Artificial Intelligence and Machine Learning Applications in Maritime Shipping: Predictive Analytics, Decision Support Systems, and Operational Automation - Tri Cahyadi; Winarno Winarno; Irfan Faozun; Larsen Barasa (Fundamental and Applied Management Journal, 2026)
The maritime shipping industry is increasingly investing in artificial intelligence and machine learning technologies to enhance operational efficiency, safety, and environmental performance, yet the strategic integration of these technologies within maritime organizational systems remains poorly understood. This study systematically examines AI and ML applications in maritime shipping by analyzing predictive analytics capabilities, decision support system architectures, and operational automation frameworks across key maritime operational domains. Employing a qualitative research design combining thematic analysis, cross-group comparison, and narrative synthesis, the study draws upon expert consultations and document analysis involving AI technology specialists, maritime operations professionals, decision science researchers, and maritime regulatory experts. The findings reveal four principal themes: the multi-layered architecture of AI applications spanning predictive, prescriptive, and autonomous functions; the critical role of data infrastructure and quality in determining AI effectiveness; the organizational and human factors mediating successful AI adoption; and the governance challenges of deploying AI in safety-critical maritime environments. The study contributes an integrated AI deployment framework linking technological capabilities, organizational readiness, and governance requirements, offering theoretical insights for technology management scholarship and practical guidance for maritime organizations navigating the complexities of intelligent system adoption.