Shipbuilding has always absorbed the technologies of its era, from iron hulls to diesel propulsion to computer-aided design. The current transition is no different in kind, but it is different in scale. Digitalisation and artificial intelligence (AI) are not simply new tools added to an existing process, they are reshaping the logic of how vessels are conceived, built and operated. For shipyards that move quickly, the competitive implications are substantial.

 

Machine learning algorithms can evaluate thousands of hull configurations, propulsion options and internal layouts in the time it would take a design team to assess a handful. Predictive analytics can flag structural risks and schedule delays before they materialise on the shop floor. The effect is not just faster work, but qualitatively better decisions, made earlier, when they are still cheap to act on.

 

The design imperative

Design typically accounts for 5 to 10% of a vessel’s total production cost, yet that investment determines roughly 85% of final construction expenditure and conditions nearly 90% of operational performance across the vessel’s lifetime. The early decisions made regarding hull form, structural approach, propulsion and energy systems cascade through every subsequent phase. Getting them right is not merely a design office concern, it is the single greatest lever available to improve project economics.

 

Fragmented or linear workflows are no longer adequate to manage this responsibility. The interdependence between hull, structure, mechanical systems, piping and electrical architecture means that changes in one domain propagate unpredictably through others. AI-assisted integrated design environments address this directly: optimising weight distribution, identifying interference risks between components, and running multiphysics simulations that analyse several interacting physical phenomena simultaneously. Engineers can explore a far larger solution space in the concept phase, which is where that exploration has the highest return.

 

The traditional spiral design model, iterating sequentially through concept, preliminary and detailed phases, struggles to accommodate this level of interdisciplinary integration. A model-based approach, closer to the V-model used in other advanced engineering disciplines, better reflects how modern design actually works: in parallel, with continuous validation against requirements rather than staged handoffs.

 

Dr Rodrigo Pérez Fernández is senior director for software engineering at Siemens Digital Industries Software

Digital twins across the lifecycle

The digital twin has become a central concept in next-generation shipbuilding, although its value depends entirely on how it is implemented. A static geometric model is not a digital twin in any meaningful sense. The useful version is a live, data-enriched representation of the vessel that is updated throughout its lifecycle, first with design and simulation data, then with construction data, and finally with operational data from IoT sensors monitoring systems at sea.

 

This continuous feedback loop changes the economics of both construction and operation. Validating vessel behaviour under a wide range of conditions before the first steel plate is cut reduces costly late-stage design changes. Once in service, condition-based monitoring and predictive maintenance strategies, driven by real-time sensor data, can extend equipment lifespan and reduce unplanned downtime. Crucially, the operational data also feeds back into future design processes, improving the accuracy of the models used on the next vessel.

 

Underpinning all of this is what practitioners call the digital thread: a single, authoritative data environment that consolidates mechanical, electrical, piping and structural design into one system. Global teams work from the same model regardless of location or time zone, eliminating the version-control failures and conflicting drawings that have historically generated rework. The digital thread does not just accelerate the process; it changes its error profile, removing entire categories of mistake.

 

AI in the shipyard

The application of AI extends well beyond the design office. On the production floor, AI-driven planning systems optimise construction sequences, predict schedule risk and identify inefficiencies before they compound. Computer vision algorithms inspect welds and component alignment in real time, catching defects that human inspectors may miss under production-line conditions and that, if left undetected, become exponentially more expensive to rectify.

 

The integration of machine learning into computational fluid dynamics (CFD) simulations is particularly significant. CFD has long been a bottleneck in hull optimisation, computationally expensive and therefore limited in how many alternatives a design team can practically evaluate. Machine-learning-accelerated CFD dramatically shortens computation times, allowing iterative hull form optimisation across resistance, energy efficiency and fuel consumption without proportionally increasing engineering cost.

 

Augmented and virtual reality tools are changing workforce training and assembly guidance. Rather than relying on paper drawings or static digital files, technicians can work with spatially accurate overlays that guide complex assembly tasks, reducing errors and accelerating the learning curve for less experienced workers. As yards compete for skilled labour in a tight market, these tools have operational as well as quality implications.

 

Challenges that remain

None of this is straightforward to implement. Technological interoperability, getting legacy systems, supplier data and new platforms to communicate cleanly, remains a significant operational headache. Initial investment costs are substantial and the return on investment, while real, is distributed over years rather than visible in a single project. Cybersecurity risks increase as shipyard infrastructure becomes more connected. And workforce transformation requires sustained investment in training that many yards have historically under-resourced.

 

Regulatory compliance adds another layer of complexity. IMO emission reduction targets – 70 to 80% reduction in greenhouse gas emissions by 2040 and net zero by 2050 – create design requirements that did not exist a decade ago. Meeting those targets while managing cost and schedule pressure demands exactly the kind of multi-variable optimisation that AI tools are best suited to support. But it also requires regulatory frameworks to keep pace with the technologies being adopted, which is not always the case.

 

Product Lifecycle Management solutions have proven their value in managing the data complexity associated with these challenges. Yards that have centralised their data environments report improved resilience against supply chain disruptions, better customisation capability for client requirements, and more reliable planning processes. The pandemic-era supply chain failures accelerated adoption in a number of cases, demonstrating that digital integration is not just a competitive advantage but an operational necessity.

 

The direction of travel

The convergence of naval engineering and AI is not a future prospect – it is already visible in yards across Europe, Asia and the Americas. Digital twins are reducing construction time and cost. AI is cutting material waste and catching operational issues before delivery. Simulation tools are informing maintenance planning in military and commercial contexts alike. The technology is available; the differentiating variable is the organisational will and capability to deploy it effectively.

 

The next step in this evolution is the genuinely paperless vessel – not just a ship designed without drawings, but one operated and maintained through precise digital records, live system data and AI-supported decision-making throughout its service life. That is a more significant transformation than the industry has seen in generations, and the yards that position themselves for it now will have an advantage that compounds over time.

 

For naval architects, this shift redefines the scope of the discipline. The skills required to design a hull remain essential; the skills required to model its behaviour in a connected digital environment, and to interpret what that model tells you, are becoming equally so. The best engineering judgement has always been informed by the best available data. The change is that the data is now better, faster and more comprehensive than anything the industry has previously worked with.

 

This article appeared in Features, TNA Mar/Apr 2026