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Volume 1 Number 1 2025

Article Contents

RESEARCH ARTICLE   (Open Access)
Contents Vol 1 (1)

Empowering Strategic Decision-Making Through AI-Driven Business Analytics

Ariful Islam1*, Al Akhir1, Fahim Rahman2, Sonia Nashid3, Sonia Khan Papia4

+ Author Affiliations

Paradise 1 (1) 1-7 https://doi.org/10.25163/paradise.1110333

Submitted: 11 February 2024 Revised: 20 January 2025  Published: 21 January 2025 

Abstract

Background: The coming together of Artificial Intelligence (AI) and Business Analytics (BA) is changing strategic decision-making in modern enterprises. Traditional analytics do not quite fulfill the requirements set by the speed, size, and multiplicity of the contemporary business data environment. AI technologies such as machine learning and deep learning along with natural language processing enable real-time interpretation and predictive modeling, thus bringing increased agility and competitiveness. Methods: The qualitative literature review combined with quantitative analysis on randomly generated datasets from four sectors: retail, finance, healthcare, and manufacturing. AI algorithms were simulated using Python to evaluate their impact on key performance indicators for strategic decision-making. Results: AI-driven analytics augmented decision quality and operational efficiency across all the sectors understudy.AI has taken customer segment analysis in retail to another level with a 12% increase in sales. The Finance department's accuracy of fraud detection rates is successful at 35%. The uses of predictive maintenance in healthcare offered almost a 20% increase in diagnostic accuracy rate, while predictive maintenance performed very well, reducing down-time in manufacturing by another 17%. AI differs from traditional analytics in many important ways, including agility, accuracy, and unlimited customer insights. The other issues of algorithmic bias, data privacy, high implementation costs, etc., are many, but the advantages far outweigh the issues. Conclusion: This research proposes the development of ethical and explainable frameworks for an AI system so that sustainable adoption and value creation acquisition can take place for business concerns.

Keywords: Artificial Intelligence, Business Analytics, Strategic Decision-Making, Machine Learning, Predictive Analytics, Organizational Efficiency.

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