Siemens is moving industrial AI beyond copilots toward what it calls "engineering intelligence" - AI grounded in trusted engineering data, digital twins and physics-based simulation. Tony Hemmelgarn, President and CEO of Siemens Digital Industries Software, laid out the vision at Realize Live Asia-Pacific 2026 in Bengaluru, saying the approach will speed product development while augmenting engineers rather than replacing them.
From copilots to engineering intelligence
Enterprise AI is built to assist knowledge workers with text and documents. Industrial AI must respect physics, engineering rules and manufacturing constraints. Hemmelgarn argued that conventional AI models cannot deliver reliable engineering outcomes unless they are grounded in what he called "engineering truth."
"AI only really works if it is built on engineering truth. It is grounded in how products actually function and not statistical guesses," he said. "When change is constant, clarity becomes a competitive advantage."
As products become software-defined, combining mechanical systems, electronics, embedded software and semiconductors, Siemens is embedding AI across the entire engineering lifecycle rather than treating it as an isolated assistant. The strategy centers on a digital twin that brings mechanical, electrical, software and manufacturing models into one engineering environment. Industrial AI is being built into engineering workflows instead of deployed as a standalone assistant - a distinction that matters for product teams evaluating AI for Product Development.
Trusted data is the new foundation
A recurring theme was that industrial AI depends on trusted engineering data. While many organizations invest heavily in AI models, Hemmelgarn said poor-quality engineering data remains one of the biggest barriers to deployment. Companies that manage engineering data on desktops won't be able to compete in AI, he argued.
Unlike conventional data lakes, engineering data carries design intent, product configurations, version histories and relationships between components. Simple data copies cannot preserve those relationships, Hemmelgarn said, making lifecycle management platforms more important as AI becomes embedded in product development.
Two India-based companies illustrate the shift. Sarla Aviation, a Bengaluru eVTOL maker, is using Siemens' integrated digital thread - Designcenter for product engineering, Simcenter for simulation, Capital for electrical systems, Polarion for lifecycle and certification management, and Teamcenter for PLM. Simple Energy, an EV maker, has increased its use of Teamcenter X and Designcenter X, replacing a legacy 3D CAD platform with a common lifecycle environment.
Physics-based AI accelerates simulation
The most tangible examples came from simulation. Instead of replacing engineering analysis, AI reduces the number of simulations engineers run, identifies promising design alternatives and accelerates computational workflows. Hemmelgarn said AI can cut analysis cycles from weeks to days, and in some GPU-accelerated scenarios, hours, by narrowing the design space before detailed physics-based validation begins.
Siemens' SimSolid and Physics AI technologies learn from historical simulation data to evaluate thousands of design alternatives in seconds, then hand shortlisted candidates to simulation specialists for final validation. "We are not replacing deterministic truth," Hemmelgarn said. "We are making it scalable."
Sam Mahalingam, Executive Vice President and Head of Simulation, HPC and AI at Siemens Digital Industries Software, said AI expands the role of simulation rather than reducing demand for expertise. AI explores a larger design space, he said, and then experts run physics-based simulation on the narrowed-down designs.
India's engineering opportunity
Siemens executives said industrial AI is likely to increase demand for engineers who combine domain expertise with AI capabilities. They referred repeatedly to "T-shaped" engineers - professionals with deep expertise in one discipline and the ability to work across adjacent domains.
Hemmelgarn said people who can think horizontally will become more valuable as AI helps engineers accelerate. The company said several multinational engineering organizations operating in India are increasing recruitment in simulation, AI application development and advanced engineering, rather than slowing hiring because of AI.
Why this matters for product development professionals
For product development teams, the takeaway is that AI value will come from data foundations and domain-grounded tools, not standalone copilots. Engineers and managers who build trusted data pipelines and learn how AI integrates with simulation and PLM tools will be better positioned than those chasing generic AI assistants. For product managers, understanding how AI fits into engineering workflows is becoming a core skill; an AI Learning Path for Product Managers offers a structured way to build that understanding.
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