Artificial intelligence is transforming maritime shipping. From optimizing routes to predicting maintenance needs, AI tools are reshaping operations. SeaEmploy explores current projects and forecasts how AI will redefine the future of the ship’s crew.
AI is no longer abstract. It’s improving safety, fuel efficiency, and navigation decisions on vessels today. Let’s dive into what’s happening and what’s next.
Current AI Projects and Impact on Maritime Shipping
- Hullbot – Autonomous Hull Cleaning
An AI-powered underwater robot operating on the Manly Fast Ferry in Australia. It uses sensors, brushes, and rollers to remove biofouling—a root cause of drag and fuel inefficiency. Trials show a 13% reduction in diesel use, helping cut emissions and maintenance needs
- HiNAS – AI-Controlled Autonomous Car Carriers
Developed by Hyundai Glovis in partnership with Avikus, the HiNAS system powers Level-2 autonomous navigation for large car-carrying ships. Scheduled for roll-out by mid‑2026, one vessel—“Sunrise”—will become the largest AI‑driven ship ever.
- CMA CGM + Google – AI for Logistics & Customer Service
French shipping giant CMA CGM is leveraging Google’s AI infrastructure to:
- Optimize container handling and route planning
- Automate inventory and documentation
- Enhance customer support and media operations
This is part of a broader digital transformation.
- Thales MMCM – AI-Driven Naval Mine Countermeasure System
A cutting-edge AI system jointly developed by the UK and France. It detects and neutralizes naval mines using autonomous drones and sonar—keeping crew safe and securing global maritime navigation.
- Bearing AI Deployment Planner – Smart Fleet Management
This AI tool helps liner shipping companies balance:
- Environmental regulations
- Operational demand
- Profit optimization
It simulates millions of operational scenarios to suggest fuel-efficient, emissions-compliant vessel deployment.
- Llamarine – Maritime Large Language Model
Introducing Llamarine, a domain-specific large language model (LLM) for maritime operations. Trained on navigation, regulatory, and operational data, it aids decision-making in route planning, risk assessment, and compliance.
- Maritime Collision Avoidance Systems (MCAS)
AI-enhanced collision systems combine radar, AIS, sonar, and thermal imaging to prevent accidents. Notable technologies include:
- Sea.AI: Detects non-transmitting obstacles using thermal imaging
2. Watchit.ai: Predicts risks and issues real-time alerts. These deliver aviation-level safety support to vessels.
- Autonomous Voyage Planning – Via Kaizen Project
Developed by Yara Marine Technologies, Molflow, and Swedish universities, this semi-autonomous system optimizes voyage efficiency. Utilizing AI tools like FuelOpt and Slipstream, it helps fine-tune propulsion and routing for energy savings.
- Autonomous Vessel Yara Birkeland
The Yara Birkeland is a fully autonomous, battery-powered container ship in Norway. It operates without crew between Herøya and Brevik, showcasing real-world deployment of AI in autonomous navigation.
- Wallenius Wilhelmsen & DeepSea – AI Voyage Optimization
Adopted across its fleet of 120+ ships, this AI tool—Performance Routing by DeepSea—generates optimal route and speed plans, targeting a 27.5% emission reduction by 2030
- Next-Gen AI Maps US Inland Traffic Patterns
A recent study uses machine learning and AIS data to accurately predict barge presence and convoy size in real-time. This breakthrough enhances planning for dredging, infrastructure investment, and logistics optimization.
Shaping Operations Today and Redefining Crew Futures
The global maritime AI market has surged past $4 billion in 2025, growing at 23% annually—proof that operators are betting on AI’s efficiency potential. Major carriers are also deep into trend. CMA CGM committed €500 million to AI investments, including a €100 million deal with Mistral AI to boost customer service.
AI won’t replace crews overnight—but it will change roles. Deckhands and navigators will lean on AI, not be side-lined. RAG (Retrieval-Augmented Generation) systems, predictive tools, and decision support help reduce human error and speed actions.
Maritime training is also evolving. AI-powered simulators recreate storms, equipment failure, and emergency scenarios. Crew members learn faster in safer virtual environments.
AI in Drilling, Dynamic Positioning, and Energy Systems
In the offshore oil and gas sector, AI tools are also rapidly deployed. Onboard rigs, drillships, and FPSOs, AI is used for:
- Dynamic Positioning (DP) control systems that adapt in real time to wind, wave, and load conditions
- Drill bit optimization—adjusting pressure and torque using ML to increase yield
- Subsea monitoring with AI image recognition for identifying corrosion and cracks
- Energy efficiency optimization for thruster load sharing and fuel reduction
For engineers offshore, this means:
- Learning to supervise AI-driven systems
- Understanding AI diagnostic tools
- Collaborating with remote control centers onshore
Some offshore roles—such as manual inspection divers, data loggers, and routine diagnostics techs—are likely to shrink or evolve into hybrid tech/AI monitoring roles.
AI Tools at Sea—For Smarter, Not Smaller Crews
AI tools in maritime shipping are real and rapidly evolving. From ships and offshore rigs to engine rooms and DP stations, automation is reshaping crew roles.
- Engineers now manage smart systems, not just engines
- Offshore crews work alongside AI for DP, drilling, and diagnostics
- Entire maritime workflows—from planning to performance—are AI-augmented
SeaEmploy encourages mariners to adapt early. Upskill in digital tools, system thinking, and AI integration. The future is not crewless—it’s crew-smart.
