AI-driven business analytics for operational efficiency
1 MS. Business Analytics (2025), Trine University, USA.
2 MSc. Digital Business Management (2022), University of Portsmouth, UK.
3 BBA. Accounting (2019), Army Institute of Business Administration, (Affiliated with the BUP), Bangladesh.
4 International Institute of Business Analysis.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 12(02), 535–543.
Article DOI: 10.30574/wjaets.2024.12.2.0329
Publication history:
Received on 23 June 2024; revised on 29 July 2024; accepted on 01 August 2024
Abstract:
This research paper explores the integration of artificial intelligence (AI) in business analytics and its impact on operational efficiency. Business analytics traditionally relies on historical data and statistical methods to optimize processes and decision-making. However, with the advent of AI technologies such as machine learning and natural language processing, businesses can now leverage advanced analytics to enhance operational performance. This study investigates how AI-driven analytics can address existing limitations in traditional business analytics by providing real-time insights, predictive capabilities, and automation. By reviewing case studies and empirical evidence, the paper highlights the improvements in operational efficiency achieved through AI technologies. The findings demonstrate that AI not only streamlines processes but also drives strategic decision-making, leading to significant gains in productivity and cost-efficiency. The research identifies practical implications for organizations, discusses challenges, and suggests future research directions in AI-driven business analytics.
Keywords:
Artificial Intelligence; Business Analytics; Operational Efficiency; Machine Learning; Predictive Analytics; Data-Driven Decision Making
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Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0