Let’s Team Up with AI! Toward a Hybrid Intelligence System for Online Customer Service

Poser, Mathis; Wiethof, Christina; Banerjee, Debayan; Subramanian, Varun Shankar; Richard, Paucar,; Bittner, Eva A. C.

doi:10.1007/978-3-031-06516-3_11

Cited by 2 publications

(1 citation statement)

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“…Recent research in frontline service technologies has drawn upon the technological advances made in artificial intelligence and particularly hybrid intelligence (HI) (Dellermann, Ebel, Söllner, & Leimeister, 2019) to augment support agents in their problem-solving activities (Kubiak & Rass, 2018;Poser et al, 2022;Poser & Bitner, 2021;Schmidt, Li, Weigel, & Peters, 2021). HI systems are defined by combination of complementary capabilities of human (e.g.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Shifting Dynamics of Information Source Selection: Situational Characteristics and the Rise of Artificial Intelligence

Meservy,

Fadel

et al. 2024

Proceedings of the Annual Hawaii International Conference on System Sciences

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The rapid adoption of innovative technologies confronts IT-Service-Management (ITSM) with incoming support requests of increasing complexity. As a consequence, job demands and turnover rates of ITSM support agents increase. Recent technological advances have introduced assistance systems that rely on hybrid intelligence to provide support agents with contextually suitable historical solutions to help them solve customer requests. Hybrid intelligence systems rely on human input to provide high-quality data to train their underlying AI models. Yet, most agents have little incentives to label their data, lowering data quality and leading to diminishing returns of AI systems due to concept drifts. Following a design science research approach, we provide a novel Human-in-the-Loop design and hybrid intelligence system for ITSM support ticket recommendations, which incentivize agents to provide high-quality labels. Specifically, we leverage agents' need for instant gratification by simultaneously providing better results if they improve labeling automatically labeled support tickets.

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