Jochen Wulf’s scientific contributions

Successful smart services require seamless integration into existing corporate systems and an interdisciplinary approach that aligns the development of both business models and technical architectures. Multi-disciplinarity and cocreating with customers add a layer of complexity but are essential collaboration schemes for validating the value proposition of smart services and building longterm customer loyalty. This paper explores these challenges and distills the design principles for the architectures of technical smart service systems, based on empirical data from architecture projects in two manufacturing companies. These principles contribute to the sparse academic literature on this topic and help practitioners navigate several design trade-offs commonly arising in smart service projects.

We introduce a novel framework aimed at identifying and quantifying the value of customer pains as a critical element in service innovation. The proposed approach enhances existing end-to-end frameworks by offering a structured method to elaborate on and measure the value derived from solving these customer challenges. The effectiveness of the framework is validated by operationalizing it in an industrial case study, where the model parameters were captured specifically and the value of solving various operational and structural pains was evaluated numerically.

The use of large language models (LLMs) such as OpenAI's GPT-4 in technical customer support (TCS) has the potential to revolutionize this area. This study examines automated text correction, summarization of customer inquiries and question answering using LLMs. Through prototypes and data analyses, the potential and challenges of integrating LLMs into the TCS will be demonstrated. Our results show promising approaches for improving the efficiency and quality of customer service through LLMs, but also emphasize the need for quality-assured implementation and organizational adjustments in the data ecosystem.

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.