From Operational Disruption to Operational Intelligence
How AI can help shipping companies identify the risks that emerge while vessels are waiting
When disruption occurs in shipping, the visible consequences receive immediate attention.
Cargoes are delayed.
Schedules are revised.
Charter implications are assessed.
Assets remain idle.
Commercial teams calculate the cost of waiting.
But operational disruption does more than delay an asset.
It changes the asset’s risk profile.
While teams focus on the immediate commercial consequences, secondary risks may begin developing quietly across the vessel, its equipment and its operating environment.
Biofouling is one example.
When vessels remain inactive for extended periods, conditions can allow microorganisms to attach to submerged surfaces and begin forming a biofilm. If that development continues unchecked, more complex marine growth may affect the hull, propeller, sea chests, cooling systems, strainers and internal seawater systems.
These biological processes do not follow the commercial schedule.
They continue whether the disruption is caused by geopolitics, congestion, weather, maintenance delays or changing trading patterns.
This creates an important operational question:
Does the organisation merely know that a vessel is waiting, or does it understand what that waiting may be doing to the vessel?
The hidden cost of waiting
The impact of biofouling varies according to vessel type, operating profile, speed and severity of growth.
But the potential consequences are significant.
One research review found that, under severe fouling conditions, maintaining service speed at maximum continuous engine rating could increase fuel consumption by as much as 62.5%. This is an extreme scenario rather than a universal result, but it demonstrates how a condition developing gradually below the waterline can translate into substantial operational and financial cost.
Biofouling can increase hydrodynamic resistance, reduce propeller efficiency, restrict seawater flow, place additional pressure on cooling systems and contribute to higher fuel use and emissions.
The challenge is that these effects do not always appear immediately.
By the time changes become visible through performance loss, increased consumption, vibration, restricted flow or equipment stress, the most effective intervention window may already have narrowed.
Why disruption weakens reactive maintenance
Traditional maintenance models often assume that an emerging issue can be addressed during the next planned intervention.
That assumption becomes increasingly fragile when:
drydock availability is constrained,
underwater service providers are operating at capacity,
planned port calls change,
maintenance windows are missed,
or a vessel remains inactive longer than expected.
A reactive approach depends on having timely access to the right people, location and service capacity.
Operational disruption makes all three less predictable.
This is why prevention and early detection become more valuable precisely when operations are under pressure.
The IMO’s revised 2023 Biofouling Guidelines recommend a proactive approach based on risk profiles, inspection, monitoring, contingency planning and documented management. The guidelines explicitly note that digital tools may be used to monitor biofouling risk parameters.
The shift is important.
Biofouling should not be treated only as something to remove once it becomes visible. It should be treated as a changing operational risk that can be monitored and managed throughout the vessel’s operating life.
From fragmented data to operational intelligence
Most shipping organisations already hold much of the information required to identify changing exposure.
They may know:
how long a vessel has remained inactive,
where it is located,
the temperature and characteristics of the surrounding water,
previous fouling and maintenance history,
the condition of coatings and marine-growth prevention systems,
changes in fuel consumption or equipment performance,
upcoming port calls,
and the availability of inspection or cleaning services.
The difficulty is that these signals are often distributed across different systems, reports, emails and teams.
Individually, each data point may appear routine.
Combined, they can indicate that risk is increasing.
This is where AI-enabled operational intelligence can create value.
A properly designed system could combine operational, environmental and maintenance data to:
identify vessels entering a higher-risk inactivity period,
detect changes in performance that may be linked to fouling,
prioritise inspections,
highlight maintenance windows before they disappear,
support technical teams with vessel-specific risk assessments,
and document the information behind each operational decision.
The value is not another dashboard.
The value is knowing:
Which vessel requires attention?
Why now?
What action should follow?
And who is responsible for taking it?
AI is already turning vessel data into measurable decisions
The broader maritime sector already offers examples of AI converting vessel-specific data into operational improvements.
DeepSea Technologies’ Pythia platform builds AI models that incorporate factors including speed, draft, weather, fouling and vessel condition to optimise route and speed decisions. The company currently reports fuel reductions of approximately 6% compared with generic models, while earlier deployments reported savings in the range of 8–10%. These are provider-reported results rather than universal guarantees, but they demonstrate the value of replacing generic assumptions with vessel-specific intelligence.
Voyage optimisation does not solve biofouling directly.
But it demonstrates a critical principle:
When the actual condition and behaviour of the vessel are included in the model, better decisions become possible.
The same logic can be applied to operational disruption.
A vessel’s risk should not be assessed only from its original schedule. It should be reassessed as the operating conditions change.
Prediction, inspection and prevention
AI can support biofouling management in several ways.
Predictive models can combine environmental conditions, inactivity periods, historical maintenance and vessel performance to estimate changing risk.
Computer-vision systems can analyse underwater imagery to identify, map and classify marine growth.
Robotic systems can support inspection and targeted cleaning in conditions where manual intervention is difficult, costly or unsafe.
But prediction and detection are only part of the response.
Physical prevention technologies can also reduce the likelihood that biofilm develops into more complex marine growth.
HASYTEC, for example, uses ultrasound-based technology designed to prevent biofilm and marine growth on propellers and other liquid-contact surfaces. The company reports installations on more than 270 vessel propellers and states that some of its earliest projects have operated for more than five years without requiring subsequent propeller cleaning. These are company-reported outcomes, but they illustrate the operational value of continuous prevention when future maintenance access cannot be guaranteed.
The most effective operating model will not depend on a single technology.
It will combine:
risk intelligence, inspection, preventive systems, technical judgment and timely intervention.
Prevention requires accountability
A predictive warning has limited value if nobody owns the response.
Operational intelligence therefore requires more than an algorithm.
It requires:
defined risk thresholds,
clear escalation paths,
integration with maintenance planning,
accountable decision-makers,
access to supporting evidence,
and the authority to act before the problem becomes urgent.
AI can identify patterns, prioritise assets and recommend action.
Technical and operational teams must still validate the situation, account for commercial constraints and make the final decision.
The objective is not autonomous vessel maintenance.
The objective is earlier awareness and better-supported human judgment.
Biofouling is only one example
The broader lesson extends well beyond marine growth.
During disruption, secondary risks may emerge in:
equipment condition,
maintenance exposure,
crew workload,
safety procedures,
compliance documentation,
supplier availability,
inspection schedules,
energy efficiency,
and the transfer of operational knowledge between teams.
When attention is concentrated on the immediate event, these quieter changes may remain outside the operational picture.
Operational readiness should therefore not be measured only by how quickly an organisation reacts after a problem becomes visible.
It should also be measured by how effectively it identifies the conditions from which the next problem may emerge.
From waiting to readiness
The next disruption may occur in a different region and for an entirely different reason.
The cause may be geopolitical tension, congestion, weather, infrastructure failure or a commercial change.
The operational principle remains the same:
Waiting is not a neutral condition.
It changes exposure.
It changes maintenance assumptions.
It changes the availability of intervention.
And it can change the condition of the asset itself.
The companies best prepared for disruption will not simply monitor where their vessels are and how long they have been waiting.
They will understand how the risk profile is changing, what signals require attention and what action must follow.
That is the transition from operational disruption to operational intelligence.
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The starting point for this article was a series of recent reports and case studies highlighting how biofouling and extended waiting periods can create significant operational and financial consequences. These examples prompted a broader question: how can shipping companies identify such risks earlier and respond before they escalate?
Dimitris Avdelopoulos, “What the Strait of Hormuz Backlog Reveals About Operational Readiness” — Marine Log, July 2026.
International Maritime Organization, “Biofouling”.
Maritime Innovations, “AI Takes on Biofouling Prevention and Removal in 2025” — By Joachim Rosenoegger, 30 January 2025.
ERMA TECH GROUP, “ERMA TECH GROUP Acquires HASYTEC’s Ultrasonic Antifouling Technology”.
