Impact of Artificial Intelligence on Decision-Making in Information Systems

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

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Publication Details

Published
2025-02-19
DOI
https://doi.org/10.1109/icarc64760.2025.10962922
Primary Topic
Advanced Research in Systems and Signal Processing
Type
article
Field-Weighted Citation Impact
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Impact of Artificial Intelligence on Decision-Making in Information Systems

RWMR Dharani Rajasinghe
Advanced Research in Systems and Signal Processing
article

Impact of Artificial Intelligence on Decision-Making in Information Systems

RWMR Dharani Rajasinghe
article en

Abstract

The study examines how artificial intelligence (AI) affects information systems (IS) decision-making, emphasizing how AI improves precision, speed, and flexibility in a range of industries. AI presents potential and problems, including ethical, legal, and societal ramifications, as it revolutionizes organizational decision-making. In order to demonstrate AI's function in intricate IS systems, this paper examines a variety of AI strategies used in decision-making, assesses their efficacy using accepted metrics, and examines real-world case studies. Key findings address issues including data bias, lack of transparency, and job displacement while highlighting the advantages of AI in processing massive information and producing useful insights. The recommendations highlight the necessity of worker reskilling, sector-specific adaptability, explainable AI (XAI) models, and regulatory frameworks. In order to facilitate sustainable AI integration in IS for improved organizational and social results, this research emphasizes the significance of striking a balance between AI-driven innovation and ethical issues.

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Advanced Research in Systems and Signal Processing
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