WHEN SMART SYSTEMS MAKE THE WHEELHOUSE LOUDER, NOT SAFER

When smart systems make the wheelhouse louder, not safer. By Ollie Thompson, Director of Engineering, MarineAI.

Artificial intelligence is not a distraction from the safety challenges facing commercial fishing, it is one of the few tools capable of addressing them at scale. As crews get smaller, traffic gets denser and demands on navigators continue to rise, so, the idea that we can rely on ever greater human vigilance alone is unrealistic.

Used well, AI and machine learning should be the next cognitive wave in maritime operations. They are ideally suited to high frequency monitoring, pattern recognition and background vigilance. Done properly, that frees up the navigator to focus on judgement, coordination and seamanship, the things humans are best at.

The problem is not the technology. It is how much of it is currently being deployed.

From the perspective of those who work at sea, particularly in fishing vessels operating close to shore and in mixed traffic, many AI systems are making the wheelhouse harder to manage, not easier. Instead of compressing complexity, they often layer more information on top of an already crowded bridge routine.

Radar, AIS, ECDIS, sonar, radios, deck operations and weather already compete for attention. Too often AI arrives as another screen, another alert stream, another set of probabilities that must be interpreted in real time. When a system presents multiple warnings with no clear priority or recommendation, the navigator has gained data but lost clarity.

In safety critical environments, that trade-off matters. Fishing vessels frequently operate in poor visibility, heavy traffic and confined waters, often while managing deck activity at the same time. In those conditions, a system that demands attention rather than directing it becomes a liability.

This is where the industry needs to be more honest about what good looks like. AI should not be judged on how much information it can surface, but on whether it reduces cognitive load at the moment of decision.

That means distilling complexity into clear priorities and legible guidance. It means quietly handling the constant background monitoring that humans find exhausting and only surfacing when there is something genuinely actionable to say. Raw probabilities, confidence intervals and system caveats may satisfy engineers, but they are rarely helpful on a working bridge.

Trust is central to this. An AI system that becomes noisy or overly cautious when sensors degrade, or when conditions become complex, will quickly be ignored. Once crews start muting alerts or disregarding recommendations, even well-founded warnings lose their value. In fishing operations, where attention is often split between navigation and the deck, that loss of trust can actively reduce safety.

None of this is an argument against maritime AI. On the contrary, the sector needs it. But deployment needs to be driven by operational reality rather than feature lists or demonstration metrics. Systems should be evaluated on whether they make the working day calmer, not busier.

If AI is to become a true lifeline at sea, it must earn its place by simplifying decisions in the conditions that actually drive risk. Heavy traffic, poor visibility and imperfect data are the norm, not the exception. That is the standard fishers, regulators and buyers should demand.

Image: MarineAI