Courtesy: Siemens
Industry today is not facing a single technological change but a structural transformation. Markets are evolving faster than production systems, product life cycles are shortening, while industrial assets are designed to last for decades. At the same time, complexity along the entire value chain is increasing – technologically, organizationally, and regulatory. In this reality, adaptability becomes the decisive capability to secure and sustainably develop industrial value creation.
Within this context, classical automation reaches its structural limits. Automation based on fixed sequences, static logics, and extensive manual engineering can no longer keep up with the pace of modern industry. Efficiency gains within this paradigm are insufficient when products, processes, and frameworks are constantly changing – and they do not provide a sustainable foundation for the widespread use of artificial intelligence.
What is needed now is the next evolutionary step: the automation of automation itself. Instead of specifying every process in detail, industrial systems must be empowered to solve tasks autonomously – based on objectives, context, and continuous learning. Software-Defined Everything (SDx) becomes the necessary organising principle: it decouples functionality from specific hardware, creates a continuous, lifecycle-spanning data foundation, and enables systems to self-configure, adapt, and optimise.
In production, this approach manifests as Software-Defined Automation (SDA). SDA is the consistent application of Software-Defined Everything to the production automation layer. Control logic, functionality, and intelligence are decoupled from physical hardware, software-defined, and continuously developed. Hardware remains the stable, high-performance foundation, while software provides flexibility, adaptability, and learning capability to production systems.
This creates the structural basis for the AI-powered Digital Enterprise: an industrial organisation in which software, digital twins, and industrial AI work in closed-loop cycles, systems learn continuously, and decisions are not only prepared but also operationally executed. From this capability, the path to the Industrial Metaverse opens up – as the next stage of development, where planning, simulation, collaboration, and operational control converge in a shared digital space, supporting real industrial value creation in real time.
Stable foundation, flexible control: Software-Defined Automation in production
For many years, industrial functionality was inseparably tied to hardware. New requirements meant new components, modifications, or downtime. This model was stable – but no longer fast enough.
Software-Defined Everything breaks this logic. Functions, intelligence, and control are decoupled from specific hardware and moved into software. In production, this takes the form of Software-Defined Automation (SDA): the automation layer itself becomes software-defined, controlled, and continuously improved, while hardware continues to serve as a stable, high-performance foundation.
This fundamentally changes industrial systems:
- Functions can be adapted via software instead of physical modifications
- Systems evolve continuously throughout their lifecycle
- Adaptability becomes a structural characteristic
Industry becomes not only more digital but also definable, controllable, and optimizable through software.
Practical example: Software-Defined Automation in action
How this transformation is already becoming reality can be seen in the automotive industry. Companies, together with Siemens, are implementing Software-Defined Automation as an integral part of Software-Defined Everything. By introducing a virtual, TÜV-certified PLC, production control logic is no longer tied to physical control hardware but runs as software – centrally managed, flexibly scalable, and continuously updated.
This implements a core principle of SDA: the automation layer itself is software-defined. New functions can be rolled out via software, production systems can be quickly adapted to new vehicle variants, and updates and tests can be prepared and validated virtually. IT and OT environments converge into a unified, software-based operation.
The result is production that is not only more efficient but also learning- and AI-capable – a key prerequisite for the AI-powered Digital Enterprise.
Software-Defined as a bridge between goal and reality
The real value of Software-Defined Everything lies not in individual applications but in connecting the digital target picture with actual operations. SDx – and in production specifically SDA – enables the digital representation of target and actual states of industrial systems and products.
Real operational data from running plants is combined with target states from simulations, digital twins, and engineering models. Unlike isolated analytics or digital twin solutions, this creates a continuous, consistent data foundation across the entire lifecycle – from design through implementation to optimisation. Most importantly, it creates a bidirectional connection: digital insights directly influence operations.
Digital insights are no longer abstract. They become actionable.
Why Software-Defined Everything is the prerequisite for Industrial AI
Artificial intelligence only delivers value in industry if it can do more than analyse – it must act. On a software-defined data foundation, target and operational states can be continuously compared and contrasted. AI methods detect deviations, identify correlations across products, machines, and plants, and derive concrete optimisation recommendations.
The decisive step follows: Software-Defined Everything – and in production, Software-Defined Automation – closes the loop. AI-driven insights are directly translated into operational adjustments. Machines, processes, and products respond autonomously, without manual reconfiguration.
This creates learning systems that continuously improve – not as an exception, but as the standard.
The AI-powered Digital Enterprise: Learning as an operating system
When Software-Defined Everything, Software-Defined Automation, digital twins, and industrial AI interact, a new form of industrial organisation emerges. Products become platforms, production systems dynamically adapt to new variants and requirements, and knowledge is generated in ongoing operations and systematically made usable.
The AI-powered Digital Enterprise is therefore not a static target but a continuous learning process embedded within the systems themselves.
Industrial Metaverse: The consequence of a Software-Defined reality
From this development, the Industrial Metaverse becomes tangible – not as a visualisation, but as a new operational and management layer. When digital twins accurately reflect the real state, when AI prepares or autonomously makes decisions, and when software directly translates these decisions into real-world actions, the virtual space becomes the central environment for planning, collaboration, and optimisation.
Software-Defined Everything as a structural capability
Software-Defined Everything – with Software-Defined Automation as the core for production – is not a short-term trend or an isolated technology choice. It is the structural prerequisite to make industrial systems learning-capable, adaptable, and future-proof, and to unlock the full potential of AI for the industry of the future.
