Smart Warehouse Innovation through AI and Collaborative Robotics.

Abstract This study examines the limitations of traditional manual warehouse systems in meeting the increasing demands of modern e-commerce. It evaluates the integration of artificial intelligence (AI) and collaborative robotics to improve efficiency, coordination, and system responsiveness. A mixed-method approach is used, combining a systematic review of 189 recent studies with thematic analysis to identify key performance drivers. Based on these insights, an Integrated Smart Warehouse Optimisation Model is developed as a multi-objective framework targeting operational speed, energy efficiency, and human–robot collaboration. Results show substantial improvements across core functions. Computer vision reduces reception time by up to 79%, while machine learning-based picking systems decrease picking time by approximately 60%. Collaborative robotics further reduces cycle time by 33% and improves human learning rates by 45% in augmented environments. However, scalability constraints remain, particularly in high-volume inventory systems. The findings highlight the need to align technological capabilities with organisational readiness. A managerial framework is proposed to support implementation and reduce adoption risks. Future research should focus on large-scale empirical validation and the integration of sustainability considerations into warehouse system design. Keywords: Artificial Intelligence, AI, Warehouse, Warehousing, Warehouse Management, Machine Learning, Collaborative Robotics, Industry 5.0.