Robin Yeman, Technical Fellow at Leidos

Building complex, safety-critical cyber-physical systems has never been harder or more urgent. Organizations are under constant pressure to deliver faster, integrate emerging technologies like AI, and adapt to changing mission needs, all while maintaining rigorous safety, security, and compliance standards. Too often, MBSE, Agile, DevSecOps, and AI are treated as separate initiatives rather than parts of a coherent system development strategy.

This presentation introduces Industrial DevOps as a unifying approach for building better systems faster by intentionally using every tool in the toolbox models, code, pipelines, governance, and AI together. Attendees will explore how MBSE provides the backbone for system-level thinking, traceability, and decision-making, while Agile and DevSecOps practices are adapted for large-scale, regulated environments rather than copied wholesale from software-only contexts.

The talk will discuss the practical integration of AI and LLMs into system development workflows, including backlog refinement, risk analysis, architecture trade studies, compliance tracking, and threat modeling along with the governance required to apply AI safely and ethically in safety-critical domains. Real-world patterns will illustrate how AI-assisted workflows can accelerate learning and execution without eroding trust, accountability, or engineering rigor.

Participants will leave with a clear understanding of how to combine MBSE, Agile, DevSecOps, and AI into a continuous, system-level lifecycle one that balances speed with precision, innovation with assurance, and autonomy with responsibility. This presentation challenges teams to stop choosing between “fast” and “safe” and start designing development ecosystems that deliberately deliver both.

Enrique Krajmalnik, General Manager – ALM at PTC

TBA

Joseph Green, Chief Systems Engineer at Medtronic

The pace of technological advancement and customer expectation is accelerating exponentially, and traditional document-based approaches are no longer sufficient to meet the complexities and demands of modern solutions. More Systems Engineering time and resources are spent to achieve only incremental results – barely keeping up with demand and functional partners.

There is a flaw in how Systems Engineering is practiced, and only a transformation in approach can break the cycle and unleash Systems Engineers to create the value their organizations need. Model-Based Systems Engineering has been sold as the solution to these problems for decades, but organizational results have been mixed. Why are some teams able to cross the chasm while others fall back to legacy approaches? If MBSE is the future, when will the future arrive? This presentation will delve into the compelling case for adopting a model-based approach laying out what is required from an effective approach and the value such an approach can provide. An exploration of integrating MBSE into a Digital Engineering ecosystem will highlight how Continuous Integration (CI) can initiate a virtuous cycle of speed and quality, expose the valuable information contained in MBSE models, and move from a focus on artifacts to a focus on engineering and execution.

“The future is already here – it’s just not evenly distributed” – William Gibson

Arjen Spaans, Systems Engineering Workstream Lead at Airbus Defence & Space
Christoph Neuboeck, Eurodrone MBSE Model Manager at Airbus Defence & Space

MBSE is widely promoted as a key enabler for faster development, improved quality, and better decision-making. In industrial reality, particularly in defence projects, its success is shaped by a persistent tension between collaboration, integration, and segregation. This “devil’s triangle” defines everyday MBSE practice and fundamentally challenges the notion of a single source of truth.
We address the resulting misalignment between key stakeholders: chief engineers seeking concrete deliverables, MBSE experts striving for methodological perfection, and system architects focusing on solving specific use cases pragmatically. In addition, we highlight the stakeholders that are mostly largely missing when MBSE methods and tools were conceived, without whose needs MBSE cannot scale or even survive in regulated, multi-national and multi-partner environments.