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Date of Award

5-2025

Document Type

Restricted Project: Campus only access

Degree Name

Master of Science in Information Systems and Technology

Department

Information and Decision Sciences

First Reader/Committee Chair

Shayo, Conrad

Abstract

This project employed a case study research strategy to investigate the integration of Generative Artificial Intelligence (GenAI) within public organization operations, focusing on the XYZ Organization (henceforth XYZ) and its adoption of XYZChat, a GenAI-powered chatbot. The research questions guiding this study were: Q1: What specific factors led XYZ Organization to adopt XYZChat? Q2: How exactly was GenAI integrated into the day-to-day work processes at XYZ Organization? Q3: What were the expected outcomes of XYZChat since its launch, and how do those expectations compare with the actual results observed? Using Yin’s Case Study approach, this study analyzed XYZChat, specifically focusing on its operation within a NIST 800-171 compliant environment designed to process Controlled Unclassified Information (CUI).

The findings of this research were: Q1: XYZ Organization adopted XYZChat primarily to enhance operational efficiency, streamline communication, and ensure stricter compliance with established data security standards. Q2: The integration of GenAI at XYZ Organization involved a carefully planned, multi-phase strategy, including comprehensive training programs for all employees, rigorous system testing, and strategic collaborations with AI vendors to ensure seamless integration with existing workflows. Q3: While XYZ Organization initially expected improved response times and reduced workloads, the actual results showed significant efficiency gains but also revealed challenges such as user adoption issues and occasional system errors, highlighting the need for continuous refinement.

The conclusions are: Q1: XYZ ORGANIZATION’s decision to adopt GenAI was driven by operational needs and strict compliance requirements, demonstrating AI’s potential to transform government operations while safeguarding security and privacy. Q2: Effective GenAI integration requires meticulous planning, stakeholder engagement, and iterative improvements addressing both technical and human factors. Q3: Realizing GenAI’s full potential in government settings demands addressing user concerns, enhancing system reliability, and fostering a culture of innovation and continuous improvement.

Future scope: Q1: Future research should focus on optimizing GenAI adoption in government while mitigating security, privacy, and compliance risks, including exploring innovative AI-driven defense mechanisms. Q2: Studies should develop strategies for improving user adoption and refining AI systems to perform optimally in complex government environments. Q3: To enhance GenAI integration, novel approaches to secure data handling and compliance should be investigated, leveraging cutting-edge AI technologies and collaborative frameworks to boost resilience against evolving cybersecurity threats.

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