Integrating Artificial Intelligence into Naval Capability Development

The rapid advancement of artificial intelligence (AI) is transforming naval capabilities, reshaping ship design, lifecycle management, operational decision-making, and autonomous maritime systems. Naval platforms are among the most complex engineered systems, characterised by long service lives, safety-critical functions, and demanding operational environments, making AI integration both strategically attractive and technically challenging. This paper presents an engineering-oriented review of AI applications in the naval domain, focusing on their role across the capability development lifecycle. To illustrate practical implementation, a Random Forest regression model is developed to support early-stage prediction of the block coefficient of naval ships. The review highlights significant opportunities associated with AI integration, including enhanced decision-making, improved design efficiency, and increased operational effectiveness. However, successful AI adoption requires technological advancement alongside organisational adaptation, strong governance, and sustained investment in human expertise. AI should therefore be understood not as a replacement for naval engineering expertise, but as a force multiplier that augments analytical capacity and accelerates innovation across the maritime domain.

Authors

Publication Details

Journal
TransNav the International Journal on Marine Navigation and Safety of Sea Transportation
Published
2026-01-01
DOI
https://doi.org/10.12716/1001.20.01.19
Primary Topic
Maritime Transport Emissions and Efficiency
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Integrating Artificial Intelligence into Naval Capability Development

Igor Štambuk, Andrija Ljulj, Vedran Slapničar
TransNav the International Journal on Marine Navigation and Safety of Sea Transportation
Maritime Transport Emissions and Efficiency
article

Integrating Artificial Intelligence into Naval Capability Development

Igor Štambuk, Andrija Ljulj, Vedran Slapničar
article en

Abstract

The rapid advancement of artificial intelligence (AI) is transforming naval capabilities, reshaping ship design, lifecycle management, operational decision-making, and autonomous maritime systems. Naval platforms are among the most complex engineered systems, characterised by long service lives, safety-critical functions, and demanding operational environments, making AI integration both strategically attractive and technically challenging. This paper presents an engineering-oriented review of AI applications in the naval domain, focusing on their role across the capability development lifecycle. To illustrate practical implementation, a Random Forest regression model is developed to support early-stage prediction of the block coefficient of naval ships. The review highlights significant opportunities associated with AI integration, including enhanced decision-making, improved design efficiency, and increased operational effectiveness. However, successful AI adoption requires technological advancement alongside organisational adaptation, strong governance, and sustained investment in human expertise. AI should therefore be understood not as a replacement for naval engineering expertise, but as a force multiplier that augments analytical capacity and accelerates innovation across the maritime domain.

TransNav the International Journal on Marine Navigation and Safety of Sea TransportationVol. 20(1)
Openalex Percentile: Top 12%
Maritime Transport Emissions and Efficiency
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.