Artificial intelligence in shipping
AI is reshaping the maritime industry, enabling smarter, safer, and more sustainable operations
As shipping companies face increasing demands to cut costs, improve safety, and meet environmental regulations, artificial intelligence (AI) offers integrated solutions across key operational domains.
As an example, AI-driven predictive maintenance leverages real-time sensor data and machine learning to forecast equipment failures, replacing traditional time-based schedules with condition-based strategies.
Techniques such as vibration analysis, thermal imaging, and pressure monitoring are applied to engines and rotating machinery, extending asset life and reducing downtime. Offshore platforms, for instance, use AI to monitor riser integrity, detecting early signs of fatigue or corrosion. These models evolve with historical data, enabling dynamic fleet-wide maintenance planning.
Route optimisation
AI can also be used within systems that optimise voyage routes by analysing weather, ocean currents, traffic, and fuel prices. Using reinforcement learning and geospatial analytics, they minimise delays and fuel consumption while enhancing safety. Vessels can be rerouted to avoid storms without compromising delivery schedules. Integration with port logistics further reduces idle time and improves supply chain efficiency.
The technology can enhance fuel efficiency separately by monitoring propulsion systems, engine parameters, and vessel load distribution. It recommends operational adjustments such as speed optimisation, trim and ballast changes, and engine tuning. These measures help reduce greenhouse gas emissions, aligning with IMO decarbonisation goals and improving EEOI (Energy Efficiency Operating Indicator) and CII (Carbon Intensity Indicator) ratings.
Autonomous operations
AI enables autonomous vessels to operate with minimal human input through technologies like computer vision, sensor fusion, and deep learning-based decision algorithms. These systems detect obstacles, integrate radar and GPS data, and make real-time navigation decisions.
Autonomous ships offer improved safety, lower crew costs, and greater efficiency, especially in remote or high-risk environments such as Arctic routes.
AI is also useful when dealing with regulatory compliance, simplifying it by automating emissions monitoring, documentation, and reporting. It supports frameworks like EEXI (Energy Efficiency Existing Ship Index), CII, and SEEMP (the Ship Energy Efficiency Management Plan), generating real-time dashboards and reports. AI also aids carbon accounting and lifecycle emissions analysis, essential for regional regulations such as FuelEU Maritime and the EU ETS (Emissions Trading System).
Yet despite its potential, AI adoption in shipping faces hurdles including inconsistent data quality, cybersecurity risks, regulatory ambiguity, and a skills gap in digital competencies.
Future advancements will likely involve integration with blockchain, edge computing, and quantum analytics. Collaboration among shipowners, tech providers, regulators, and academia will be key to scaling AI solutions globally.
This article represents the views and thoughts of the author, and not necessary of IMarEST.
Dr. Biju George, PhD, CEng, CMarEng, FIMarEST, is a principal hull integrity engineer with over 30 years of experience in marine engineering and offshore asset integrity. A Fellow of IMarEST, he has led pioneering initiatives in AI-based risk management and digital transformation. His doctoral research focused on deep Q-reinforcement learning for offshore systems. Dr. George actively contributes to industry thought leadership and chairs the IMarEST South East England & London Home Counties branch.
Image: waves breaking on a ship’s bow. Credit: Shutterstock.
