AI at Sea: From Connecting Vessels to Measurable Value
When artificial intelligence (AI) and digitalization are discussed in maritime, attention still gravitates toward autonomous ships. Yet the market is not about de-crewing but, rather, applying vessel and shore data to make operations safer, more fuel-efficient, and easier to maintain.
As of 2024, the maritime AI market stood at $4.13 billion, with a projected five-year CAGR of 23%. Market-size estimates should be treated cautiously, but the direction is hard to miss – AI solutions have advanced from trials into voyage planning, machinery monitoring, bridge decision support, port coordination, and digital assurance. The main questions are where it is producing measurable operational value and where the evidence still lags the marketing.
The Vessel Has to Be Connected First
AI does not create useful ship data – it consumes it. The connected vessel is thus a prerequisite for the AI-capable vessel. Propulsion load, fuel flow, vibration, temperature, pressure, weather, satellite, radar, and camera data are collected through the Internet of Things (IoT) sensors and existing shipboard systems, aggregated and pre-processed onboard, and transmitted to shore via secure communications links.
Data quality and context set the ceiling on model performance. Sensor drift, missing values, inconsistent naming, and different operating profiles can affect the lab-tested solution reliability. Latency is another factor – any critical computer vision or machinery control cannot rely on continuous cloud connectivity, so the processing must happen on board.
AI and digitalization should not be confused with automation in general. Maritime systems are usually a combination of physics-based models, optimization, rules, and machine learning, not a black-box approach. And sensors, inspection, and edge computing are as important as modeling. Leading companies focus on high-value and repeated use cases, hardware-software-AI integration, and actual customer data.
AI and digital tools also serve buyers differently. Table 1 maps key needs to the technologies most directly associated with them, distinguishing shipbuilders’ priorities from those of ship operators.
Table 1. What shipowners and shipbuilders want from AI, digitalization, and enabling tools
Source: author's analysis
Where the Numbers Are Already Visible
One of the clearest commercial applications is voyage optimization because fuel consumption provides an immediate KPI. In February 2026, NAPA Voyage Optimization trials demonstrated fuel savings of up to 7.1% on both long and shorter voyages. On the separate 34-day Lila Global voyage from Suez to China, NAPA reported $40,200 in voyage cost savings, 35 tons of fuel saved, 106.5 tons of CO2 avoided, and almost one day less steaming time compared to the simulated standard route. These case studies illustrate why voyage optimization is being scaled – even low-single-digit percentages matter at the fleet level.
Computer vision on the bridge is also moving beyond pilots. A 2026 Orca AI study covered 139 vessels and more than 10.8 million nautical miles. High-severity close encounters fell 52% between the early adaptation period and months 10-12. Lloyd's Register separately tested Orca AI on an 828-nautical-mile Mediterranean voyage — across 739 targets, the system achieved 94% precision and 98.6% recall, including small and low-radar-signature craft, with no system downtime. It does not mean that AI should substitute the officer on watch, but machine vision can provide an additional layer of attention when humans, radar, and Automatic Identification System (AIS) face certain limitations.
Maintenance presents another application with a measurable business case. Wärtsilä reports that AI-enabled diagnostics reduced unscheduled maintenance by 25% on average, 90% of issues were resolved remotely, and 80% within 24 hours. The operating model here matters – AI detects weak signals and anomalies while engineers validate the diagnosis and reaction. The product thus is not a single algorithm but a combination of sensors, analytics, and expertise.
Physical AI is also entering inspection and sustainment. Wall-climbing robots of Gecko Robotics are equipped with ultrasonic sensors to inspect the steel structure of vessels and collect data on its thickness and defects related to corrosion and degradation. In a U.S. Navy application, the system collected more than 4.2 million data points versus around 100 using traditional inspection methods, while a ship-rudder inspection fell from 11 days to one. The data feed digital condition models for maintenance planning and drydock decisions. This example shows another domain where physical AI produces value – by combining robotics with AI-driven analysis of data.
Beyond the Ship
Ports are another natural target due to the abundance of data on arrival times, berth availability, traffic, weather, and terminal capacity. A 2026 peer-reviewed study of an AI-enabled Just-In-Time (JIT) arrival system reported simulated fuel and emissions savings of 6.45%-7.39% where earlier coordination allowed ships to slow down.
Digital twins and data-driven health management are in a similar space – they compare actual behavior of machinery to the expected one to detect deterioration prior to failure. In May 2026, Lloyd's Register granted Approval in Principle to Navantia's AI-based Digital Twin Ready framework. In September 2026, LR also awarded AiP to HD KSOE's VesselWise autonomous auxiliary-machinery optimization function, with onboard validation planned on a 174,000-cbm LNG carrier. AI in maritime is moving from monitoring towards machinery optimization, but the assurance follows closely.
Generative AI is less mature operationally. It can make manuals, maintenance histories, and fleet data more searchable or assist shore teams with anomalies investigation. But for now, hard maritime KPI evidence is still scarce in this field, and errors in safety-critical workflows can be costly.
Taken together, these examples illustrate the variety of maritime AI applications and their different levels
of maturity and autonomy.
Figure 1. Maritime AI and digital tools applications: commercial maturity vs degree of autonomous action.
Source: author's analytical assessment based on public deployment. Scores are comparative,
The Hype Gap Is Mostly a Systems Problem
One of the major obstacles to using AI in maritime is not access to a model but integration. AI works in a closed loop – sensors generate data, the model discovers/predicts something valuable, someone makes the decision or controls systems according to the results, and outcomes become feedback. Any disconnected data or process break this loop.
The importance of cybersecurity grows as the loop closes. The connected maintenance platform is different from a spreadsheet, and AI integrated with navigation or machinery affects physical operations. IMO has repeatedly highlighted cyber risk across shipboard IT and operational technology. Sensor spoofing, corrupted data, compromised communications, and unauthorized access are therefore not separate IT issues – they are operational risks.
Human factors matter too. Too many alerts create alarm fatigue, unexplained recommendations may be ignored, and models can fail in unusual conditions. Crew training, interface design, decision responsibility, and fallback procedures are no less important than model accuracy.
From Fancy Tools to Operating System
Commercial applications include voyage and speed optimization, computer-vision watchkeeping, condition-based maintenance, and fleet analytics. They solve repetitive, data-rich problems measurable with KPIs on fuel, safety, or downtime.
The second group requires proving at scale – autonomous machinery control, AI-enabled digital twins, robotic inspection, generative copilots and highly autonomous ships. With AI moving closer to physical control, validation, cybersecurity, and class approval become more important.
For shipowners, the relevant question is not “Do we have AI?” but the decision affected, the data backing it, result measurement, and actions in case of error or offline mode. The winning applications would be those that make a few expensive decisions better.
Ultimately, digital tools and AI can help with detection and analysis, but human expertise would still be needed to interpret outputs, take decisions, and apply insights in practice.
▪ Sources
1. Lloyd's Register
2. Thetius
3. Bessemer Venture Partners
4. NAPA
5. Orca AI / NorthStandard
6. Wärtsilä
7. Gecko Robotics
8. Nguyen, S., Fu, X., Zhao, L., Xu, H., Zhang, X., Li, N., Zhang, W., Yin, X. F., Daichi, O., Koh, J., & Zheng, Q. (2026). AI-driven Just-In-Time coordination between port and ships: Advancing maritime decarbonization. Transportation Research Part E: Logistics and Transportation Review, 207, 104569. DOI: 10.1016/j.tre.2025.104569.
9. International Maritime Organization (IMO)
