EFFECTIVENESS OF USING AI-BASED ANALYTICAL SYSTEMS IN INTERNATIONAL TRADE
Keywords:
artificial intelligence, international trade, analytical systemsAbstract
This scientific article analyzes the effectiveness of using artificial intelligence-based analytical systems in international trade processes based on a deep scientific, theoretical and practical approach. In the digital economy, the complexity of trade operations, the expansion of global supply chains, and the increase in real-time information flow require the improvement of analytical decision-making tools. The study reveals the role of AI-analytical tools in forecasting accuracy, risk assessment, logistics optimization, market situation determination, and the formation of trade strategies. Methodologically, the methods of systematic analysis, comparison, generalization of empirical results, and review of scientific sources were used. The results show that AI-based analytical systems are emerging as an important tool in increasing trade efficiency, reducing costs, and ensuring strategic stability.
References
1. Porter, M. E. (1985). Competitive Advantage: Creating and Sustaining Superior
Performance. New York: Free Press.
2. Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and
Prosperity in a Time of Brilliant Technologies. New York: W.W. Norton & Company.
3. Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics
of Artificial Intelligence. Harvard Business Review Press.
4. McKinsey & Company. (2020). The state of AI in 2020 – AI adoption and value creation in
international business. Retrieved from https://www.mckinsey.com/
5. World Trade Organization. (2022). Digital Trade and E-commerce Trends. Geneva: WTO
Publications. [Xalqaro savdo va raqamli transformatsiya]
6. OECD. (2021). AI and Data-Driven Innovation in Trade. Paris: OECD Publishing.
7. Chui, M., Manyika, J., & Miremadi, M. (2016). Where machines could replace humans—
and where they can’t (yet). McKinsey Quarterly.
8. Zhang, J., & Wang, L. (2019). Machine Learning for Supply Chain Forecasting: A
Comparative Analysis. International Journal of Production Economics, 208, 79–91.
9. Davenport, T. H., & Harris, J. G. (2007). Competing on Analytics: The New Science of
Winning. Boston: Harvard Business School Press.
10. Kshetri, N. (2018). 1 Blockchain’s roles in meeting key supply chain management objectives.
International Journal of Information Management, 39, 80–89.
11. IBM Institute for Business Value. (2020). Global AI Adoption in Business: Insights and
Trends. IBM Research. https://www.ibm.com/thought-leadership
12. Huang, G., & Rust, R. T. (2021). Artificial Intelligence in Service. Journal of Service
Research, 24(1), 3–21.
