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Generative AI in Business Analytics: Opportunities and Risks for National Economic Growth
Abstract
Generative Artificial Intelligence (AI) is reshaping business analytics by enabling organizations and governments to generate insights, improve decision-making, optimize resources, and support data-driven economic planning. This study examines the role of generative AI-driven business analytics in national economic development, with particular attention to macroeconomic performance, financial inclusion, human capital, and sustainable growth. Using secondary data from the World Bank World Development Indicators, specifically the Economic Policy and Debt and Financial Sector datasets, the study analyzes five major economies: the United States, China, India, Japan, and Germany. A quantitative and comparative research approach was applied using Python, Tableau, and Microsoft Excel for data cleaning, descriptive analysis, correlation assessment, regression estimation, and visual interpretation. Key indicators included GDP growth, adjusted net national income, education expenditure, financial account ownership, unemployment, and gross savings. The findings indicate that stronger digital readiness, financial inclusion, and investment in education are positively associated with economic performance and innovation capacity. Emerging economies such as China and India demonstrate stronger growth-oriented effects from AI-enabled digital transformation, while developed economies such as the United States, Japan, and Germany show more stable patterns linked to efficiency, productivity, and sustainability. However, the study also identifies critical risks, including data inequality, automation-related job displacement, algorithmic bias, privacy concerns, and weak ethical governance. The results suggest that generative AI in business analytics represents both a strategic opportunity and a governance challenge. Sustainable economic progress will depend on how effectively countries balance AI innovation with human capital development, inclusive digital access, ethical regulation, and long-term policy planning.
Article information
Journal
Journal of Computer Science and Technology Studies
Volume (Issue)
8 (8)
Pages
16-32
Published
Copyright
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0/
Open access

This work is licensed under a Creative Commons Attribution 4.0 International License.

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