IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v30y2023i3p113-119.html
   My bibliography  Save this article

Enterprise large language models: Knowledge characteristics, risks, and organizational activities

Author

Listed:
  • Daniel E. O'Leary

Abstract

Since the release of OpenAI's ChatGPT, there has been substantial interest in and concern about generative AI systems. This paper investigates some of the characteristics, risks, and limitations with the enterprise use of enterprise large language models. In so doing, we study the organizational impact, continuing a long line of research on that topic. This paper examines the impact on expertise, the organizational implications of multiple correlated but different responses to the same query, the potential concerns associated with sensitive information and intellectual property, and some applications that likely would not be appropriate for large language models. We also investigate the possibility of agents potentially manipulating the content in these large language models for their own benefit. Finally, we investigate the emerging phenomenon of “ChatBot Enterprise” versions, including some of the implications and concerns of such enterprise large language models.

Suggested Citation

  • Daniel E. O'Leary, 2023. "Enterprise large language models: Knowledge characteristics, risks, and organizational activities," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(3), pages 113-119, July.
  • Handle: RePEc:wly:isacfm:v:30:y:2023:i:3:p:113-119
    DOI: 10.1002/isaf.1541
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.1541
    Download Restriction: no

    File URL: https://libkey.io/10.1002/isaf.1541?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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:wly:isacfm:v:30:y:2023:i:3:p:113-119. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.