AI in shipping and the vital data foundations

Artificial Intelligence (AI) is already driving huge changes across the world and is starting to have an impact on the maritime industry, but its transformative potential depends on addressing fundamental challenges first. While AI promises increased efficiency, intelligent automation, and decision support, its adoption in shipping software requires a strong foundation of well-structured and engineered data, a shift in digital mentality, and a realistic approach to evaluating solutions.

As global shipping navigates regulatory change, supply chain disruptions, and sustainability pressures, we must retain some realism, whilst moving quickly to take advantage of these opportunities as they present themselves.

Commercial operations and chartering

AI-driven analytics can process vast amounts of data - market trends, weather conditions, port congestion, and fuel prices - to optimise voyage planning and chartering decisions, while predictive modelling can help ship operators select the most profitable voyages while minimising risks. There is no shortage of solutions entering the market in this space (for many more purposes than those mentioned) however, without well-structured data and a clear understanding of AI’s predictive capabilities, its effectiveness may be limited. If we look at other industries, “AI Powered” solutions are often sold as a black box, with some far-reaching claims around efficiency and effectiveness. We should be wary of this in maritime, and as suppliers of technology should be very transparent on how our solutions work, what data we use to model with and how those models make their predictions.

Fleet Management: Procurement, technical, and safety

There are significant and obvious savings and improvements to be made across the Fleet Management space, if data quality and digital adoption challenges are addressed:

• Procurement and inventory management: AI is well placed to help forecast spare part demand based on historical usage patterns, vessel-specific needs, and market conditions, reducing procurement costs and minimising stockouts or overstocking. Additionally Agentic AI may play a role in automating the ordering of these spare parts. However, ensuring that inventory and consumption data is structured correctly is essential for reliable predictions – a major part of this is working with the crew to ensure that software is being used correctly, and gathering the required data.
• Predictive maintenance: Driven by sensor data, there has been much activity in this space – however, for much of shipping, retrofitting vessels with numerous and often expensive sensors remains a barrier. There is opportunity here for machine learning to derive greater value that we currently do from the thousands of maintenance and breakdown records we gather.
• Safety and compliance: AI-driven insight and analysis can help us gain intelligence that may otherwise have been missed. However, human oversight is still crucial to help derive that operational intelligence. There are opportunities for solutions that automate, assist and give feedback to crews in real time in this space – helping the humans in control of vessels do their jobs safely.

Challenges and considerations

While AI presents huge opportunities, the maritime industry must first address underlying challenges such as data standardisation, cybersecurity risks, and the need for skilled personnel to manage AI-driven systems. More importantly, the industry must fully embrace digital transformation - not just AI solutions - by investing in high-quality data infrastructure and cultural shifts toward digitisation. Overpromising AI’s capabilities without resolving these foundational challenges may lead to suboptimal implementations and rejection of further new developments because of this.

The future of AI in maritime software

AI is not just an enhancement to existing shipping software - it has the potential to redefine how the maritime industry operates. To change workflows, operational processes and drive a step change in the efficiency of shipping companies in all areas. However, the success of AI depends on the industry’s ability to manage data effectively, shift its mindset toward digital transformation, and critically evaluate AI solutions. While AI will undoubtedly have a role to play in the evolution of the next generation of maritime software, early adopters must take a measured approach, ensuring that AI solutions are truly effective rather than just following industry hype. A cautious but forward-thinking strategy will help shape the future of smart shipping.