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analyse. That is where AI enters the scene %u2014 not to replace digitalisation, but to enhance the processing of large datasets, the identification of patterns, and the support of data-driven corrective actions. In this way, operational control is further strengthened, thereby improving competitiveness.In a nutshell, digitalisation has already established a more efficient operational model, while AI is emerging as the next layer of enhancement, contributing to and supporting fleet competitiveness in terms of cost discipline, sustainability performance, and market positioning.To what extent do data analytics and predictive models contribute to the reduction of fuel consumption and CO2 emissions on a day-to-day basis?Data analytics and predictive models can contribute to reducing fuel consumption and CO2 on a day-to-day basis. Still, the extent of their impact depends on the quality, volume, and completeness of the underlying data.These tools transform collected operational data into structured insight. For instance, by enabling earlier detection of fuel consumption deviations and suboptimal operating patterns, they shorten response time and support corrective, data-driven actions, such as scheduling hull and propeller cleaning and fine-tuning of main engine operation. In this way, they strengthen monitoring benefits and contribute to more efficient daily operations.However, building on the same data foundation, it must be recognised that while the industry can now collect massive volumes of data, volume alone is only a necessary precondition for improvement. The validity of analytics depends not only on the availability and quality of large-scale data, but also on the capture of all critical process parameters. Large data volumes may still be insufficient if specific parameters of the under-examination process are not measured or are incomplete. In such cases, outputs remain indicative rather than fully reliable for decisive corrective action.Essentially, data analytics and predictive models hold significant potential to support overall vessel operational performance, including daily fuel efficiency and CO2 emissions reduction. Yet their full value depends on continuous improvement in data integrity and the availability of all critical parameters required for reliable analysis.How is the role of seafarers and shorebased teams evolving with the introduction of AI, remote operations, and smart vessels?AI tools, remote access systems, and smart vessel technologies currently serve as supporting tools rather than replacements for human experience and judgment. They are indeed reshaping the way seafarers and shore personnel work.AI and digitalisation are reducing firstlevel tasks such as routine data collection, cleaning, and visualisation, which allows seafarers and shore-based teams to devote more time to evaluating information and making sound decisions, rather than spending time extracting and organising data. We are witnessing a clear shift in their role, from manual processing toward data evaluation and a more decision-making approach based on readily available, large-scale information.Remote access systems add another important dimension that gradually reshapes crew and ashore roles. Systems allow office personnel and Makers%u2019 experts to connect remotely to the vessel, view operational parameters, assess equipment performance, and support the crew in troubleshooting. This improves response time, builds more confidence, and enhances the traditional handling of technical issues, eventually making it more efficient and reliable.In brief, the role of seafarers and shorebased teams is evolving toward greater emphasis on evaluation and informed decision-making rather than on May 2026 173