Journal of Ai ML DL
RESEARCH ARTICLE   (Open Access)

Artificial Intelligence (AI)-Powered Predictive Analytics: Driving Strategic Transformation in Business Analytics

Ariful Islam1*, Sonia Khan Papia2, Al Akhir1, Fahim Rahman3, Sonia Nashid4

+ Author Affiliations

Journal of Ai ML DL 1(1) 1-8 https://doi.org/10.25163/ai.1110372

Submitted: 02 July 2025  Revised: 04 September 2025  Published: 06 September 2025 

AI-powered predictive analytics enhances strategic decision-making, forecasting accuracy, and operational efficiency, driving competitive advantage in data-driven businesses.

Abstract


Background: As artificial intelligence reshapes the realm of business analytics & predictive analytics improving probability forecasting, leading to a more strategically informed long-term plan, and perhaps allow decisions to be made more quickly, it would make sense that organizations will continue to have demand for future-facing insights made possible through AI, as these features will be a sustainable competitive advantage to any organization, and can lead to productivity gains in any industry. Method: This is a mixed-methods study, where the data was collected from practitioners in finance, retail, manufacturing and technology using survey data and qualitative data from interviews with experts on AI and analytics. Survey participants were segmented into cohorts, based on their experience working with AI and urged to share how they were using AI tools, what benefits they were realizing and what obstacles they encountered. Results: The two main use cases of AI-empowered predictive analytics consist of prediction (32%) alongside consumer behavior analysis (26%). Several survey participants revealed their ability to achieve better forecasting accuracy (65%) while also making decisions at a faster pace (70%). The research data reveals an increasing number of participants who achieved success in demand planning (58%) together with risk management (52%). AI-empowered predictive analytics faces implementation obstacles from three main issues which include data quality problems (63%) and high implementation expenses (54%) and insufficient talent availability (49%). Conclusion: The studies hypothesis received support from the analysis which shows AI-empowered predictive analytics delivers substantial strategic value.

Keywords: Artificial Intelligence, Predictive Analytics, Business Analytics, Data-Driven Decision-Making, AI Adoption Challenges

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