AI is shaping offshore performance: From fragmented data to trusted decisions

Opsealog’s latest technical paper – Building Trust in Offshore Performance through Data Science & AI – explores how applied data science and artificial intelligence (AI) can address this challenge by building operational trust. Drawing on insights from more than 1,000 OSVs worldwide, the company outlines proven methodologies and real-world use cases for embedding AI into offshore performance management, without turning vessels into testbeds for abstract algorithms.

Data isn’t insight – yet

Many offshore operators already collect large volumes of data from onboard systems, manual reporting, and third-party platforms. But quantity does not equal clarity. Inconsistencies between reported activity and sensor data remain common, especially in operations that rely heavily on manual spreadsheet reporting or outdated vessel management systems.

This disconnect leads to a familiar dilemma: teams tasked with improving fuel efficiency, reducing idle time, or reporting emissions often don’t know which data points to trust, or, crucially, how to reconcile conflicting inputs. In practice, this can result in poor decision-making, missed efficiency opportunities, or misaligned commercial outcomes between vessel owners and charterers.

Without a method to filter, compare, and validate data streams, analytics becomes noise. The industry needs tools that don’t just visualize data, but actively assess its reliability, while providing structured insight that can be traced back to operational behavior.

A model for trust: scoring, validation, and explainability

One approach to solving this problem is the use of activity scoring models, a technique explored in detail in the paper. These models evaluate reported vessel states (such as transit, standby, or operations) against high-frequency sensor data. Rather than treating either source as infallible, the model assigns a confidence score to each reported activity based on the alignment of underlying parameters like speed, position, and engine load.

Where discrepancies occur, they are not simply flagged as errors. Instead, they form part of a broader narrative that helps users identify reporting gaps, detect patterns, and develop a more coherent operational picture. In this way, AI acts not as a black box, but as a diagnostic tool – auditable, explainable, and useful for both technical and commercial teams.

This approach enables operators to move beyond reactive reporting and into continuous performance monitoring, where validated data feeds into decision-making at every level, from the bridge to the boardroom.

Real-world use cases

The value of this methodology becomes clear when applied across fleet-scale datasets. One use case featured in the paper involved the common industry practice of slow steaming (reducing vessel speed in the belief that it conserves fuel). While often effective, analysis showed that on certain vessels, slow steaming led to inefficient generator loading, negating expected savings and increasing maintenance risks.

In another case, time-series segmentation of engine data revealed excessive idle time that had not been captured in daily logs. By validating and scoring vessel behavior at a granular level, the operator was able to flag avoidable emissions and identify opportunities to improve fuel allocation during planning.

Comparative analysis between sister vessels also revealed significant variation in energy use under similar conditions – insights that were then used to benchmark crews, optimize voyage planning, and drive operational best practices. These findings were presented using Sankey diagrams and efficiency heatmaps, making them accessible to stakeholders beyond the technical department.

Contract performance and generative AI

Beyond vessel behavior, performance data is increasingly being used to track charter party agreement compliance, a trend that’s gaining momentum as ESG accountability and emissions transparency rise up the agenda. Time charter terms such as fuel quotas, on/off-hire conditions, and service availability can now be monitored in near real time, reducing the risk of disputes and fostering a more collaborative dynamic between owners and charterers.

Looking further ahead, the paper touches on how generative AI models – such as large language models (LLMs) – may be used to deliver operational guidance at scale. These tools could turn structured performance data into tailored recommendations for different users onboard or ashore, helping close the loop between insight and action. Early results suggest this could significantly improve engagement with data-driven decision support systems, especially across large, diverse fleets.

The case for operational trust

As offshore operators navigate the transition to lower-carbon operations and tighter performance margins, trust in data has become a strategic asset. But trust doesn’t come from collecting more information. It comes from curating it, validating it, and applying it in a way that respects operational context and supports transparent collaboration.

AI is not a replacement for experience, but a tool that strengthens it. When paired with structured methodologies and a clear governance framework, data science can help the offshore sector move from fragmented reporting to confident decision-making. This would close the gap between what vessels are doing, and what stakeholders believe they’re doing. The result is not just improved efficiency, but a more accountable and resilient performance culture across the marine offshore ecosystem.