Smart Shipping: Are Current Models Too Basic or Even Useless?

As the maritime industry embraces digital transformation, the adoption of smart shipping technologies is accelerating. Yet, there is growing concern that some of the models currently used in “smart” systems may be too simplistic—or in some cases, not useful at all.

Many existing models are trained on limited datasets, often collected under narrow operational conditions. This raises doubts about their reliability, scalability, and real-world utility in the face of complex maritime environments. Are these models genuinely delivering value, or are they just digital placeholders offering little real-time insight or operational advantage?

A new wave of developments from major players in AI and cloud services (e.g. Google, Amazon, Microsoft) may soon redefine this space. With access to massive training datasets and superior machine learning infrastructure, these companies are increasingly offering off-the-shelf models for predictive maintenance, fuel optimization, and routing.

This begs the question: will shipowners and software vendors continue developing their own niche models, or will the industry shift towards integrating robust, general-purpose models from tech giants—then customizing them for maritime needs?

In the near future, smarter shipping may not come from building more models, but from selecting better ones.