Hybrid quantum AI in logistics and operations management: Operational fit and strategic value

Logistics optimization and operations management increasingly depend on artificial intelligence and quantum computing to address complex, time-sensitive planning problems. Existing research has focused mainly on algorithmic benchmarks or speculative quantum advantage, while offering limited insight into how Hybrid Quantum AI creates organizational value under current hardware constraints. This study advances theory by integrating Information Processing Theory (IPT) with the Dynamic Capabilities View (DCV), demonstrating how Hybrid Quantum AI architectures generate organizational value despite the current absence of quantum advantage. Using a case analysis of Malaysia Airlines Cargo (MAB Kargo), it is shown how combining AI-driven data structuring with quantum-enhanced optimization can close critical information-processing gaps and support the development of sensing, seizing, and transforming capabilities. Empirically, this hybrid system achieved an 83% reduction in planning time and a 5% increase in capacity utilization. These findings demonstrate that the current value of quantum computing in logistics lies not in outperforming classical solvers, but in enabling modular, future-ready architectures that improve operational fit today and build strategic agility over time. The study contributes to management and information systems research by explaining “quantum readiness” as an organizational capability enabled by modular digital infrastructure, offering practical guidance for digitization roadmaps and computational investment decisions in next-generation logistics.