Acquiring Artificial Intelligence Systems: Development Challenges, Implementation Risks, and Cost/Benefits Opportunities
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Authors
Mun, Johnathan
Housel, Thomas
Jones, Raymond
Carlton, Benjamin
Skots, Vladislav
Subjects
Advisors
Date of Issue
2020-06
Date
Publisher
American Society of Naval Engineers (ASNE)
Language
Abstract
The acquisition of artificial intelligence (AI) systems is a relatively new challenge for the U.S. Department of Defense (DoD). Given the potential for high-risk failures of AI system acquisitions, it is critical for the acquisition community to examine new analytical and decision-making approaches to managing the acquisition of these systems in addition to the existing approaches (e.g., Earned Value Management, or EVM). Also, many of these systems reside in small start-up or relatively immature system development companies, further clouding the acquisition process due to their unique business processes when compared to the large defense contractors. This can lead to limited access to data, information, and processes that are required in the standard DoD acquisition approach (i.e., the 5000 series). The well-known recurring problems in acquiring information technology automation within the DoD will likely be exacerbated in acquiring complex and risky AI systems. Therefore, more robust, agile, and analytically driven acquisition methodologies will be required to help avoid costly disasters in acquiring these kinds of systems. This research identifies, reviews, and proposes advanced quantitative, analytically based methods within the integrated risk management (IRM) and knowledge value added (KVA) methodologies to complement the current EVM approach.
Type
Article
Description
17 USC 105 interim-entered record; under review.
Series/Report No
Department
Organization
Naval Postgraduate School (U.S.)
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NPS Report Number
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Naval Postgraduate School
Funder
Format
16 p.
Citation
Mun, Johnathan, et al. "Acquiring Artificial Intelligence Systems: Development Challenges, Implementation Risks, and Cost/Benefits Opportunities." Naval Engineers Journal 132.2 (2020): 79-94.
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This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.