Acquiring Artificial Intelligence Systems: Development Challenges, Implementation Risks, and Cost/Benefits Opportunities
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Authors
Mun, Johnathan
Housel, Thomas
Subjects
Advisors
Date of Issue
2020-04-15
Date
Publisher
Monterey, CA; Naval Postgraduate School
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 (i.e., 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
Report
Description
Panel #9: Artificial Intelligence and the Cloud
Series/Report No
Department
Identifiers
NPS Report Number
SYM-AM-20-067
Sponsors
Naval Postgraduate School
Funder
Format
26 p.
Citation
Distribution Statement
Approved for public release; distribution is unlimited.
Rights
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.