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Al-Driven Decision Support Systems for Business Strategy

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Osita Victor Egwuatu *

MBA (Information Systems), College of Business and Innovation, The University of Toledo, Toledo, Ohio United States.

Review Article

World Journal of Advanced Research and Reviews, 2025, 27(02), 1752-1769

Article DOI: 10.30574/wjarr.2025.27.2.3065

DOI url: https://doi.org/10.30574/wjarr.2025.27.2.3065

Received on 16 July 2025; revised on 24 August 2025; accepted on 26 August 2025

This study explores how artificial intelligence (AI), specifically machine learning (ML), transforms Decision Support Systems (DSS) from descriptive tools into predictive and prescriptive engines for strategic decision-making. Using a case study in the retail sector, structured (sales, financials) and unstructured (reviews, social media) data were analyzed through supervised learning, natural language processing, and reinforcement learning models. Findings show improved predictive accuracy, customer retention, and sustainable pricing strategies compared to traditional IS/MBAs frameworks. The research contributes theoretically by extending DSS and IS literature, and practically by providing business leaders with actionable, AI-driven frameworks for long-term strategic agility.

Artificial Intelligence Governance; Enterprise Information Systems; Algorithmic Bias; Data Ethics and Privacy; Responsible AI Adoption; Regulatory Compliance

https://journalwjarr.com/sites/default/files/fulltext_pdf/WJARR-2025-3065.pdf

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Osita Victor Egwuatu. Al-Driven Decision Support Systems for Business Strategy. World Journal of Advanced Research and Reviews, 2025, 27(02), 1752-1769. Article DOI: https://doi.org/10.30574/wjarr.2025.27.2.3065.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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