ARTIFICIAL INTELLIGENCE AS A DRIVER OF DIGITAL TRANSFORMATION IN AGRICULTURE: PRODUCTIVITY GROWTH, DIGITALIZATION BARRIERS, AND STRUCTURAL INEQUALITY IN THE GLOBAL AGRO-ECONOMY

The rapid diffusion of artificial intelligence (AI) technologies has become one of the most significant structural transformations shaping the agricultural sector and the broader global economy. AI is increasingly recognized as a general-purpose technology capable of transforming agricultural productivity, rural labour markets, food supply chains, and the overall efficiency of agro-economic systems. In particular, AI-driven applications such as precision farming, satellite-based crop monitoring, automated irrigation systems, and predictive analytics are reshaping traditional agricultural production methods.

This paper examines the economic impact of artificial intelligence on agricultural productivity, employment structures in rural areas, and global agro-economic development. Using a comparative analytical approach, the research explores how AI adoption influences farm-level efficiency, resource allocation, and investment patterns in digital agriculture. The study also investigates how technological transformation affects total factor productivity in agriculture and contributes to structural changes in rural economies.

The findings highlight that AI contributes significantly to productivity growth in modern agricultural systems by improving decision-making, optimizing resource use, and reducing production risks. However, these benefits are not evenly distributed across regions and farming systems. Differences in digital infrastructure, technological readiness, access to capital, and institutional support create asymmetrical outcomes between developed and developing agricultural economies.

The study further emphasizes that while AI-driven digitalization enhances efficiency and sustainability in agriculture, it also introduces challenges related to inequality, digital exclusion of smallholder farmers, and dependency on advanced technological systems. The research discusses policy implications for promoting inclusive agricultural transformation through improved digital infrastructure, capacity building, and equitable access to agricultural technologies.