AI in Maritime Operations: Decision Support and Accountability

𝐀𝐈 𝐢𝐧 𝐌𝐚𝐫𝐢𝐭𝐢𝐦𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐒𝐮𝐩𝐩𝐨𝐫𝐭, 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲

Artificial intelligence is becoming part of the wider digitalisation of shipping. From analysing vessel performance and identifying maintenance trends to supporting voyage planning and reviewing operational records, AI-based tools offer new ways to process information and assist decision-making.

However, the practical question for shipowners and ship managers is not simply what AI can do. It is how these tools can be introduced into existing ship-management systems without weakening operational control, professional judgement or accountability.

The value of AI in maritime operations depends on three fundamentals: reliable data, clearly defined responsibilities and effective human oversight.

𝟏. 𝐖𝐡𝐞𝐫𝐞 𝐀𝐈 𝐂𝐚𝐧 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐌𝐚𝐫𝐢𝐭𝐢𝐦𝐞 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬

Ships and shore-based management teams work with large volumes of information. This includes machinery parameters, alarm histories, planned maintenance records, fuel consumption, voyage data, cargo documentation, inspection findings and reports from vessels.

Reviewing these records manually can be time-consuming, particularly when information is spread across different systems or when a developing trend is not immediately apparent.

AI-based tools can assist by identifying patterns, comparing records, summarising information and highlighting deviations that require further investigation.

Potential applications include:

• 𝐕𝐞𝐬𝐬𝐞𝐥 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: identifying changes in fuel consumption, speed, power demand or other operating indicators that warrant investigation.
• 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: identifying patterns in machinery data that may indicate deterioration or an increased likelihood of failure.
• 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐝𝐞𝐟𝐞𝐜𝐭 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: grouping recurring defects across vessels and highlighting common equipment, operating conditions or maintenance issues.
• 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐫𝐞𝐯𝐢𝐞𝐰: helping teams examine reports, certificates, procedures and maintenance records more efficiently.
• 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐢𝐬𝐤 𝐚𝐬𝐬𝐞𝐬𝐬𝐦𝐞𝐧𝐭: organising available information to help shore personnel identify issues requiring closer attention.
• 𝐕𝐨𝐲𝐚𝐠𝐞 𝐚𝐧𝐝 𝐞𝐧𝐯𝐢𝐫𝐨𝐧𝐦𝐞𝐧𝐭𝐚𝐥 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬: supporting the review of weather, route, consumption and operational data within the limits of the systems and information available.

These are decision-support applications. They can help personnel identify where attention may be needed, but a detected pattern is not automatically proof of a defect, and a predicted outcome is not a confirmed event.

For example, an increase in machinery temperature may be associated with several possible causes. AI may help identify the trend and compare it with historical operating data. Determining whether the change results from a developing fault, a different operating condition, a sensor problem or another factor still requires technical verification.

𝟐. 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 𝐂𝐨𝐦𝐞𝐬 𝐁𝐞𝐟𝐨𝐫𝐞 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐭 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬

An AI system cannot reliably compensate for information that is missing, inconsistent, incorrectly recorded or taken out of context.

Maritime data may originate from onboard sensors, equipment monitoring systems, planned maintenance software, noon reports, engine-room records and shore-based applications. Differences in reporting intervals, units, equipment identifiers and recording practices can affect how the information is interpreted.

A value may appear unusual because the machinery is developing a problem. It may also appear unusual because the operating conditions have changed or the underlying data are inaccurate.

Before relying on AI-generated analysis, ship managers should consider several questions:

• Is the source of the data known and suitable for the intended purpose?
• Are timestamps, units and equipment identifiers consistent?
• Are important records missing or delayed?
• Has the system been tested against representative operating conditions?
• Can the user trace a recommendation back to the underlying information?
• How does the system behave when the available data are incomplete or contradictory?

Data quality should therefore be treated as an operational requirement, not merely an IT concern.

Where a recommendation could affect machinery operation, maintenance priorities, navigation or safety, the underlying evidence must be sufficiently reliable for the decision being considered.

𝟑. 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 𝐈𝐬 𝐍𝐨𝐭 𝐭𝐡𝐞 𝐒𝐚𝐦𝐞 𝐚𝐬 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧

AI and automation are related, but they are not interchangeable.

A decision-support system provides information, analysis or recommendations for a person to evaluate. An automated system performs a defined function according to its design, logic, configured limits and operating conditions. Some modern systems combine both approaches.

This distinction matters because the level of control and verification required depends on what the technology actually does.

Consider three examples:

𝐀𝐈-𝐚𝐬𝐬𝐢𝐬𝐭𝐞𝐝 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞

A system analyses vibration and temperature trends and flags a possible machinery problem. The responsible technical team reviews the data, checks the equipment history and arranges appropriate inspection or testing.

𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐦𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐚𝐧𝐝 𝐚𝐥𝐚𝐫𝐦𝐬

A monitoring system detects a parameter outside its configured limit and generates an alarm. The alarm logic, sensor integrity, response procedure and escalation arrangements remain important, regardless of whether AI is also used elsewhere in the process.

𝐀𝐈-𝐠𝐞𝐧𝐞𝐫𝐚𝐭𝐞𝐝 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐫𝐞𝐜𝐨𝐦𝐦𝐞𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬

A tool compares voyage information and suggests an alternative route or operating adjustment. The recommendation must be assessed against weather, navigational safety, vessel limitations, operational requirements and the information available to the responsible personnel.

In each case, the technology may improve efficiency or help identify an issue earlier. However, its role must be clearly defined, and its output must not be treated as authoritative simply because it is generated by an advanced system.

𝟒. 𝐇𝐮𝐦𝐚𝐧 𝐎𝐯𝐞𝐫𝐬𝐢𝐠𝐡𝐭 𝐌𝐮𝐬𝐭 𝐁𝐞 𝐌𝐞𝐚𝐧𝐢𝐧𝐠𝐟𝐮𝐥

Human oversight is not achieved merely by requiring someone to click an approval button.

The person reviewing an AI-generated recommendation must have enough information, competence, time and authority to assess it. The process should also make clear when independent verification, escalation or rejection is necessary.

For shipboard and shore-based teams, this means establishing practical arrangements for:

• identifying which decisions may be supported by AI;
• defining decisions that require independent technical or operational verification;
• specifying who is authorised to accept, reject or escalate a recommendation;
• providing a means to challenge an output that conflicts with observed conditions or established procedures;
• recording material recommendations, checks and decisions where appropriate; and
• defining what happens when the system is unavailable, produces inconsistent results or operates outside its validated limits.

The appropriate arrangements will differ according to the application. A tool used to summarise routine reports does not present the same operational consequences as one influencing navigation, machinery control or cargo operations.

The greater the potential consequence of an incorrect recommendation, the more important it becomes to define the system's limitations, verification requirements and fallback arrangements.

𝟓. 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐂𝐚𝐧𝐧𝐨𝐭 𝐁𝐞 𝐃𝐞𝐥𝐞𝐠𝐚𝐭𝐞𝐝 𝐭𝐨 𝐚𝐧 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦

Introducing AI does not remove the need for clear responsibility within the vessel's and company's management structures.

The master, chief engineer, designated shore personnel, technical superintendents and other responsible staff have different duties depending on the decision and the applicable requirements. An AI tool does not replace these established roles.

The company should therefore define who owns each decision and how the use of AI fits within existing procedures and the Safety Management System.

For example, if an AI-based maintenance tool repeatedly classifies a defect as low priority, the company needs a way to challenge that assessment when equipment condition, inspection findings or operating experience indicate otherwise.

Similarly, if a generated report contains an incorrect technical statement, responsibility for reviewing and approving the final document should remain clearly assigned to the appropriate person.

𝐀 𝐮𝐬𝐞𝐟𝐮𝐥 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞 𝐢𝐬 𝐬𝐭𝐫𝐚𝐢𝐠𝐡𝐭𝐟𝐨𝐫𝐰𝐚𝐫𝐝: the person responsible for a decision must be able to understand the basis on which it is made, verify material information and act when the recommendation is not credible.

Records should support this process where the decision warrants it. They should not become a substitute for sound judgement or create unnecessary administrative work.

𝟔. 𝐂𝐲𝐛𝐞𝐫𝐬𝐞𝐜𝐮𝐫𝐢𝐭𝐲 𝐚𝐧𝐝 𝐒𝐲𝐬𝐭𝐞𝐦 𝐑𝐞𝐥𝐢𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐌𝐮𝐬𝐭 𝐁𝐞 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐞𝐝

AI tools introduce or extend dependencies on software, data, connectivity and external service providers. These dependencies need to be considered as part of the company's wider digital and cyber-risk management.

Potential concerns include unauthorised access, compromised information, unreliable data feeds, software changes, service interruptions and inappropriate integration between business IT systems and systems supporting vessel operations.

The level of risk depends on the system and how it is connected. A document-analysis tool operating in a controlled environment has a different risk profile from a system integrated with operational technology or used to influence safety-critical functions.

Before implementation, companies should establish:

• what information the system can access;
• whether data are transmitted to external services and how they are protected;
• who can change system settings, permissions or configurations;
• how software updates and service-provider dependencies are managed;
• how outputs and system behaviour are monitored; and
• what alternative process is available if the system fails or becomes unreliable.

AI should be introduced within the company's applicable cybersecurity arrangements, not treated as a separate technology that sits outside them.

𝟕. 𝐑𝐞𝐠𝐮𝐥𝐚𝐭𝐨𝐫𝐲 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐖𝐢𝐭𝐡𝐢𝐧 𝐄𝐱𝐢𝐬𝐭𝐢𝐧𝐠 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬

The regulatory position must be understood according to the technology being introduced.

In May 2026, the International Maritime Organization adopted the non-mandatory International Code of Safety for Maritime Autonomous Surface Ships (MASS Code), which took effect on 1 July 2026. The Code provides a framework for the safety of relevant remotely operated and autonomous ships and addresses matters including risk assessment, operational limitations and human oversight.

This development is important, but it should not be confused with a general approval framework for every AI application used in shipping. A conventional vessel using AI to analyse fuel consumption, maintenance records or operational reports does not become a maritime autonomous surface ship simply because the technology is used.

For ship managers, implementation should be assessed against the actual function of the system, the vessel's operating arrangements, applicable international and flag-state requirements, class requirements where relevant, and the company's established management procedures.

Where AI is integrated with equipment or functions affecting safe operation, the implications for system approval, risk assessment, cybersecurity, personnel competence and emergency arrangements should be considered before the system is placed into operational use.

Regulatory compliance remains a necessary baseline. It does not, by itself, demonstrate that a particular AI application is suitable for every operational situation.

𝟖. 𝐀 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧

For most ship-management companies, introducing AI is better approached as a controlled operational improvement than as a fleet-wide technology exercise.

A sensible process can be organised into six steps.

𝟏. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐚 𝐝𝐞𝐟𝐢𝐧𝐞𝐝 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Select a task where better analysis or information handling could produce a measurable benefit, such as identifying recurring defects or reducing the time needed to review technical reports.

𝟐. 𝐄𝐬𝐭𝐚𝐛𝐥𝐢𝐬𝐡 𝐚 𝐛𝐚𝐬𝐞𝐥𝐢𝐧𝐞. Determine how the task is currently performed, what information is available, how errors are detected and what level of effort is required.

𝟑. 𝐀𝐬𝐬𝐞𝐬𝐬 𝐭𝐡𝐞 𝐫𝐢𝐬𝐤𝐬 𝐚𝐧𝐝 𝐥𝐢𝐦𝐢𝐭𝐚𝐭𝐢𝐨𝐧𝐬. Consider data quality, potential consequences of incorrect outputs, cybersecurity, system dependencies and the need for human verification.

𝟒. 𝐓𝐞𝐬𝐭 𝐚𝐠𝐚𝐢𝐧𝐬𝐭 𝐫𝐞𝐚𝐥 𝐨𝐩𝐞𝐫𝐚𝐭𝐢𝐧𝐠 𝐜𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐬. Use representative information and compare the system's outputs with verified records and competent professional assessment. Include incomplete data and unusual cases, not only straightforward examples.

𝟓. 𝐃𝐞𝐟𝐢𝐧𝐞 𝐫𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐚𝐧𝐝 𝐩𝐫𝐨𝐜𝐞𝐝𝐮𝐫𝐞𝐬. Establish who uses the tool, who checks its output, what decisions it may support, when escalation is required and what to do if the system is unavailable.

𝟔. 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐚𝐟𝐭𝐞𝐫 𝐢𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧. Review accuracy, recurring errors, user feedback, changes in data quality and any unintended effects. Reassess the system when its purpose, data sources or operating environment changes.

The success criteria should be operational. Time saved is useful, but it should be considered alongside reliability, the quality of decisions, workload, traceability and any impact on safety or compliance.

𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧: Technology Must Strengthen Operational Control

AI can help maritime organisations analyse information, recognise patterns and direct attention towards issues that might otherwise be overlooked. Used appropriately, it can support more informed maintenance planning, performance monitoring and shore-based decision-making.

Its effectiveness, however, depends on more than the capabilities of the software.

Reliable data, clearly defined limits, cybersecurity, competent users and meaningful verification are essential. Responsibilities must remain clear, and established procedures must provide a practical way to challenge questionable outputs and respond when a system fails.

For shipowners and ship managers, the objective should not be to automate every available task. It should be to identify where technology can improve the quality and timeliness of information while preserving safe operations and sound professional judgement.

In maritime operations, the value of AI is ultimately measured not by how much it can generate or automate, but by whether it helps people make better-informed decisions while maintaining effective control and accountability.

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References for further reading

1. International Maritime Organization (IMO) — IMO adopts first global Code for autonomous ships.
2. International Maritime Organization (IMO) — Autonomous shipping: frequently asked questions.
3. International Maritime Organization (IMO) — Maritime cyber risk.
4. International Association of Classification Societies (IACS) — Addressing cyber resilience of ships.