THE IMPACT OF AI-DRIVEN FORECASTING SYSTEMS ON STRATEGIC DECISION-MAKING EFFICIENCY IN UZBEKISTAN'S TRANSPORT AND LOGISTICS ENTERPRISES
Keywords:
Artificial Intelligence, Predictive Analytics, Strategic Decision-Making, Logistics Management, Transport Enterprises, Uzbekistan.Abstract
As regional trade corridors expand, the integration of Artificial Intelligence (AI) into the strategic management of developing economies has moved from an operational upgrade to a core competitive necessity. This paper examines the impact of AI-driven forecasting systems on strategic decision-making efficiency within the transport and logistics enterprises of Uzbekistan. While predictive analytics, automated route optimization, and demand forecasting offer substantial advancements in strategic planning and resource utilization, local enterprises face distinct structural challenges, including data fragmentation, high initial capital requirements, and organizational resistance to algorithmic decision-making. Utilizing a mixed-methods approach that combines a conceptual analysis of Uzbekistan's "Digital Uzbekistan 2030" framework with practical insights from local logistics operators, this study evaluates how predictive technologies alter managerial frameworks. The findings demonstrate that relying solely on algorithmic outputs is insufficient; instead, optimal strategic efficiency is achieved when predictive AI is structured to complement, rather than replace, managerial intuition. Ultimately, the paper provides a practical roadmap for local logistics firms to navigate technological transition while mitigating operational and strategic risks.
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