Utilization of artificial intelligence in the shipbuilding project business
Swanljung, Claes (2026)
Diplomityö
2026
School of Energy Systems, Konetekniikka
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2026052553418
https://urn.fi/URN:NBN:fi-fe2026052553418
Tiivistelmä
This thesis examined how artificial intelligence can be utilized in the shipbuilding project business, as the area of review in cruise ship turnkey projects and the operating environment of the case company, Naval Interior Team Ltd (NIT). The aim was to identify suitable AI applications, assess organizational readiness, risks and develop a practical roadmap for AI implementation. The study combined a literature review and industry examples. KPI-based implementation planning was defined with internal employee and leadership surveys.
The results show that artificial intelligence is already widely used across industries, mainly in narrow and task-specific applications such as document processing, predictive maintenance, quality control, risk analysis and planning optimization. In the shipbuilding project business, the most promising near-term applications were found in document-heavy, repetitive and data-rich functions. For the case company, the strongest use cases were engineering documentation support, procurement and supplier comparison, reporting and meeting summary automation, internal knowledge search and selected support functions in project management. The survey results indicate that AI usage has already started for NIT in several departments, especially in engineering and design. AI usage maturity remains uneven and lower in production-related environments. The main barriers of AI usage are related to unclear governance, data security, reliability concerns, limited integration, lack of standardized practices, varying levels of competence and trust.
The thesis concludes that artificial intelligence should be developed as a managed organizational capability rather than adopted as an isolated tool. A governed, pilot-based implementation model with human oversight, measurable KPIs, training and gradual scaling of low-risk use cases is recommended. This improves productivity, documentation quality, decision support and competitiveness in the shipbuilding project business.
The results show that artificial intelligence is already widely used across industries, mainly in narrow and task-specific applications such as document processing, predictive maintenance, quality control, risk analysis and planning optimization. In the shipbuilding project business, the most promising near-term applications were found in document-heavy, repetitive and data-rich functions. For the case company, the strongest use cases were engineering documentation support, procurement and supplier comparison, reporting and meeting summary automation, internal knowledge search and selected support functions in project management. The survey results indicate that AI usage has already started for NIT in several departments, especially in engineering and design. AI usage maturity remains uneven and lower in production-related environments. The main barriers of AI usage are related to unclear governance, data security, reliability concerns, limited integration, lack of standardized practices, varying levels of competence and trust.
The thesis concludes that artificial intelligence should be developed as a managed organizational capability rather than adopted as an isolated tool. A governed, pilot-based implementation model with human oversight, measurable KPIs, training and gradual scaling of low-risk use cases is recommended. This improves productivity, documentation quality, decision support and competitiveness in the shipbuilding project business.