AUGMENTING PRE-AWARD CONTRACTING PROCESSES WITH AI TECHNOLOGY
Authors
Olivo, Alexander
Advisors
Yoder, E. Cory
Augier, Mie-Sophia E.
Second Readers
Subjects
contracting
artificial intelligence
AI
government contracting
Department of Defense
DoD
Procurement Processes
Marine Corps contracting
pre-award contracting challenges
requisition data analysis
Defense Agencies Initiative
DAI
barriers to AI Adoption in DoD
change management in military contracting
AI pilot programs in defense contracting
innovative approaches to DoD contracting processes
artificial intelligence
AI
government contracting
Department of Defense
DoD
Procurement Processes
Marine Corps contracting
pre-award contracting challenges
requisition data analysis
Defense Agencies Initiative
DAI
barriers to AI Adoption in DoD
change management in military contracting
AI pilot programs in defense contracting
innovative approaches to DoD contracting processes
Date of Issue
2024-12
Date
Publisher
Monterey, CA; Naval Postgraduate School
Language
Abstract
This study evaluates how artificial intelligence (AI) can enhance the Department of Defense’s (DoD) pre-award contracting process, with a focus on the Marine Corps. Through a review of relevant literature and an analysis of requisition data from the Defense Agencies Initiative (DAI) for the MCI-East Regional Contracting Office (RCO) during fiscal year 2024, the research identifies critical challenges in requirements generation, including documentation errors, approval delays, and inconsistent requirements. To address these challenges, the study assesses the feasibility of AI integration, considering barriers such as resistance to change, regulatory constraints, and the need for extensive training required prior to implementation. Using qualitative and quantitative analysis methods, the research suggests that AI tools could streamline documentation, reduce processing times, and improve the accuracy of requirements. Based on these findings, the study proposes pilot programs to test AI solutions in a controlled environment. Recommendations emphasize change management practices, tailored training programs, and updates to regulatory policies to support AI adoption. The results suggest AI has potential to significantly improve efficiency, reduce errors, and modernize the pre-award contracting process, offering actionable insights for the DoD’s contracting community.
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Distribution Statement
Distribution Statement A. 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.
