Chowdhury, Niladry and Hossain, Md. Iqbal (2025) Data-Driven Optimization in Modern Information Systems: A Multi-Sectoral Analysis of AI Integration, Risk Management, and Consumer Behavioral Dynamics. American Journal of Economics and Business Management, 8 (12). ISSN 2576-5973

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Abstract

The swift incorporation of artificial intelligence (AI), machine learning (ML), predictive analytics, and sophisticated automation tools, data-driven optimization has become an essential approach in contemporary information systems. The study explores how AI can contribute to boosting operational efficiency, advancing risk management, and facilitating consumer behavior analysis in various industries. By harnessing the power of AI, organizations can streamline repetitive tasks, process vast amounts of structured and unstructured data, and deliver meaningful insights to aid in decision-making and organizational efficiency. From optimizing resource allocation and boosting productivity in manufacturing and retail sectors to bolstering cybersecurity in finance, healthcare, and telecommunications, AI's potential to transform resource management and customer engagement is evident in various applications. The study also delves into the role of AI in risk management, examining how AI tools can enable real-time monitoring, predictive risk analysis, anomaly detection, and automated mitigation plans. Moreover, AI consumer analysis and predictive models ensure targeted marketing strategies, demand forecasting, customer retention and revenue optimization. Organizations can use these capabilities to predict market trends and react accordingly to shifting consumer trends. However, the implementation of AI has its drawbacks, such as privacy and security concerns, interoperability challenges, algorithmic bias, transparency and trust problems. Artificial Intelligence, the Internet of Things, Cloud Computing, and Industry 4.0 technologies are expected to converge and influence the future development, resulting in more intelligent and adaptive information systems. AI optimization is a paradigm shift in today's business landscape, and sustainable growth and emphasizing the importance of AI's evolving nature.

Item Type:	Article
Uncontrolled Keywords:	Artificial Intelligence, Data-Driven Optimization, Risk Management, Consumer Behavior Analytics, Predictive Analytics
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	01 Jul 2026 01:12
Last Modified:	01 Jul 2026 01:12
URI:	http://eprints.umsida.ac.id/id/eprint/16707

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