Scholarlink

<p>Generative artificial intelligence (GenAI) is a rapidly evolving field that enables novel and valuable information systems. However, developing GenAI-based information systems poses unique challenges that need to be addressed by researchers and practitioners. This paper proposes a method that supports organizations in the responsible engineering of GenAI-based information systems aimed at improving internal operations and value-creation processes. To develop the method enabling organizations to exploit GenAI’s enormous potential while mitigating associated risks, elaborated action design research was adopted. One diagnosis cycle, two design cycles, and one implementation cycle were conducted, involving the evaluation of the method’s design through 48 interviews with practitioners and based on the realization of three real-world GenAI use cases. The theoretical contribution includes prescriptive design knowledge for the responsible engineering of GenAI-based information systems. From a practical standpoint, the paper extends organizations’ methodological toolbox with a holistic, prescriptive guideline for engineering GenAI-based information systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Responsible Engineering of Information Systems Based on Generative Artificial Intelligence: An Action Design Research Study at a German Premium Car Manufacturer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月4日

查看全文
  • Dominik Fetzer,
  • Henner Gimpel,
  • Oliver Meindl,
  • Jan Strickmann

摘要

Generative artificial intelligence (GenAI) is a rapidly evolving field that enables novel and valuable information systems. However, developing GenAI-based information systems poses unique challenges that need to be addressed by researchers and practitioners. This paper proposes a method that supports organizations in the responsible engineering of GenAI-based information systems aimed at improving internal operations and value-creation processes. To develop the method enabling organizations to exploit GenAI’s enormous potential while mitigating associated risks, elaborated action design research was adopted. One diagnosis cycle, two design cycles, and one implementation cycle were conducted, involving the evaluation of the method’s design through 48 interviews with practitioners and based on the realization of three real-world GenAI use cases. The theoretical contribution includes prescriptive design knowledge for the responsible engineering of GenAI-based information systems. From a practical standpoint, the paper extends organizations’ methodological toolbox with a holistic, prescriptive guideline for engineering GenAI-based information systems.