Artificial Intelligence and Shipping: Navigating the Next Big Wave

Introduction: A Turning Point for the Maritime World

Shipping is the invisible engine of global commerce — quietly carrying over 80% of the world’s goods across oceans. For centuries, the industry has embraced technological milestones: the transition from sail to steam, the adoption of radar and GPS, and most recently, the digitization of fleet operations. Today, we are entering an era that could rival all previous transformations combined — the era of Artificial Intelligence (AI).

AI is no longer a buzzword confined to Silicon Valley; it is already making its presence felt on the bridge, in the engine room, and across shipping company boardrooms. It promises a future where ships sail smarter, operations become leaner, and safety reaches new heights. But this transformation is not plug-and-play. It comes with complex ethical, technical, and regulatory considerations that the industry must confront.

What Exactly Is Artificial Intelligence?

AI is not just advanced software — it is a machine’s ability to mimic human cognitive functions such as perceiving, reasoning, learning, problem-solving, and even creative thinking.

Unlike traditional systems that simply follow pre-set rules, AI systems learn from data and get better over time. This “self-improving” nature makes AI uniquely powerful for shipping, where conditions are dynamic and no two voyages are identical.

McKinsey & Company describes AI as the ability to improve its own outputs the more familiar it becomes with tasks — an adaptive, iterative learning loop.

Example Outside Shipping: Amazon’s recommendation engine uses AI to suggest products you’re likely to buy next. It learns from billions of transactions, constantly refining its accuracy.Example Inside Shipping:AI can analyze years of weather, current, and fuel-consumption data for a single vessel class and propose optimal speed profiles — saving fuel and reducing emissions with each voyage.

AI’s Role in the Maritime Value Chain

When you ask a freely available AI chatbot where AI fits into shipping, you’ll get a dozen possible applications. And indeed, AI touches nearly every corner of maritime operations:

Area	AI Application	Example Use Case
Navigation	Route optimisation	Dynamic weather routing, just-in-time arrivals
Safety	Behaviour-based safety, collision avoidance	CCTV analytics to ensure bridge manning, automated target prioritisation
Engineering	Predictive maintenance	Early detection of bearing wear in main engine turbochargers
Cargo Operations	Misdeclaration detection	Cross-referencing booking data to catch undeclared DG cargo
Environmental Compliance	Emissions monitoring	AI-driven trim optimisation for CO₂ reduction
Commercial	Market intelligence	Predictive freight rate modelling
Port Operations	Berth scheduling	AI-enabled port call optimisation
Crew Development	Adaptive training	VR + AI simulations for emergency drills

Each of these use cases represents a shift from reactive to proactive operations — from waiting for problems to occur to predicting and preventing them.

AI Onboard: Real-World Ship Applications

The industry is not just theorizing about AI — it is deploying it. Some of the most impactful areas include:

1. Behaviour-Based Safety (Vision Platforms)

AI-powered vision platforms review thousands of hours of CCTV footage and flag safety breaches in real time.

• Example: ORCA AI’s platform is deployed on tankers and bulkers to monitor bridge occupancy, PPE usage, and unsafe behaviour during cargo ops.

• Benefit: Enables fleet managers to measure safety culture quantitatively and share best practices with crews.

2. Collision Avoidance and Watchkeeping Support

A combination of radar, AIS, thermal cameras, and optical sensors feeds into AI algorithms that create a 360° situational picture.

• Case Study: NYK Line trialed an autonomous navigation system in 2022 that successfully completed a 790 km voyage without human intervention.

• Benefit: AI prioritises collision threats, reducing cognitive overload for officers of the watch and minimising near-misses.

3. Fire Detection and Prevention

Traditional systems trigger alarms after smoke/heat thresholds are reached. AI systems detect anomalies far earlier.

• Example: Wärtsilä’s AI-based video analytics have been used to spot overheating in electrical cabinets before visible smoke occurred.

• Benefit: Early intervention prevents catastrophic engine-room fires.

4. Route Optimisation and Fuel Efficiency

AI goes beyond basic weather routing by considering fuel curves, hull fouling data, and emission control area (ECA) compliance.

• Case Study: Maersk’s AI-enabled voyage optimisation has reported up to 5-7% bunker savings, translating to millions in annual OPEX reductions.

5. Dangerous Goods Misdeclaration Detection

AI analyses booking data, weight anomalies, and historical cargo patterns to detect misdeclared goods — a major cause of container fires.

• Example: Hapag-Lloyd uses its "Cargo Patrol" software to scan more than 6 million bookings annually, catching thousands of DG violations.

Key Challenges and Risks

Adoption of AI is not without friction. Shipowners must navigate several critical challenges:

🔐 Cybersecurity

AI creates additional digital endpoints. These must be hardened against intrusion. A cyber-compromised AI collision-avoidance system could create risk rather than prevent it.

📊 Data Quality and Bias

Poor data leads to unreliable recommendations (“garbage in, garbage out”). Owners must ensure that historical data is accurate and representative.

⚠️ Overreliance and Deskilling

AI should be treated as an aid, not a substitute. Officers must retain manual navigation and RADAR plotting skills to prevent overreliance.

🎓 Training & Change Management

Crew must be trained in simulator environments before shipboard deployment. Training should cover normal use, failure modes, and manual overrides.

🕵️‍♂️ Ethics & Privacy

AI vision systems raise legitimate privacy concerns. Owners must communicate clearly about how data is stored, who has access, and how long it is retained.

A Strategic Roadmap for Shipowners

To successfully integrate AI, shipowners should adopt a structured approach:

1. Pilot Projects: Begin with a small fleet segment and measure impact.

2. Cyber Risk Assessment: Update cyber policies to include AI endpoints.

3. Crew Involvement: Involve ship staff early to build trust and reduce resistance.

4. Data Governance: Establish protocols for data quality, storage, and compliance (GDPR, IMO guidelines).

5. Human Oversight: Maintain a “human-in-the-loop” model for critical decision-making.

Conclusion: AI as the Co-Navigator of the Future

AI is not here to replace the mariner but to empower them. It acts as a digital co-navigator, providing data-driven insights, improving safety, and supporting sustainability goals.

The maritime industry has always been resilient and adaptive. Those who embrace AI thoughtfully — combining its power with human expertise, strong governance, and ethical considerations — will set the course for safer, greener, and more profitable shipping in the decades to come.

Sponsored Advert