Why maritime AI projects fail - and how to fix it
Maritime AI adoption is accelerating, but most projects underperform or fail entirely. The root cause is rarely the AI model itself — it is the quality, consistency, and completeness of the vessel data feeding it. This article explains what "AI-ready" vessel data actually means, identifies the most common data quality failures in maritime operations, and provides a practical framework for building a data foundation before investing in AI.
The gap between AI ambitions and data readiness in maritime
The maritime industry is investing heavily in AI for voyage optimization, predictive maintenance, and emissions reduction. However, a recurring pattern has emerged across the industry: organizations invest in sophisticated AI tools only to discover that their underlying data cannot support them.
This is not a theoretical risk. According to Gartner's hype cycle framework, industries that adopt transformative technology without adequate infrastructure typically experience a "trough of disillusionment" — a period where failed implementations erode confidence in the technology itself. In maritime, that infrastructure gap is almost always about data.
The core problem is straightforward: AI is applied mathematics. Machine learning models identify patterns in data and use those patterns to make predictions. If the input data contains errors, gaps, or inconsistencies, the model's outputs will reflect those problems — often in ways that are difficult to detect until they cause real operational harm.
What "AI-ready" vessel data means in practice
For vessel data to support AI applications reliably, it must meet four criteria. Each has specific implications for how data is collected, processed, and stored onboard and onshore.
1.Accuracy: sensor error compounds in AI models
Vessel sensors routinely operate with error margins of 3–5% for measurements like fuel flow, shaft power, and exhaust gas temperature. For manual reporting or human decision-making, this level of accuracy is often workable. For AI models, it is not.
The reason is error compounding. When a voyage optimization model combines fuel flow data, speed through water, draft readings, wind speed, and current data — each with its own error margin — the cumulative uncertainty can make the model's recommendations unreliable or even counterproductive. A fuel flow sensor with a 5% error margin, combined with a 3% error in speed measurement, can produce voyage optimization recommendations that increase fuel consumption rather than reducing it.
Practical implication: sensor calibration programs need to be systematic and docmented, not ad hoc. Every data source feeding an AI model should have a known and accepted error margin.
2.Consistency: data standardization across the fleet
One of the most common and least visible data problems in maritime is inconsistency between vessels. Different ships in the same fleet may report the same measurement in different units, at different intervals, using different naming conventions, or from different sensor types.
For example, fuel consumption may be reported in metric tons per hour on one vessel, liters per nautical mile on another, and as daily aggregates via noon reports on a third. Engine load may come from the automation system on one vessel and from a separate shaft power meter on another. Without standardization, combining this data for fleet-wide AI analysis produces misleading results.
The maritime industry has begun addressing this through standards like ISO 19847 (shipboard data server) and ISO 19848 (standard data for shipboard machinery and equipment), but adoption remains uneven. A practical data strategy must include a data harmonization layer that normalizes naming conventions, units, timestamps, and sampling rates before data reaches any analytics or AI system.
3.Completeness: missing data creates blind spots
AI models depend on continuous, complete datasets to identify meaningful patterns. In maritime operations, data gaps are common due to connectivity limitations at sea, sensor malfunctions, system restarts, or simply because certain measurements were never configured for collection.
A predictive maintenance model trained on engine performance data that has frequent gaps will either fail to detect degradation patterns or generate excessive false alerts — both of which erode crew trust and operational value. Similarly, a voyage optimization model that loses weather data or AIS position data during critical segments of a voyage cannot produce reliable recommendations.
Data completeness in maritime should be measured as a percentage and tracked as a KPI. Industry benchmarks suggest that AI applications in maritime typically require data completeness above 95% to produce reliable outputs, though the exact threshold depends on the specific use case and model architecture.
4.Timeliness: high-frequency data vs. noon reports
Traditionally, vessel performance data was reported once per day through noon reports — manual summaries compiled by the crew. While noon reports serve important contractual and operational purposes, they are insufficient for AI applications.
High-frequency data collection (at intervals of 1–15 seconds for most operational parameters) captures the dynamic reality of vessel operations: speed changes, weather encounters, maneuvering events, load variations, and equipment transients. This granularity is what enables AI models to distinguish between, for example, increased fuel consumption caused by hull fouling versus consumption caused by adverse weather — a distinction that noon report averages cannot make.
The shift from daily reporting to high-frequency data collection requires edge computing infrastructure onboard the vessel, capable of collecting, processing, and transmitting data in near real-time even in bandwidth-constrained environments.
Common failure patterns in maritime AI projects
Based on industry experience, the most frequent data-related failure patterns in maritime AI initiatives fall into three categories:
Voyage optimization producing unreliable recommendations
When weather data is incomplete or fuel consumption sensors are poorly calibrated, optimization algorithms may recommend speed or routing adjustments that are suboptimal or counterproductive. The Wallenius Wilhelmsen fleet achieved a verified 7% fuel saving through AI-driven voyage optimization — but this result was only possible because of a multi-year investment in high-frequency, high-quality data collection across the fleet using an onboard IoT platform.
Predictive maintenance generating excessive false alerts
When historical maintenance records are inconsistent in format or completeness, machine learning models cannot reliably distinguish between normal operational variation and genuine equipment degradation. The result is alert fatigue — crews learn to ignore the system, defeating its purpose.
Performance benchmarking producing misleading fleet comparisons
When different vessels report data in different formats, at different frequencies, or from different sensor configurations, fleet-wide comparisons become unreliable. Vessels may appear to be underperforming or outperforming due to data artifacts rather than actual operational differences.
A practical framework for building your vessel data foundation
Before committing to AI initiatives, maritime organizations should invest in their data foundation using the following sequence.
Systematically evaluate every data source that will feed your intended AI applications. For each source, document the sensor type, calibration status, known error margin, data format, sampling rate, and completeness over the past 12 months. Identify the largest gaps and the most unreliable sources.
Step 1: Audit your data sources and quality
Define a single data model for your fleet: naming conventions, units of measurement, timestamp formats, and minimum sampling rates for each data category. Align with ISO 19847 and ISO 19848 where possible. Implement a data harmonization layer - either onboard or in the cloud - that enforces these standards automatically.
Step 2: Establish fleet-wide data standards
Install onboard IoT infrastructure capable of collecting data from all relevant systems (automation systems, flow meters, shaft power meters, navigation systems, weather instruments, and any additional wireless sensors). Edge computing capability is essential for processing data onboard, managing bandwidth constraints, and ensuring data quality before transmission to shore.
Step 3: Deploy edge infrastructure for high-frequency collection
Treat data quality as an ongoing operational metric, not a one-time project. Implement automated monitoring that tracks completeness, consistency, and anomaly rates across the fleet. Set thresholds and alerts so that data quality issues are detected and resolved quickly — before they contaminate AI model outputs.
Step 4: Implement continuous data quality monitoring
Choose a single, well-defined AI use case where you have confidence in the quality of the underlying data. Voyage optimization, energy efficiency monitoring, or a specific predictive maintenance application for critical equipment are common starting points. Demonstrate value in one area before expanding.
Step 5: Start with a focused AI use case
Key takeaways
Maritime AI is not a plug-and-play technology. Its effectiveness depends entirely on the quality of the data it receives. The most common reason maritime AI projects fail is not inadequate algorithms — it is inadequate data.
Organizations that invest in a systematic vessel data foundation — accurate sensors, standardized formats, high-frequency collection, and continuous quality monitoring — position themselves to extract real value from AI. Those that skip this step will find themselves in the trough of disillusionment, having spent significant resources on AI tools that cannot deliver on their promise.
The sequence matters: data foundation first, AI applications second.
Raa Labs provides RaaEDGE, a vessel IoT platform that collects, harmonizes, and validates high-frequency vessel data to create an AI-ready data foundation. To learn more, visit raalabs.com or contact the team.
Ari Marjamaa is the CEO of Raa Labs. His experience ranges from senior management, strategic and academic advisory roles as well as technology driven business development. Prior to his current role, Ari was Chief Transformation Officer in Wallenius Wilhelmsen. Ari holds a MSc in Economics from the Norwegian School of Economics.
