Abstract

The integration of Artificial Intelligence (AI) in supply chain management has emerged as a transformative strategy to enhance efficiency, resilience, and adaptability in complex global logistics networks. This proposes a comprehensive model for AI integration in supply chain optimization, drawing comparative insights from the United States and Nigeria two economies with contrasting technological infrastructures, regulatory frameworks, and economic dynamics. The model emphasizes a multi-tiered approach incorporating predictive analytics, real-time decision-making, and intelligent automation. In the U.S., AI adoption is characterized by advanced infrastructure, data availability, and investment in digital transformation, enabling real-time inventory management, demand forecasting, and autonomous logistics. Conversely, Nigeria presents a context of infrastructural challenges and data limitations, yet exhibits increasing potential for AI-driven solutions through mobile technology, cloud computing, and emerging policy support. The proposed model comprises four core pillars: (1) AI-readiness assessment focusing on digital infrastructure and data maturity; (2) phased AI adoption strategy tailored to sector-specific needs; (3) stakeholder collaboration for technological transfer and capacity building; and (4) policy alignment to foster ethical AI use and cross-border interoperability. Through comparative analysis, the model identifies key enablers and barriers within each country, highlighting opportunities for technology leapfrogging in Nigeria and continuous innovation in the U.S. This argues that while the scale and mode of AI integration may differ, the strategic alignment of technology, talent, and governance is essential for effective supply chain optimization in both regions. This research contributes to the discourse on inclusive AI deployment by illustrating how adaptable frameworks can bridge technological disparities and drive supply chain resilience. Ultimately, it underscores the importance of localized strategies within a global model, fostering both economic growth and digital equity. The findings serve as a guide for policymakers, industry leaders, and technology developers seeking to harmonize AI integration across diverse socio-economic contexts.