IDEAS home Printed from https://ideas.repec.org/h/spr/prochp/978-3-030-90192-9_6.html
   My bibliography  Save this book chapter

Comparing the Methodology for the Development and Project Management of Artificial Intelligence Systems

In: Platforms and Artificial Intelligence

Author

Listed:
  • Timothy Shives

    (Naval Postgraduate School)

  • Thomas Housel

    (Naval Postgraduate School)

  • Johnathan Mun

    (Naval Postgraduate School)

  • Raymond Jones

    (Naval Postgraduate School)

Abstract

The acquisition of artificial intelligence (AI) systems is a relatively new challenge for the international community, but one organization that has placed a major interest in acquiring AI is the United States Department (U.S.) of Defense (DoD). This book chapter will focus on the DoD and its challenges in the development and fielding of major AI systems to glean lessons from addressing these challenges that might benefit the international community of project managers who must manage AI acquisition programs. The chapter will focus on the standard DoD acquisition program management methodology, i.e., Earned Value Management (EVM), and how it might be improved through incorporation of two methodologies, i.e., Integrated Risk Management (IRM) and Knowledge Value Added (KVA), in the managing of complex DoD information technology (i.e., AI) programs. This research compared and contrasted these three methodologies with the goal of demonstrating when and how each method can be applied to improve the acquisitions lifecycle for AI systems. Finally, the results of this study can also be applied to for-profit and other non-profit organizations throughout the international community.

Suggested Citation

  • Timothy Shives & Thomas Housel & Johnathan Mun & Raymond Jones, 2022. "Comparing the Methodology for the Development and Project Management of Artificial Intelligence Systems," Progress in IS, in: Ahmed Bounfour (ed.), Platforms and Artificial Intelligence, pages 111-145, Springer.
    • Handle: RePEc:spr:prochp:978-3-030-90192-9_6
    DOI: 10.1007/978-3-030-90192-9_6
    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.

    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:prochp:978-3-030-90192-9_6. 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.