Maritime operators grapple with AI deployment across aging systems
Technical executives from major shipping companies gathered at the Maritime IT Networking Summit in Greece to discuss how to implement artificial intelligence without overhauling the legacy infrastructure that keeps vessels and ports running.
The conversation revealed a clear gap: while AI has moved beyond pilot projects into active use, most maritime organizations are still figuring out how to deploy it safely across systems built decades ago.
Where AI is already delivering results
Three concrete applications are generating measurable gains:
- Route Optimization: Systems combine sea currents, weather data, historical vessel speeds, and fuel pricing to generate real-time voyage recommendations.
- Predictive Maintenance: Algorithms monitor engine sensors continuously to flag performance issues before they cause equipment failures.
- Crew Assistance: Conversational AI assistants handle onboard reporting, reducing administrative work for crews at sea.
Dr. Stavros Daniel, Group CTO of Capital Maritime Group, said corporate reporting requirements and C-level expectations are driving the shift from experimentation to deployment. AI is no longer optional.
Adoption remains uneven across the industry
About half of maritime organizations are running AI pilot programs, but full-scale implementation concentrates in larger, highly structured fleets. The barrier isn't the technology-it's user behavior.
Yiannis Sofianidis, IT Director at A.M. Nomikos, said static PDF policies fail to change how people handle data. He advocates for hands-on training with real-world examples to help teams distinguish between safe uses (drafting correspondence) and dangerous ones (uploading sensitive documents to public chatbots).
The panel agreed unanimously: humans must retain final control over AI outputs. The technology isn't mature enough to operate without verification.
Ports face their own integration challenges
AI deployment extends beyond vessels into port operations, where customs, coast guards, agents, and terminal operators must coordinate.
Vasilis Parthenis, Deputy Manager of IT at the Piraeus Port Authority, said AI will not replace the established rules governing these networks. Instead, intelligent applications layer on top to accelerate communication and cargo processing.
The Piraeus Port Authority is evaluating software for yard optimization and building Digital Twin platforms to simulate logistical decisions. A five-month vendor assessment found that supplier capabilities have matured significantly over a short period.
Shadow AI creates new security problems
Unmanaged use of public AI tools by shore staff and crews has created a governance crisis. Employees bypass corporate controls to use free chatbots, risking data exposure.
Kostas Grivas, Information Security Officer at the Angelicoussis Group, said his company established a dedicated AI committee to oversee strategy and maintain data integrity. Corporate data is a shipping company's core asset.
Some maritime enterprises are rolling out controlled solutions like Microsoft 365 Copilot for email summarization instead of allowing open access to public tools. IT teams are also launching Data Loss Prevention projects using automated data classification to map company data flows.
Grivas flagged backend APIs as critical blind spots. Security vulnerabilities typically emerge at the processing layer, not the user interface, requiring thorough third-party risk assessments.
Measuring financial return remains difficult
ROI tracking works best for specialized, vertical use cases-such as measuring man-hours saved through IT helpdesk chatbots. Calculating exact metrics in early-stage deployments remains challenging.
The real value of scaling AI in maritime operations extends beyond cost reduction or labor replacement. The core benefit is the ability to make faster, consistent, data-backed decisions that reduce operational risk across the global supply chain.
Operations professionals implementing AI should focus on AI for Operations fundamentals and consider the AI Learning Path for Operations Managers to understand governance, deployment strategy, and performance measurement.
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