Artificial Intelligence in Logistics: A Conceptual Framework for Intelligent and Adaptive Logistics Systems

Artificial intelligence (AI) is rapidly transforming logistics operations, moving beyond isolated automation tools toward becoming a central intelligence that shapes planning, execution, and control across logistics networks. However, much of the existing literature treats AI as a discrete technological add-on applied to specific logistics functions such as forecasting or routing, rather than as an integrated capability that redefines logistics management as a whole. This paper adopts a conceptual approach to examine how AI reshapes logistics systems into intelligent and adaptive networks. We propose a conceptual framework in which AI functions as an embedded cognitive layer that senses operational conditions, learns from data, and continuously adjusts decisions across transportation, warehousing, and last-mile delivery activities. The framework links data infrastructure, AI-driven decision-making, and physical logistics operations, emphasizing adaptability and continuous learning as defining features of intelligent logistics systems. By focusing on the systemic role of AI rather than on specific tools or vendors, the study offers a higher-level understanding of how AI enables logistics networks to respond proactively to demand volatility, disruption, and operational uncertainty. The framework provides a theoretical foundation for understanding how AI-enabled logistics can support efficiency, resilience, and sustainability over the long term. This conceptual contribution is intended to guide future empirical research, case-based investigation, and practical implementation of AI-embedded logistics systems.