Predictive AI in Business Intelligence Enhancing Market Insights and Strategic Decision-Making

Anastasios, Papadopoulos and Maria, González (2024) Predictive AI in Business Intelligence Enhancing Market Insights and Strategic Decision-Making. American Journal of Technology Advancement, 1 (8). pp. 72-90. ISSN 2997-9382

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Abstract

In the rapidly evolving landscape of business, staying ahead of market trends and making informed strategic decisions are crucial for organizational success. Predictive Artificial Intelligence (AI) is emerging as a transformative force within business intelligence (BI), offering advanced tools for uncovering hidden patterns, forecasting future trends, and enhancing decision-making processes. This article explores the integration of predictive AI within BI systems and its profound impact on market insights and strategy development. We examine how AI-powered predictive models enable businesses to anticipate market fluctuations, optimize resource allocation, and refine marketing strategies. By analyzing real-time data, customer behavior, and industry trends, predictive AI facilitates data-driven decision-making that improves operational efficiency and competitiveness. Additionally, the article highlights key use cases across various industries, such as finance, retail, and healthcare, demonstrating the practical applications of predictive AI in shaping business strategies. Challenges, including data quality, model interpretability, and integration complexities, are also addressed, with a focus on overcoming these barriers to fully leverage the power of AI in business intelligence. Ultimately, this article underscores the transformative potential of predictive AI in redefining how businesses understand their markets, anticipate future opportunities, and make smarter, more strategic decisions.

Item Type:	Article
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	19 Mar 2025 09:22
Last Modified:	19 Mar 2025 09:22
URI:	http://eprints.umsida.ac.id/id/eprint/15851

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