Shipbuilding meets digitalization: two years in the SEUS research project

Two intense years in the European research project SEUS are now behind me. Our shared goal: to drive the digitalization of European shipbuilding forward. The need for this remains as pressing as ever. The EU continuously funds research and development projects in the maritime sector to strengthen Europe’s position in global competition – which is vital for survival given the low-wage countries and extensive subsidies in markets outside of Europe.

Although European quality is highly valued in the maritime industry, cost pressures are forcing even this traditional sector to achieve massive efficiency gains and optimize its processes. This is exactly where we at CONTACT came in: as an integral partner in this research and a driving force behind the transformation, our goal was to provide the crucial data backbone required for successful digitalization.

A floating city – and what it has to do with PLM

First, we had to dive deep into the complex requirements and processes of shipbuilding to truly understand them. Through intensive discussions with shipyards, in-depth analyses, and a thorough review of the current state of research, we were able to build a comprehensive foundation of knowledge.

What really impressed us was the meticulous care with which the shipyards in this project process enormous volumes of information, collaborate securely across disciplines, and confidently navigate strict classification requirements. This involves tens of thousands of pages of test reports, calculations, manuals, and technical specifications.

What is being built here is nothing less than a small, self-sustaining city floating across the oceans – a construction process that never fails to fascinate. This is just as true for classic cargo and container vessels as it is for highly specialized cable-laying vessels, complex research ships, or military vessels.

The data backbone: connecting the maritime world

The greatest challenge lies in efficiently managing and transparently controlling the diverse information generated across all disciplines, engineering phases, and construction stages. To address this, we developed a data model specifically tailored to shipbuilding that connects all types of information, including project schedules, CAD models, simulation results, engineering designs, and supplier contracts.

Viewing the ship as a “physically massive and complex system” is crucial here. This means that instead of a few individuals keeping track of the entire vessel, a large number of engineers divide the work into smaller areas of responsibility. Depending on the engineering task and phase, they look at the ship from different angles. For example, while designing the propulsion unit focuses primarily on the architecture of that specific system, designing the ship’s hull centers on spatial layout.

A traditional product structure, which hierarchically organizes parts and assemblies based on how they fit together, cannot meet these demands. There is not one “true” product structure; instead, there are multiple viewpoints: systemic, spatial, production-centric, and module-oriented.
Many shipyards and engineering offices use a centralized system structure, as a large portion of the work involves designing, engineering, and integrating systems of all kinds. In Europe, the “SFI Group System” has established itself as the de facto standard for this purpose. This three-level standard catalog contains 4,080 entries for systems and subsystems that can be found in any type of vessel.

Schematic Diagram of Shipbuilding
CONTACT’s data model expands the standard catalog with additional perspectives.

Our IT architects designed a data model that systematically maps these other perspectives around this standard (for details, see our paper on Zenodo). In early-stage ship design, systems are first roughly dimensioned using placeholders. For example, you might specify that an engine is needed, but not yet which particular model. These placeholders – referred to below as “items” – are organized according to the SFI Group System catalogs.

As development progresses, we link these items to additional structures that form the basis of the other perspectives. Using the previous propulsion example, this could mean selecting a specific engine for that item. In addition to relevant documents, requirements, specifications, and project tasks, a CAD model can also be linked. Our partner Cadmatic enables this through a deep integration of CAD tools.

Although the project is not yet complete, a shipbuilding-specific PLM backbone, the flexible and modular CONTACT Elements platform, and the deep integration with marine CAD software already form a solid foundation for further application-level development. The sheer scale of these projects highlights why this is so critical: if even ten documents, such as specifications, CAD files, analyses, manuals, and test reports, are associated with each of the up to 4,000 possible subsystems, you quickly end up with massive amounts of data that would be uncontrollable without structured management.

On top of that, each of these documents goes through its own lifecycles, reviews, and approvals that must be coordinated both internally and with external partners. Seamless, traceable document management across corporate boundaries is therefore absolutely vital, and thanks to an extension in CONTACT Elements for shipbuilding, it is fully achievable.

To learn more about the background and the partners involved, check out the SEUS Annual Report 2025.

From hype to reality: Why AI system integration is critical

The hype surrounding Artificial Intelligence in engineering is massive. Almost every software vendor now offers smart AI features. When applied correctly, AI-powered solutions deliver maximum leverage right where engineers and designers waste valuable time every day, whether on tedious routine work or background tasks. Examples include automated test case generation, understanding complex documents, or quickly assessing minor design changes.

But behind the scenes, the industry’s reality paints a sobering picture. According to a CIMdata study, while about 80% of software vendors already offer AI features in their portfolios, the actual adoption rate among industrial customers is only 8% to 33%. Nearly nine out of ten industrial companies use AI in less than a quarter of their projects, leaving most initiatives stuck in pilot mode. So why is adoption stalling?

The challenge: the invisible data wall

The biggest hurdle isn’t the algorithm itself, but what comes next: AI integration. For industrial customers, seamless connection to existing systems is a key selection criterion. Yet software and service providers systematically underestimate this barrier. In practice, service providers encounter unexpected issues with legacy systems 2.4 times more often than customers anticipate. When new AI tools are bolted onto existing systems in isolation, they fail when faced with the reality of complex engineering processes. As our Chief Product Officer Frank Patz-Brockmann puts it: “The real pain point isn’t buying the software but getting it activated.”

Integration over API patchwork

CONTACT Software is tackling this problem head-on with Fourier AI. We need to view AI system integration as a fundamental architectural decision, not an afterthought or a quick DIY project. Instead of connecting an isolated external AI via standard interfaces, Fourier AI is deeply embedded as a fully integrated intelligence layer within our proven CONTACT Elements platform.

In customer discussions, we sometimes encounter an urge to throw established data structures overboard and replace them with a “data lake” or even a “data swamp” in the hope that the AI will somehow piece the information together on its own. However, this is entirely the wrong approach, because AI thrives on structured data.

That’s why with Fourier AI, we rely on a clear, bottom-up layered architecture:

  1. A Single Source of Truth (PLM) as the foundation: At the very base is still the PLM system with its highly structured data. This foundation is essential and serves as the reliable starting point for any intelligence.
  2. Context building: Simply storing data somewhere isn’t enough. A precise context must be constructed for the AI. For example, if a user submits a prompt like “Give me all data on this Engineering Change,” this context layer needs to know exactly which data belongs to it, who created it, and how everything is connected.
  3. The model layer: Sitting above that is the model layer. In addition to leading external models, we offer our own specialized models, such as for 3D similarity search. The system automatically selects the right model for specific platform tasks while giving companies the freedom to choose which models they want to access for their custom use cases.
  4. AI orchestration & governance: To evaluate and continuously improve answers, generated execution traces are captured and logged. The system filters data so the model only returns information the specific user is authorized to see via integrated permission controls.
  5. User interaction (the PLM chatbot): At the top layer is the user, who interacts directly by typing a query into the PLM chatbot, for example.

Conclusion: Foundation first, value follows

This deep architectural approach initially requires more diligence when structuring data and preparing systems. But that’s precisely where the key advantage lies: because the foundation is cleanly built from day one, the AI layer automatically adapts whenever a customer expands their data model in CONTACT Elements – without writing a single line of additional code. This is how we bridge the gap between mere tech gimmicks and genuine, productive value in day-to-day engineering.

Learn more about this topic in our Live Talk:

Why AI engineering needs PLM

Artificial Intelligence (AI) is already an integral part of modern engineering. Especially when you‘re dealing with the development of complex machinery and its major challenges. For example, long product lifecycles, safety, compliance, and service traceability, decades of legacy product knowledge and multi-discipline engineering (mechanical, electrical, software, controls). In these areas, AI can help to reduce complexity, and thus, increase development speed and time-to-market.

Tools like ChatGPT and GitHub Copilot support many areas across a wide range of tasks. While these applications are easy to use, successful AI engineering is far more complex. This is due to intricate processes as well as a frequent lack of data quality, quantity, and infrastructure.

Here, PLM systems come into play.

PLM software not only provides a platform for managing product data. It’s an important interface for implementing AI-powered technologies across various engineering phases.

How to implement industrial AI in engineering?

Model training and data provisioning remain key hurdles for successful industrial AI.

Ready-to-use AI applications like ChatGPT and DALL-E are available for general purposes, such as text processing or image generation. However, there is a lack of instantly usable applications or models for complex industrial issues. Publicly available datasets rarely meet specific industrial requirements. As a result, you need to fine-tune AI models and build company-specific datasets and custom models.

In addition, deploying AI models in industrial environments presents further complexities. This includes

  • the integration with existing systems,
  • access security and authorization when managing the underlying data as well as
  • ensuring reliability and performance.

PLM systems provide the infrastructure to collect, structure, and process data. They facilitate AI model integration into existing processes and offer a central platform for managing AI solutions throughout the entire product lifecycle. This includes ensuring data quality, version control, and managing the generated outputs.

By connecting AI tools and PLM systems, your company ensures a consistently structured use of data. Thus, you built the foundation for successful AI applications.

A second major challenge lies in the complexity of AI implementation itself. This ranges from the selection of suitable models and algorithms to the adaptation of these models to the specific requirements of your company. To make AI engineering work, IT, engineering, and data science must collaborate closely. PLM systems facilitate this by providing a unified platform that bridges these disciplines and drives teamwork.

PLM systems as enablers for AI

PLM systems with deep integration capabilities create the necessary conditions for incorporating AI into existing development processes. As central data hubs, they enable access to data from various sources in the product lifecycle.

By providing a consolidated data foundation, PLM systems clear the path for generative AI (GenAI) technologies. These advanced solutions independently generate fresh, creative content, from text to images, offering companies unprecedented opportunities, particularly within design and product development.

By facilitating the integration of AI-powered design tools, PLM systems ensure efficient management and versioning of AI-generated designs. For instance, AI-powered tools can automatically generate design variants of CAD models. They can also optimize these designs based on simulation results.

Connecting AI solutions to existing data structures is another critical aspect. By integrating AI into PLM systems, AI-powered design tools can be linked directly to your product data. This accelerates the entire development process. This improves component reuse, enhances collaboration between teams, and continuously optimizes product development.

Furthermore, PLM systems capture AI-generated insights and optimizations in a central repository, making them available for future use. As a result, you can significantly increase your capacity to innovate.

AI-based applications in engineering

The interaction between GenAI and PLM systems enables the integration of AI-driven design processes into existing workflows. This unlocks a wide range of applications:

Automated design variants

AI creates new design variations based on existing CAD data and optimizes them for parameters such as cost, material usage, or production options. This gives your engineers more time for creative design tasks.

Simulation-driven optimization

The synergy between simulation tools and GenAI algorithms enables a continuous feedback loop, allowing design concepts to be iteratively refined and optimized. This helps you to identify the best design choices based on comprehensive data analysis. It reduces the number of physical prototypes and saves both time and costs.

Optimized product development

AI dynamically adjusts designs to new requirements during the ongoing development process. In connection with the PLM system, your company can react faster to changes and adapt product development without delays. This offers significant benefits, especially for mechanical and plant engineering.

Time-series forecasting

GenAI analyzes time-series data to predict future developments. By leveraging historical and operational data, PLM systems combined with AI help you to identify trends at an early stage and make informed decisions.

KI-Anwendungen für Ingenieur*innen

Data management and deployment

Integrating AI into industrial engineering is not only a technical challenge but also an organizational one.

Successful AI deployment requires suitable data management. PLM systems provide a platform to manage data in a structured manner – from collection and storage to provision for AI models. Data protection, security, and availability must be guaranteed.

In addition to data processing, AI deployment – the availability in the production environment – is also crucial. To integrate AI, your IT and engineering teams must work together. PLM systems make this process secure and efficient, while ensuring employees can easily use AI in their daily work.

Therefore, PLM systems must support the development and integration of AI models as well as ensure their operation and maintenance in day-to-day business.

PLM systems also serve as a platform to monitor, update, and improve AI models. By using operational data, these models can easily adapt to changes, ensuring consistently high product quality.

Trends and perspectives

PLM systems will increasingly serve as central platforms for managing and integrating AI solutions. AI-powered automation will become more prevalent, as the ability to learn from data and recognize complex relationships is constantly improving.

Another trend is the growing integration of AI in collaboration across different departments and companies. PLM systems will develop into platforms that support both internal processes and seamless cooperation across company boundaries. This leads to more efficient supply chains and accelerated innovation. Additionally, explainable and transparent AI models are becoming increasingly important to strengthen trust in AI and increase acceptance in safety-critical areas of engineering.

In die PLM-Lösung CONTACT Elements integrierter KI-Assistent (Screenshot).

Success factors for AI in engineering

Data availability and quality

High-quality data and its availability form the basis for the success of AI applications. PLM systems ensure that the required data is available in the appropriate quality through consistent data collection and management. This allows models to train on a solid database and deliver precise and reliable results.

Seamless process integration

To exploit the full added value of AI technologies, you must integrate them into existing workflows. PLM systems integrate AI-powered applications into existing processes and simplify their use. As a result, AI solutions can be implemented into existing systems without major adjustments and put into operation more quickly to deliver relevant results immediately.

Training and change management

Companies must prepare their employees specifically for the use of the new tools by providing technical training and conveying an understanding of their use in the respective work context. Well-planned change management promotes acceptance and actively involves all employees in the change process.

Management support

Support from management is crucial for the successful introduction of AI in engineering. Communicating clear goals and strategies for the AI transformation is just as important as providing the necessary resources. Meanwhile, management must foster a culture of change.

Conclusion

The integration of AI is revolutionizing engineering. PLM systems play a key role as the central hub for data. They create the necessary infrastructure to integrate AI applications into existing processes and use them efficiently. This enables you to optimize your product development processes and exploit the full potential of AI.

The path to success lies in the sensible use of data, the integration of AI models into the existing IT landscape, and the involvement of employees. Only those who tackle these challenges in a targeted manner will be able to shape the AI transformation in engineering sustainably.

A strategic approach is key. Companies should focus equally on technology and people. This is how they sustainably unlock the full potential of artificial intelligence in engineering.

Webcast: Accelerate complex machinery development

Ready for real results? If your team’s AI projects are still struggling with messy data or adding unwanted complexity to your product development, it’s time for a reality check. Watch our Industry Talk to discover what it takes to make AI deliver reliable value for your engineering team.

What you will learn:

The Pitfalls: Why most AI initiatives fall short in complex product data environments.

The Foundation: The non-negotiable foundation needed to deliver truly reliable AI insights.

The Integration: Practical strategies to seamlessly embed AI within your existing engineering workflow.