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By Kathryn G. Elagio
Siemens’ Bas Kuper explains why connected data, physics-informed AI and scalable cloud tools are becoming increasingly important to Asia Pacific’s manufacturing sector.
Manufacturers across Asia Pacific are under growing pressure to do more on several fronts at once. They must improve productivity, bring products to market faster, reduce energy consumption and build more resilient operations—all while keeping pace with rapid technological change.
At Siemens Realize LIVE in Bengaluru, India, Bas Kuper, Senior Vice President and Managing Director for Asia Pacific at Siemens Digital Industries Software, discussed how artificial intelligence is accelerating this transformation. He also explained why the value manufacturers gain from AI will depend largely on the strength of their digital foundations.
According to Kuper, AI has become one of the most prominent topics in industrial discussions across the region. China was an early mover, while interest has grown quickly in India and other Asian markets. However, he stressed that the opportunity extends well beyond generative AI.
Industries such as aerospace, marine and shipbuilding, batteries, electronics and automotive are creating new demand for advanced engineering and manufacturing technologies. Humanoid robotics is another emerging area attracting significant attention.
Siemens has identified hundreds of companies across Asia Pacific that are working on humanoid technologies in some form, Kuper said. Although the market remains relatively small, he expects it to grow rapidly and believes suppliers should begin developing the necessary capabilities now.
The same thinking applies to more established industries. Companies that connect their engineering, simulation and production data today will be in a stronger position to adopt new technologies as they become commercially viable.
AI needs a digital backbone
Kuper’s message is clear: manufacturers cannot scale AI effectively if their data remains scattered across departments, applications and file systems.
A connected digital thread brings together information from product design and engineering through manufacturing and the wider product lifecycle. This provides the context industrial AI needs to generate useful and dependable results.
Siemens has long positioned the digital thread as the backbone of its industrial software portfolio. The rise of AI is now making its value easier for customers to understand.
When information is connected, consistent and accessible, manufacturers can use AI to support engineering decisions, production planning, troubleshooting and continuous improvement. When data is fragmented or poorly managed, even the most advanced AI tools may struggle to deliver reliable insights.
“We are seeing an acceleration within companies in implementing that digital thread to be able to enjoy the benefits of AI,” Kuper said.
The shift, therefore, is not simply about adding another AI application. It is about creating a connected environment in which data, digital twins, simulation and AI can work together.
Sustainability starts with design
A connected digital environment can also help manufacturers address sustainability as part of their everyday decision-making rather than as a separate initiative.
Kuper noted that as much as 80% of a product’s environmental impact may already be determined during the design stage. Decisions about materials, geometry, manufacturing methods and performance requirements can all influence the resources a product will consume throughout its lifecycle.
Digital tools allow engineers to assess these effects earlier, compare different options and make changes before a product reaches the factory. The digital thread can then carry those decisions through production, creating a clear link between the original design and what is ultimately manufactured.
Simulation can also reveal opportunities to save energy on the shop floor. Manufacturers can test different production layouts, reduce unnecessary machine use and limit power consumption during idle periods before making physical changes.
Kuper cited customer examples in which improvements guided by simulation reduced factory energy consumption by approximately 20%.
This demonstrates how productivity and sustainability can often support each other. Reducing physical trial and error, improving equipment utilisation and using fewer resources can lower both operating costs and environmental impact.
Practical AI for engineering and production
Among the most promising applications today is physics-informed AI, according to Kuper. This approach combines engineering knowledge and physical models with machine learning, allowing manufacturers to accelerate simulations and explore more design possibilities while still accounting for real-world constraints.
AI copilots are another practical source of value. When built into engineering and manufacturing workflows, they can help users find information, identify problems and reach solutions more quickly.
These capabilities are becoming particularly useful as products grow more complex and experienced technical professionals become harder to find. Rather than replacing engineering judgement, AI can assist with routine and data-intensive work, leaving people with more time to focus on decisions that require experience, creativity and expertise.
Kuper also distinguished Siemens’ product-based approach from one-off AI projects. The aim is to offer capabilities that customers can use immediately and expand as their requirements develop, instead of building a separate system for every application.
Knowledge graphs and industrial ontologies play an important role by showing how different data points and concepts are connected. This context will become even more important as agentic AI systems begin to take on more complex industrial tasks.
A scalable path for SMEs
For smaller manufacturers, the challenge is not deciding whether digitalisation matters. It is finding a realistic way to begin despite limited capital, IT resources and specialist skills.
Kuper advises SMEs to choose a foundation that can grow with the business. A company should not have to replace its entire system or migrate all its data simply because it has expanded.
Cloud-based solutions are helping to make this possible. Preconfigured tools can reduce the time and effort needed for implementation while easing the burden of maintaining IT infrastructure. They can also give smaller companies access to the same core platforms used by large enterprises.
Siemens’ Designcenter X offerings, for example, combine cloud-based engineering applications with integrated data management. Companies can begin with a manageable set of tools and add further capabilities as their needs grow.
This flexibility is especially relevant in Asia, where today’s start-ups and specialist suppliers may become tomorrow’s industrial leaders. Smaller companies face many of the same engineering, quality and traceability challenges as larger manufacturers, Kuper said, but have fewer resources with which to address them.
Integrated data management and cloud-based tools can help narrow that gap.
Start with the data, then build value
The speed of technological change makes long-term predictions difficult, but Kuper’s priorities for manufacturers are practical and clear: build a connected digital thread, bring data under consistent management and choose cloud tools that can grow with the business.
From there, companies can apply AI to clearly defined engineering and production challenges where the results can be measured.
For Asia Pacific manufacturers, this step-by-step approach offers a way to move beyond the noise surrounding AI. Competitive advantage will not come from experimentation alone. It will come from embedding intelligence into the everyday workflows used to design products, plan factories and manage production.
Manufacturers that begin strengthening their data and processes now will be better prepared to turn the next generation of industrial AI into real improvements in speed, sustainability and resilience.