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Workshops

AI4MBSE

Client/Partner · European Space Agency (ESA)

AI-Assisted Data Linking Across Engineering Domains

SysDICE designed and led a collaborative workshop with ESA to explore whether — and how — AI can help link data across model-based engineering domains, building a shared understanding of the problem space.

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Client

European Space Agency (ESA)

Industry

Space

Engagement focus

Exploring how AI can help link data across model-based domains

Status

Collaborative exploration · publicly presented

Project context

SysDICE designed and led a collaborative workshop with ESA to explore whether — and how — AI can help link data across model-based engineering domains, building a shared understanding of the problem space.

The challenge

Engineering information exists across many domains and formats. Teams face different data-linking problems and need a shared way to frame them, and the opportunities and limitations of AI in that context require structured exploration rather than assumption.

What SysDICE delivered

  • Workshop design and facilitation for a cross-domain audience.
  • Structured problem framing around the meaning of ‘linking data’ in a model-based context.
  • Cross-domain exploration organised around four questions: types of data linking; participant data-linking problems; whether AI can assist across domains; useful analogies.
  • Identification of opportunity areas for AI-assisted data linking between engineering domains.

Value created

  • A shared understanding of data-linking challenges across domains.
  • Clearer AI-support opportunity areas for engineering data work.
  • A collaborative foundation for further investigation and prototyping.

Capabilities applied

AI4MBSE

Facilitation

Data linking

Systems thinking

Cross-domain engineering

Opportunity discovery

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Project journey

How the project unfolded

A step-by-step view of the path from framing the problem to establishing a way forward.

01

Step 1

Frame questions

Design the four guiding questions that anchor the workshop and keep the discussion concrete.

02

Step 2

Gather domain challenges

Surface the specific data-linking problems participants face across their model-based domains.

03

Step 3
Explore analogies & link types
Use analogies and a shared vocabulary to map the different kinds of data linking involved.

04

Step 4
Evaluate AI assistance
Assess where AI can genuinely assist cross-domain data linking — and where it cannot.

05

Step 5
Define next opportunities
Agree the opportunity areas worth exploring further as AI-assisted data-linking work matures.

Consulting · Implementation · Training

How this project connects to SysDICE's way of working

Every SysDICE engagement blends consulting, implementation and training. Here’s how those three modes came together on this project.

Consulting

Structured problem framing and opportunity discovery for AI-assisted data linking across engineering domains.

Implementation

Groundwork for future AI-assisted data-linking work — no production AI solution was implemented in this workshop.

Training

A facilitated learning experience that built shared vocabulary and understanding across participating teams.

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