Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/5338
Title: | Augmenting Pre-Award Contracting Processes with AI Technology |
Authors: | Alexander Olivo |
Keywords: | contracting artificial intelligence AI government contracting Department of Defense DoD Procurement Processes |
Issue Date: | 27-Feb-2025 |
Publisher: | Acquisition Research Program |
Series/Report no.: | Contract Management;NPS-CM-25-285 |
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. |
Description: | Contract Management / Graduate Student |
URI: | https://dair.nps.edu/handle/123456789/5338 |
Appears in Collections: | NPS Graduate Student Theses & Reports |
Files in This Item:
File | Description | Size | Format | |
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NPS-CM-25-285.pdf | Student Thesis | 1.33 MB | Adobe PDF | View/Open |
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