The biggest AI risk in shipping isn’t algorithms — it’s data quality
According to Indian Register of Shipping, the main failure point of AI in maritime operations is not the models themselves, but the degradation of data. AI is already widely used in navigation, predictive maintenance, and structural monitoring, directly impacting operational safety. However, in real-world conditions, data is far from perfect: sensors lose calibration, AIS and GPS signals become unstable, and system synchronization is often disrupted. Despite this, AI systems continue to produce “confident” outputs even as input data gradually diverges from reality, creating hidden and potentially critical risks. From an industry perspective, the core challenge is shifting toward robust data governance — including validation of data sources, cross-checking inputs rather than relying on a single source such as AIS, defining operational design domains (ODD), and continuously monitoring data degradation. The key takeaway for shipowners is clear: responsibility for decisions remains with the operator, not the algorithm. As AI adoption grows, insurers and financial institutions are increasingly focusing not on the presence of advanced technologies, but on the quality of data management and cyber resilience.