Siemens (SIE) CES 2026 Keynote summary

Keynote insights and strategic vision

• AI is positioned as the next transformative force for industry, comparable to electricity’s impact a century ago, with rapid adoption expected within seven years.

• Industrial AI is being scaled through digital twins, intelligent automation, and partnerships, enabling real-time optimization and autonomous operations across manufacturing, supply chains, and infrastructure.

• Partnerships with technology leaders like NVIDIA, AWS, and Microsoft are central to building scalable, reliable, and responsible AI solutions for industry.

• Siemens Xcelerator Marketplace is highlighted as a unified platform for industrial AI technologies, data, and expertise.

• AI-driven digital twins and simulation tools are accelerating product development, manufacturing, and operational efficiency, with real-world examples from sectors like shipbuilding, automotive, and life sciences.

Industry collaborations and customer impact

• Siemens and NVIDIA are intensifying collaboration in five areas: AI-native chip design, AI-native simulation, adaptive manufacturing, AI factories, and internal technology adoption.

• Real-world use cases include HD Hyundai’s shipbuilding digital twins, Foxconn’s AI-driven factories, and PepsiCo’s deployment of Digital Twin Composer for warehouse and plant optimization.

• PepsiCo reports a 20% efficiency increase and 10–15% CapEx reduction in pilot facilities using digital twins and AI-powered simulation.

• Hero MotoCorp leverages cloud-based PLM and AI to halve product development time and enable global collaboration.

• Disney, through startup Haddy and Siemens, uses large-format 3D printing and digital threads to rapidly create durable, sustainable props for theme parks.

Technology trends and analyst perspectives

• Agentic AI and physical AI are emerging as the next phases, enabling autonomous systems and robots to reason, act, and adapt in real-world environments.

• Edge AI and vertical (domain-specific) AI models are gaining traction, with inference and decision-making moving closer to machines and production lines.

• Ecosystem collaboration, open platforms, and low-code tools like Mendix are democratizing access to industrial AI and accelerating adoption for companies of all sizes.

• Trust, data integration, and change management are identified as critical hurdles for industrial AI, with successful projects grounded in ROI, strong partnerships, and iterative scaling.

• The industrial metaverse, combining digital twins, AI, and immersive simulation, is seen as a near-term reality for optimizing design, manufacturing, and operations.