What Role Does Design Play in Autonomous Industrial Systems?

00:00 – Introduction and presentation by Matthias Staubach

01:40 – Smart Infrastructure: Products at the interface between hardware and software

03:40 – User groups and requirements in safety-critical systems

06:30 – UX beyond consumer tech: Simplicity and consequences

07:00 – Automation and the role of humans

10:30 – The autonomous building: scenarios and how it works

13:00 – Data as the foundation for Industrial AI

15:50 – AI, domain knowledge and the limits of automation

18:00 – Where does UX come into play in autonomous systems?

19:30 – Structure and role of the global UX team

21:40 – Interfaces, design systems and regulatory requirements

24:00 – Why domain knowledge makes design irreplaceable

27:45 – Requirements for future designers

31:00 – Design belief: fewer interfaces, greater impact

35:00 – Conclusion and closing remarks

Designing for safety-critical systems: Simplicity under real-world risk conditions

The systems that Siemens works on, such as those in the field of fire safety, are highly complex and are operated by a variety of user groups, ranging from technicians and facility managers to members of the public in an emergency. In this context, 'design' does not mean simplification in the traditional sense, but rather reducing complexity under real-world risk conditions. It is crucial that users understand what to do and the consequences of their decisions, even in stressful situations.

UX as system design: balancing hardware, data and infrastructure

In an industrial context, UX is inextricably linked to physical products. Sensors continuously generate data, which is made available via cloud systems and applications. Design shapes these interconnections and ensures that information is accessible at the right moment. Consequently, UX becomes less about designing individual interfaces and more about orchestrating complex systems.

AI is an accelerator, not a substitute, for design and domain expertise

Artificial intelligence is fundamentally transforming the development of industrial systems. In this context, however, it acts primarily as an accelerator, rather than a substitute, for design. Domain knowledge remains crucial: only those who understand how systems are actually used can develop meaningful solutions. The real competitive advantage lies less in the technology itself than in the quality and use of available data.

Autonomous systems require trust, and the 'human in the loop' approach remains central

Even with increasing automation, humans remain crucial, particularly when it comes to assessing consequences. Concepts such as 'human-in-the-loop' (active decision-making) and 'human-on-the-loop' (pure monitoring) demonstrate how the role of humans is evolving. As systems become increasingly autonomous, design becomes the key to ensuring trust, control and transparency.