AI VS HUMAN CREATIVITY FOR DATA MANAGEMENT IN INTERNATIONAL BUSINESS

The article focuses on analyzing the interaction between artificial intelligence and human creativity in data management processes in international business. The purpose of the article is to study the peculiarities of the interaction between artificial intelligence and human creativity in international business data management, as well as to determine their contribution to the strategic and operational decision-making of global companies. The article systematizes approaches to assessing the role of artificial intelligence in international business and shows that AI provides high speed, accuracy, and scalability of analytical processes, while human creativity shapes the interpretation of the data obtained, creates a semantic context, and offers non-standard solutions. Using Porter's model, authors have distributed business functions between algorithmic capabilities and human creativity, demonstrating that optimal efficiency is achieved in hybrid models of collaboration. The study summarizes ten international case studies (Unilever, DHL, Netflix, Toyota, Meta, Amazon, Spotify, Zara, Siemens, Airbnb), which confirm that the combination of AI and human creative competencies strengthens the competitiveness of companies. It has been shown that algorithms are capable of forecasting demand, optimizing logistics operations, identifying risks, and analyzing behavioral data, while humans provide the strategic vision, cultural adaptation of decisions, ethical assessment, and generation of innovations necessary for complex international markets. Prospects for further research in this area include the development of models for evaluating the performance of hybrid solutions in transnational corporations, an in-depth study of the cross-cultural characteristics of creative thinking, the formation of ethical standards for the use of AI in global operations, and the analysis of organizational transformations associated with the growing role of artificial intelligence in data management.