IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-662-71209-2_11.html
   My bibliography  Save this book chapter

Large Language Models und Datenökosysteme zur Automatisierung des technischen Kundendienstes

In: Data Sharing für KMU

Author

Listed:
  • Jochen Wulf

    (ZHAW Zürcher Hochschule für Angewandte Wissenschaften)

  • Jürg Meierhofer

    (ZHAW Zürcher Hochschule für Angewandte Wissenschaften)

Abstract

Zusammenfassung Die Nutzung von Large Language Models (LLMs) wie GPT-4 von OpenAI im technischen Kundendienst (TKD) hat das Potenzial, diesen Bereich zu revolutionieren. Diese Studie untersucht die automatisierte Textkorrektur, Zusammenfassung von Kundenanfragen und Fragenbeantwortung mittels LLMs. Durch Prototypen und Datenanalysen werden das Potenzial und die Herausforderungen der Integration von LLMs in den TKD aufgezeigt. Unsere Ergebnisse zeigen vielversprechende Ansätze für die Verbesserung der Effizienz und Qualität des Kundenservice durch LLMs, betonen jedoch auch die Notwendigkeit einer qualitätsgesicherten Implementierung und organisatorischen Anpassungen im Datenökosystem.

Suggested Citation

  • Jochen Wulf & Jürg Meierhofer, 2025. "Large Language Models und Datenökosysteme zur Automatisierung des technischen Kundendienstes," Springer Books, in: Petra Kugler & Martin Dobler & Jürg Meierhofer & Marc Strittmatter & Manuel Treiterer & Helen Vogt (ed.), Data Sharing für KMU, chapter 0, pages 189-203, Springer.
    • Handle: RePEc:spr:sprchp:978-3-662-71209-2_11
    DOI: 10.1007/978-3-662-71209-2_11
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-662-71209-2_11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.