management and Accounting › Marketing › Service and Product Innovation
- Keywords
- artificial intelligence in services, customer engagement, service strategy, human-AI collaboration, service standardization, personalized services, service delivery, service creation, service interaction, cost leadership, quality leadership
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