Artificial intelligence is moving rapidly from pilot projects to fleet level deployment across global shipping. From collision avoidance and route optimisation to machinery monitoring and compliance support, AI is increasingly positioned as a solution to rising operational and regulatory pressure. For owners and managers, the momentum behind adoption is clear.

Yet there is a risk that AI is judged primarily on technical capability rather than operational behaviour. What matters in shipping is not what a system can do in ideal conditions, but how it performs when placed into everyday operations, with imperfect data, human variability and commercial pressure. In that environment, trust matters as much as performance.

Capability alone does not deliver confidence

Shipping rarely operates in clean or predictable conditions. Sensors degrade, inputs conflict and human behaviour remains a decisive factor. AI is well suited to continuous monitoring and large-scale data processing, while humans remain better at contextual judgement, coordination and accountability. The promise of maritime AI lies in combining these strengths, not confusing them.

In practice, many deployments struggle to strike this balance. New systems are often introduced as an additional layer, adding alerts, dashboards and decision aids without reducing the burden of existing ones. Operators are presented with probabilities, confidence scores and competing recommendations that require interpretation at exactly the moment when clarity is most valuable.

From a fleet perspective, this creates inconsistency. Some crews engage fully with the system, others disengage, and informal workarounds emerge. Over time, this undermines standardisation, training effectiveness and auditability. A system that is trusted on one vessel and ignored on another is not delivering fleet level benefit.

Trust is fragile and easily lost

Trust in AI systems is slow to build and quick to erode. Tools that generate excessive warnings, behave conservatively when data quality drops, or regularly contradict experienced judgement are rapidly sidelined. Alerts are muted, recommendations discounted and the technology fades into the background.

This is not a user failure. It is a design and governance issue. Once trust is lost, even accurate interventions may be ignored, creating new safety and compliance risks that are difficult to detect from shore.

There is a common assumption that greater transparency will resolve these issues. While explainability is important, exposing users to every intermediate calculation or uncertainty range does not necessarily improve confidence. In many cases, it increases hesitation. Operators need to understand what matters, how confident the system is, and when human intervention is required. They do not need to see every step in the reasoning.

Implications for owners and managers

For owners and technical managers, this has direct consequences for procurement and oversight. Systems are frequently evaluated on detection rates, algorithmic performance or alignment with regulatory frameworks. Far less attention is paid to how they integrate into real workflows, how they affect cognitive load, or how they behave in degraded conditions. These factors are harder to quantify, but they are critical to real world effectiveness.

There is also a commercial dimension. AI that increases workload, training demands or procedural complexity carries hidden costs. These may not appear in a business case, but they surface in crew resistance, inconsistent use and increased management overhead. Technology that appears efficient on paper can become expensive in practice.
As the industry moves towards greater autonomy and remote operations, these challenges will intensify. Shore based teams will be responsible for more vessels, more data and more decisions. Without careful design, AI risks shifting cognitive overload from ship to shore rather than reducing it.

Scaling what actually works

The path forward is not to slow adoption, but to be more disciplined about what success looks like. Maritime AI should be judged on whether it simplifies decisions, reduces variability and supports human judgement in the conditions that actually drive risk. Heavy traffic, imperfect data and operational pressure are the norm, not the exception.

AI has the potential to be a powerful force for safer and more efficient shipping. But scale without trust is fragile. If these systems are to deliver lasting value across fleets, they must earn confidence on the bridge and in the operations room before they are rolled out at scale.

MarineAI’s white paper, Cognitive Load: the navigator’s lifeline in the age of AI at sea, explores how maritime AI can reduce workload rather than add to it in confined waters.
Read more here.
Source: By Ollie Thompson, Director of Engineering, MarineAI