RESOURCE MANAGEMENT IN HISTORICAL AND MODERN CONTEXTS: FROM TRADITIONAL LAND TAXATION TO AI-DRIVEN STRATEGIC RESOURCE ALLOCATION

Abstract

Resource management has historically played a central role in economic development, governance, and social stability. Traditional resource and land taxation systems, particularly in Central Asia, served as mechanisms for regulating the use of natural resources, ensuring fairness among economic actors, and sustaining state finances. In modern economies, the emergence of Artificial Intelligence (AI) has profoundly transformed strategic resource allocation by optimizing financial, human, and operational decision-making. This article integrates historical perspectives on resource taxation with contemporary AI-driven resource management practices. It examines the evolution of land taxes, their socio-economic functions in the region, and compares them with AI-based models for optimizing resources in finance, workforce planning, and supply chain management. The study concludes that while the fundamental goal of resource management—achieving fairness, efficiency, and sustainability—remains consistent across eras, AI technologies significantly enhance precision, transparency, and long-term sustainability.