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:

User Experience (UX) in the Age of AI

By trade, I am a UX designer. My heart beats for intuitive interfaces, consistent user experiences, and the question: How does a user get from A to B as easily as possible? For years, the answer in business software was always fairly straight forward: clear menus, learnable patterns, and efficient paths through the system. But is this question still relevant given the impact of AI on UX?

Artificial intelligence doesn’t just change what software can do. It changes how we work with software. This fundamentally reshapes our definition of what a ‘good user experience’ actually means.

The end of the classic user interface?

Let’s look at an everyday engineering example: You receive an email. Your supplier informs you that component #4711 will no longer be available starting in March. Previously, you would click through the bill of materials (BOM), manually search for where it is used, research alternatives in another system, create a change request, and notify colleagues via email. All of this takes hours, scattered across multiple tools.

Soon, you will only have to say: “Component #4711 is discontinued – what is affected and what are the alternatives?” The system independently checks all assemblies, suggests qualified replacement parts, and prepares the change request for approval.

Take it one step further into the future, and you won’t even have to ask. The system detects the impact itself, proactively suggests the change, and simply waits for your approval.

H2: New requirements for AI UX

This example illustrates the fundamental shift that AI capabilities and agents are currently ushering into engineering environments. It’s a transformation that upends much of what we’ve learned about UX.

The AI UX of tomorrow will no longer be measured by how intuitive an interface is to use. Instead, it will be measured by:

  • …how well the system understands the user’s intent
  • …how relevant the proactive suggestions are
  • …how intelligently the automation works in the background

Simply put: The best UX will probably be the one that requires the least amount of interface interaction to get the job done.

Five principles redefine UX

What does this mean in practice? I see five shifts emerging—especially in the engineering environment.

1. Conversation instead of clicks

Voice and text are becoming the primary interface. Not because clicking is bad, but because natural language is closer to our actual intent than any menu structure. A well-trained system understands what is meant—even if the phrasing isn’t perfect.

This requires a radical rethink: It is no longer about users learning how to navigate the interface. The system must learn to understand humans.

2. Proactive instead of reactive

Today’s software waits for input. Future systems act on their own: they identify risks before they arise, suggest actions before the user looks for them, and point out inconsistencies before they turn into problems.

A quality agent that monitors data streams and automatically initiates corrective action – this is not science fiction. It is a concrete architectural decision that can be made today.

3. Adaptive workspace – just in time

The interface configures itself based on role, task, and data context. A design engineer needs a different interface than a project manager or a quality assurance manager. And what you need at 9:00 AM might be completely different from what you need at 3:00 PM.

Intelligent systems adapt to the human – not the other way around.

4. Knowledge graph instead of keyword search

Classic search finds documents containing a keyword. Semantic search based on a knowledge graph understands relationships: Where is this component used? Which variants are affected? What changed in the last version?

This difference sounds technical, but it has immediate UX consequences: users no longer need to know exactly what to search for. The system delivers the right context.

5. Human-in-the-loop

As impressive as AI agents are, decision-making authority remains with humans – and it always should.

Good AI UX means finding the right moment for the system to pause and wait for human approval—not too early (which would be annoying), and not too late (which would be risky).

Human-in-the-Loop is not just a safety net. It is a design decision that builds trust.

A fleet of digital colleagues

One analogy helps me when I think about the future of working with AI: Imagine that instead of getting a new tool, you are getting new colleagues who take care of engineering and design, who specialize in quality and compliance, and keep an eye on the supply chain.

These digital agent-colleagues work silently in the background, orchestrated and coordinated with one another. They are not omnipresent, but they are always there when you need them.

This changes not only the work itself, but also our relationship with software. A tool that must be operated becomes a partner that thinks ahead.

AI assistants integrated into CONTACT Elements support engineers with their daily tasks

What this means in practice

These changes won’t happen overnight, and they are not a given. Anyone who wants to benefit from intelligent UX needs the right foundation: structured data and clear governance. And a system that views AI not as an add-on, but as an integral component.

The quality of AI outputs is directly dependent on the quality of inputs. An agent accessing unstructured or incomplete data will deliver unreliable results and poor UX, no matter how well-designed the interface is.

That is why my key takeaway from my work over the last few months is: Investments in data quality and platform architecture are investments in User Experience.

Putting people at the center

AI changes the interface, but not the objective. Technology should empower people to do their jobs well. With less friction, more context, and stronger foundations for decisions.

The fact that this will happen in the future via conversation instead of clicks, proactive suggestions instead of empty text fields, and semantic connections instead of keyword searches is not a threat to good UX design. It is an invitation to rethink what “intuitive” actually means.

Deploy AI the right way

Artificial intelligence only delivers real impact when AI features are systematically and valuably integrated into your processes. Discover how this works in our AI Insights. This newsletter provides you with tangible real-world examples, best practices, and valuable tips on all aspects of engineering and manufacturing intelligence.