Please use this identifier to cite or link to this item:
https://dair.nps.edu/handle/123456789/5360
Title: | AcquireAI - AI Platform for Acquisition Management |
Authors: | Pradeep Krishnanath |
Keywords: | Acquisition GenerativeAI |
Issue Date: | 1-May-2025 |
Publisher: | Acquisition Research Program |
Citation: | APA |
Series/Report no.: | Acquisition Management;SYM-AM-25-341 ;SYM-AM-25-374 |
Abstract: | The Department of Defense (DoD) acquisition process is a complex, time-consuming life cycle that often struggles to keep up with rapid technological advancement. This paper explores how generative artificial intelligence (AI) can significantly accelerate and enhance defense acquisitions by automating routine tasks and supporting human decision-making. Focusing on TechSur’s “AcquireAI” platform as a case study, we examine AI-driven efficiencies in acquisition planning, market research, drafting of Requests for Proposals (RFPs) and contracts, and source selection evaluations. Key research questions address integrating AI solutions into existing DoD procurement IT frameworks (like the Air Force’s CON-IT contract-writing system and KT File Share repository), ensuring regulatory compliance through AI-driven checks, and evaluating the impact on acquisition speed, cost, and accuracy. The paper outlines a comprehensive technical solution for deploying generative AI in secure DoD environments and presents anticipated improvements (e.g., substantial reductions in procurement lead times and administrative workloads). Our findings indicate that leveraging generative AI can enable faster acquisition cycles, enhanced compliance and transparency, and better allocation of human effort to high-value strategic activities—ultimately boosting mission readiness and return on investment in defense procurement. |
Description: | SYM Paper / SYM Presentation |
URI: | https://dair.nps.edu/handle/123456789/5360 |
Appears in Collections: | Annual Acquisition Research Symposium Proceedings & Presentations |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
SYM-AM-25-341.pdf | SYM Paper | 1.01 MB | Adobe PDF | View/Open |
SYM-AM-25-374.pdf | SYM Presentation | 2.4 MB | Adobe PDF | View/Open |
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