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

Timothy Shives (), Thomas Housel, Johnathan Mun and Raymond Jones
Additional contact information
Timothy Shives: Naval Postgraduate School
Thomas Housel: Naval Postgraduate School
Johnathan Mun: Naval Postgraduate School
Raymond Jones: Naval Postgraduate School

A chapter in Platforms and Artificial Intelligence, 2022, pp 111-145 from Springer

Abstract: 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.

Keywords: Acquisition; Artificial intelligence; Earned value management; Knowledge value added; Integrated risk management (search for similar items in EconPapers)
Date: 2022
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:prochp:978-3-030-90192-9_6

Ordering information: This item can be ordered from
http://www.springer.com/9783030901929

DOI: 10.1007/978-3-030-90192-9_6

Access Statistics for this chapter

More chapters in Progress in IS from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().