  Purpose: The purpose of this study is to investigate the potential of Large
Language Models (LLMs) in transforming technical customer service (TCS) through
the automation of cognitive tasks. Design/Methodology/Approach: Using a
prototyping approach, the research assesses the feasibility of automating
cognitive tasks in TCS with LLMs, employing real-world technical incident data
from a Swiss telecommunications operator. Findings: Lower-level cognitive tasks
such as translation, summarization, and content generation can be effectively
automated with LLMs like GPT-4, while higher-level tasks such as reasoning
require more advanced technological approaches such as Retrieval-Augmented
Generation (RAG) or finetuning ; furthermore, the study underscores the
significance of data ecosystems in enabling more complex cognitive tasks by
fostering data sharing among various actors involved. Originality/Value: This
study contributes to the emerging theory on LLM potential and technical
feasibility in service management, providing concrete insights for operators of
TCS units and highlighting the need for further research to address limitations
and validate the applicability of LLMs across different domains.
