Generative AI in Maritime Industry: A Beginner’s Guide
Maritime has never been short on data. What it’s been short on is time—time to search through manuals during a machinery issue, time to stitch together an incident narrative from multiple emails, time to prepare an inspection evidence pack, time to interpret port disruption notices, time to standardize defect reports across vessels, time to translate technical guidance for a multinational crew, and time to convert all of that knowledge into repeatable best practice.
That’s exactly why Generative AI (GenAI) is showing up across shipping, ports, class, logistics, and maritime services. Not as “science fiction automation,” but as a practical layer that helps people find, understand, and produce operational work faster—while keeping humans responsible for safety-critical decisions.
This guide is written for any maritime leader or maritime professional who’s new to the topic and wants a clear, realistic understanding of:
what GenAI is (and what it isn’t),
where it’s already being used in the industry (real examples),
high-value GenAI use cases across maritime domains,
how to deploy it safely (cyber + governance),
and how to measure impact with the right KPIs.
1) What is Generative AI in plain maritime terms?
Generative AI refers to AI systems (usually large language models, or LLMs) that can generate new content—like text, summaries, checklists, emails, procedures, explanations—based on patterns learned from large datasets. Think of it as a powerful “language engine” that can read and write in a human-like way.
ABB describes GenAI as using large language models to create fresh content based on learned patterns from data.
GenAI is not a magic brain
A critical beginner point: a general-purpose LLM does not “know your ship.” It doesn’t automatically know which manual revision applies to which vessel, what your SMS requires, what your superintendent prefers, or what your last five defects looked like.
That’s why serious maritime GenAI systems are usually designed to work like this:
Retrieve relevant information from trusted sources (manuals, service letters, SMS, defect history, incident reports)
Generate a response using the retrieved content
Cite sources so humans can verify
This pattern is commonly called RAG (Retrieval-Augmented Generation).
2) Why maritime is an unusually good fit for GenAI
Maritime operations combine three conditions where GenAI excels:
A) Document-heavy reality
Ships and shipping companies run on documents:
OEM manuals, service letters, circulars, drawings
SMS procedures, checklists, permits
inspection evidence, audit trails
incident reports, near-miss records
port instructions, cargo documents, voyage messages
Crew rotation and operational urgency create a constant gap between:
what the organization knows,
and what is accessible at the moment of need.
GenAI reduces that gap by making knowledge queryable and reusable.
3) Reality check: where risk and value concentrate
When deciding where to start, use industry signals. The Allianz Safety & Shipping Review 2025 reports that machinery damage/failure accounted for well over half of all shipping incidents globally (1,860) in 2024, followed by collision (251) and fire/explosion (250).
That doesn’t mean “GenAI fixes machinery.” It means the highest leverage beginner deployments are often around:
faster troubleshooting knowledge access,
better defect documentation,
stronger preventive learning from incidents,
and more consistent maintenance communication.
4) “Live” maritime use cases happening in the real world
Below are publicly reported, concrete examples (not theoretical slides).
A) SmartSeas.AI: Using Generative AI for Maritime Troubleshooting & Reliability
SmartSeas.ai is an example of generative AI being applied to everyday maritime operations—especially where teams need fast, accurate answers from scattered technical knowledge. SmartSeas.ai presents itself as an AI-powered maritime troubleshooting platform designed to make technical support and knowledge access quicker and easier.
What SmartSeas.ai uses GenAI for (practical workflows)
Real-time, ship-specific troubleshooting: SmartSeas.ai states its AI assistant provides instant, ship-specific solutions to resolve technical issues and reduce downtime.
Multilingual + voice-based interaction: The platform highlights multilingual and voice support so users can ask questions in their preferred language using text or voice.
Faster access to manuals and technical knowledge: SmartSeas.ai emphasizes helping users quickly find relevant equipment guidance, reducing the effort of searching across multiple references.
Support for scanned documents: SmartSeas.ai’s support information references OCR capabilities to read text from scanned manuals and images.
Earlier identification of potential equipment issues: SmartSeas.ai describes “incident prediction” using analytics and machine learning to help identify potential equipment failures before they occur.
Why this matters as a “live” GenAI example
SmartSeas.ai reflects a practical adoption pattern: connect GenAI to trusted operational knowledge (manuals + historical records) so maritime teams can resolve technical questions faster, standardize troubleshooting outputs, and improve consistency across ship–shore workflows.
B) CMA CGM using AI at scale for operations and customer response
Reuters reported CMA CGM’s partnership with Google (July 2024) to accelerate AI solutions across operations, including route optimization and logistics efficiency. Reuters also reported CMA CGM’s €100m partnership with Mistral AI (April 2025) aimed at improving customer service—specifically handling over a million weekly customer emails and reducing response times.
What this proves: GenAI value is immediate in communication-heavy maritime workflows, even before touching onboard systems.
C) World Shipping Council launching AI cargo screening to prevent ship fires
In September 2025, the World Shipping Council (WSC) announced an industry program combining AI-powered cargo screening and inspection standards to identify misdeclared/undeclared high-risk shipments before loading. The Financial Times reported the tool scans millions of bookings in real time, and carriers representing about 70% of global container capacity opted to join. WSC also notes the platform builds on a tool processing 10+ million bookings per month.
What this proves: The industry is applying AI to reduce major operational risk, not only to “improve productivity.”
D) Port of Rotterdam using AI to detect disruptions from messages
Port of Rotterdam’s digital report describes how, when a terminal or depot sends a message about a disruption, AI picks it up and translates it into a message in Port Alert.
What this proves: Ports are increasingly using AI to convert unstructured messages into actionable operational updates—an ideal space for GenAI-style summarization and translation.
E) Lloyd’s Register using Generative AI for regulatory/permitting workflows
Lloyd’s Register announced it would use generative AI (built upon Microsoft Azure OpenAI Service) to enhance permitting capabilities for nuclear technology applications in maritime.
What this proves: GenAI also supports high-stakes, regulated documentation workflows, where traceability and process quality matter.
F) DNV exploring AI search/analysis to reduce offshore risk
DNV published a report exploring how AI search and analysis could improve safety in offshore operations, initially focusing on dropped object incidents.
What this proves: “Search + analysis + structured learning” is a credible path for safety improvement—not only automation.
G) Research case study: LLMs drafting replies in maritime industry (with human oversight)
An arXiv case study examined using LLM-generated draft replies to support human experts responding to stakeholder inquiries in maritime—finding workflow benefits but emphasizing the need for human oversight for precision and safety-critical contexts.
What this proves: A beginner-safe pattern is: AI drafts, humans decide.
5) Core GenAI concepts beginners must understand (without the jargon)
LLM (Large Language Model)
A model trained to predict and generate text. It’s great at:
summarizing,
drafting,
reformatting,
translating,
explaining.
Hallucinations (confident mistakes)
LLMs can produce plausible-sounding wrong content. That’s why maritime GenAI must be grounded.
RAG (Retrieval-Augmented Generation)
RAG reduces hallucinations by forcing the system to reference your approved documents.
Prompts (how you ask)
Prompting is simply giving the model clear instructions:
role (“act as an incident investigator”),
context (equipment, symptom, constraints),
output format (checklist, email draft, timeline),
and safety rules (“cite sources, do not guess”).
6) High-value GenAI use cases across the maritime ecosystem
Below are beginner-friendly use cases that apply broadly across operators, managers, technical teams, ports, logistics stakeholders, and maritime service providers.
Use Case 1: “Ask-the-Manual” knowledge assistant (with citations)
Problem: People waste time searching PDFs and sometimes follow outdated steps. GenAI approach: Ask natural-language questions and receive:
the best relevant excerpt(s),
a summarized answer,
plus citations (manual page, bulletin reference).
Typical outputs:
troubleshooting sequence,
checklists,
alarms → likely causes mapping,
parts/tools list.
Beginner rule: No citations = no operational answer.
Use Case 2: Defect report drafting and standardization
Problem: Defects are written inconsistently, which makes analysis harder and slows decisions. GenAI approach: Convert free-text notes into a standard defect structure:
symptoms,
conditions,
actions taken,
immediate risk,
recommended next steps,
missing details to request.
Why leaders like it: Better structured defect data improves recurring failure learning, reporting quality, and decision speed.
Use Case 3: Incident & near-miss synthesis (faster learning loops)
Problem: Incident learnings often live in long PDFs that don’t get reused. GenAI approach: Turn incident documentation into:
executive summary (one-page),
timeline of events,
causal factor hypotheses,
corrective/preventive action draft,
training bulletins.
Safety note: GenAI can help organize and draft—final causation is still human-led.
Use Case 4: Cargo risk screening and dangerous goods detection (industry scale example)
The WSC Cargo Safety Program shows how AI screening can flag suspicious bookings and reduce risk of misdeclared hazardous cargo.
Where GenAI fits: Beyond pattern detection, GenAI can:
draft inspection instructions,
generate ship/terminal briefing notes,
summarize “why flagged,”
standardize reporting feedback loops.
Use Case 5: Email + communication copilots (ship/shore/ports/class)
This is often the fastest “first win” because it’s low disruption and high volume.
GenAI can:
summarize long threads,
extract key facts (dates, serial numbers, ports, quantities),
draft a reply in your preferred tone,
create action lists with owners.
CMA CGM’s investments show how significant this category is at scale.
Use Case 6: Port disruption intelligence (message → action)
Port of Rotterdam describes AI detecting disruptions as soon as they’re messaged and translating them into Port Alert updates.
GenAI can help maritime teams by:
summarizing port/terminal messages,
translating into operational instructions,
creating checklists for response (documentation, crew planning, berth updates).
Use Case 7: Compliance evidence pack drafting
Problem: Inspections and audits consume time because evidence is scattered. GenAI approach: Build a “compliance pack generator” that:
10) Safety, cyber risk, and governance (non-negotiable)
Maritime leaders should treat GenAI as a safety-adjacent digital capability, not a casual tool.
IMO cyber risk management guidance
IMO’s updated guidelines on maritime cyber risk management (MSC-FAL.1/Circ.3/Rev.3, April 2025) provide high-level recommendations and functional elements for cyber risk management.
Industry cyber guidelines (Version 5)
“The Guidelines on Cybersecurity onboard Ships” (Version 5, published 14 Nov 2024) emphasize work processes, equipment, training, incident response, and recovery management.
Practical GenAI governance rules to implement from day one
Use approved tools only (no sensitive data in public chatbots)
Role-based access control (ship/shore/vendor segmentation)
Citations required for technical answers
Human approval for any safety-critical recommendation
Audit logging for prompts, outputs, and sources used
Document version control (avoid outdated procedures)
Redaction for sensitive identifiers (crew PII, security details)
Below are practical prompt templates maritime professionals can use in an approved GenAI environment.
Template A: Summarize an email thread into decisions + actions
“Summarize the following email thread into: (1) key facts, (2) decisions made, (3) open questions, (4) action items with suggested owners. Keep to 10 bullets max. If any technical claims appear, list them as ‘needs verification’.”
Template B: Convert notes into a structured defect report
“Turn these notes into a structured defect report with headings: Symptoms, Conditions, Actions Taken, Suspected Causes (with confidence), Immediate Risk, Recommended Next Checks, Spares/Tools Required, Missing Information Questions.”
Template C: Ask-the-manual with strict grounding
“Answer using only the provided excerpts. If the excerpts do not support an answer, say ‘Not enough information’ and list what document sections are needed. Always include citations.”
Template D: Create an incident timeline
“Create a timeline from the following incident notes. Include timestamps where available. Identify gaps and list 5 clarifying questions.”
12) Common mistakes beginners make (and how to avoid them)
Mistake 1: Starting with “AI everywhere”
Fix: Choose one workflow with measurable outcomes.
Mistake 2: Feeding messy documents without metadata
Fix: Tag documents by vessel/equipment/version/date.
Mistake 3: Allowing non-cited technical advice
Fix: Enforce “no citations → no operational answer.”
Mistake 4: No governance → shadow AI usage
Fix: Publish a clear policy + approved toolset.
Mistake 5: Treating GenAI as the decision-maker
Fix: GenAI drafts and assists; humans decide.
Research on LLM draft replies in maritime contexts reinforces that LLMs can streamline workflows but often need significant modifications and must remain under human oversight for safety-critical quality.
Conclusion: the simplest way to win with GenAI in maritime
Generative AI is not a futuristic “autonomy switch.” It’s a practical capability that helps maritime organizations move faster through the work that consumes time every day: searching, summarizing, drafting, standardizing, translating, and preparing evidence.
The strongest beginner strategy is:
start with one high-frequency workflow,
ground answers in trusted documents (RAG + citations),
implement cyber and governance rules aligned with maritime guidance,
measure operational KPIs,
scale only after reliability is proven.
The industry examples are already clear: from large-scale customer communication initiatives (CMA CGM) to safety-focused cargo screening at industry scale (World Shipping Council), maritime is moving from experimentation into structured adoption.
FAQs
1) Can GenAI be used safely in maritime operations?
Yes—when it’s deployed with cyber controls, access control, audit logging, and “human approval” safeguards aligned with IMO and industry cyber guidance.
2) What’s the best first GenAI use case?
Common first wins include: email/thread summarization, defect drafting standardization, and “ask-the-manual” Q&A with citations.
3) How do we prevent hallucinations?
Use RAG grounded in approved documents, require citations, and force the system to say “Not enough information” instead of guessing.
4) Do we need system integrations (PMS/ERP) to start?
No. Many successful pilots start with documents + communications, then integrate later once value is proven.
5) How do we measure ROI?
Track time-to-find information, time-to-draft responses, audit pack preparation time, repeat issue rates, and quality scores of reports.
6) Is GenAI only for shipping companies?
No. Ports (e.g., disruption message processing), classification/regulatory workflows, logistics providers, and maritime service companies can all benefit.
7) Can GenAI help with safety and risk reduction?
Yes—when used to improve early detection, standardize learning, reduce miscommunication, and strengthen pre-loading risk screening (as shown by WSC’s AI cargo screening initiative).
8) What should never be automated?
Any safety-critical decision or action without human oversight. GenAI can assist, not replace accountability.
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