Captains Remain, but AI Sets the Course: The Shift Toward Selling 'Navigation Software' in Shipbuilding

[Fact Check]

In January 2026, Avikus, an HD Hyundai subsidiary, secured a contract to install its AI-based autonomous navigation solution, HiNAS Control, on 40 HMM vessels. The system assists in perception, decision-making, and control, demonstrating an average fuel saving of 4.2%. Under the IMO's MASS Code, human supervision remains mandatory, ensuring that the captain retains ultimate responsibility for the vessel's safety and operations.

[AIxLogis Insight]

As someone who has been in this industry for a while, I see this HMM-Avikus deal as a clear signal that the business model of shipping is shifting from pure manufacturing to data-driven services. It is not just about building ships anymore; it is about selling 'navigation efficiency.' That 4.2% fuel saving might seem modest at first glance, but when you scale that across an entire fleet, it translates into massive cost reductions and a significant edge in carbon tax compliance, especially with the EU's strict emission regulations.

The real game-changer here is who owns the data. The operational data collected by AI will become the most valuable asset for shipping lines. It will dictate how efficiently you can navigate, how much you save on carbon credits, and ultimately, how competitive your freight rates can be. We are moving into an era where 'green efficiency' is the primary currency of logistics.

However, a word of caution for those of us on the front lines: do not let the AI do all the thinking. AI is a fantastic tool for optimizing fuel consumption and routine navigation, but it lacks the human intuition needed for unpredictable scenarios like sudden storms or congested fishing grounds. Always treat the AI as your co-pilot. Your expertise as a forwarder or operator in verifying these AI-driven decisions is what will keep the cargo safe and the schedule on track.

[Action Plan]

1. Track Fuel Efficiency Metrics: Regularly compare the fuel savings reported by your AI system against actual bunker consumption. Identifying discrepancies is key to fine-tuning your operational efficiency.
2. Calculate Carbon Cost Impact: Analyze how these efficiency gains translate into actual savings on carbon credit purchases. This will be critical for managing bottom-line costs on routes subject to environmental regulations.
3. Review Manual Override Protocols: Ensure your team is fully trained on when and how to override AI navigation during complex port entries or extreme weather. Technology should empower your decisions, not replace your judgment.

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Original source: 네이버뉴스