Please use this identifier to cite or link to this item: https://dair.nps.edu/handle/123456789/5133
Title: Enhancing Acquisition Outcomes through Leveraging of Artificial Intelligence
Authors: Ryan Novak, Justin Raines
Christopher R. Barlow, Kevin M. Forbes
Rachel T. Giachinta, Jay Kim
Zachary G. Levenson, Stephen W. Roe
Keywords: Artificial Intelligence (AI)
Generative AI
Neural Networks
Computer Vision
Robotics
Maching Learning
Automation
Reinforcement Learning
Natural Language Processing (NLP)
Natural Language Generation (NLG)
Issue Date: 1-May-2024
Publisher: Acquisition Research Program
Citation: APA
Series/Report no.: Acquisition Management;SYM-AM-24-070
Abstract: The extraordinary advancement of Artificial intelligence (AI) technology emerges at a critical juncture in which the Federal acquisition workforce is ill-equipped to meet the sky rocketing demand for products and services, alike. AI poses the opportunity to overcome data-intensive, laborious tasks and expedite the speed in which acquisition professionals operate; potential benefits may increase efficiency, enhance transparency, and reduce workload. While the use of AI across the Federal Government differs between agencies, the significance and scrutiny of Government Acquisition makes implementing AI across the acquisition process uniquely challenging. This paper will explore the current state of AI; who (i.e., which agencies) and how AI currently supports the acquisition process across the Federal Government. Next, the future state of AI and anticipated applications for the acquisition community will be discussed…think the future, think the next generation of Acquisition! This will be developed through strategic exploration across thought leaders, academic research, and working within our own AI model for acquisition. Next, we will discuss how the risks of this new technology -- new tools and novel concepts -- introduce both procedural, ethical, and operational risks that must be taken into consideration. Finally, we will offer a set of recommendations on how best to implement AI in the acquisition process as well as a list of best practices to maximize utility, mitigate risks, and ensure the acquisition workforce is well positioned to embrace the benefits and efficiencies of integrating AI capabilities.
Description: SYM Paper
URI: https://dair.nps.edu/handle/123456789/5133
Appears in Collections:Annual Acquisition Research Symposium Proceedings & Presentations

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