MANAGERIAL DECISIONS IN LARGE-SCALE AGRICULTURE THROUGH THE INTEGRATION OF AI

The adoption of artificial intelligence (AI) in managerial decision-making processes within large-scale agriculture represents a significant advancement in optimizing financial performance, resource allocation, and operational efficiency. This study examines the impact of AI integration by comparing traditional Enterprise Resource Planning (ERP) systems with a conceptual AI-driven model, emphasizing economic efficiency and sustainability. Through a structured analysis, the findings indicate a 13.5% increase in profitability per hectare and a 10% reduction in adjusted operational costs when AI-based automation is employed. The AI model enhances decision-making by incorporating real-time data from multispectral imaging, weather monitoring systems, and financial reports, generating predictive analytics that refine input allocation and risk management strategies. Furthermore, AI contributes to environmental sustainability by minimizing resource inefficiencies and reducing the ecological footprint of agricultural interventions. The results underscore the potential of AI as a transformative tool in agricultural management, advocating for further empirical validation and integration of AI-enhanced decision-making frameworks to improve resilience and long-term competitiveness in the sector.