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Factories are integrating AI tools to improve the manufacturing process; robotics and physical AI represent the next phase of evolution.
Physical AI and the new energy calculus of manufacturing
While the energy transition is often associated with renewables and EVs, industry remains the world’s largest energy-consuming sector, accounting for nearly 40% of final global energy demand and around two-thirds of new energy demand (see Figure 1). This makes it one of the largest opportunities for efficiency gains.1,2 Physical AI, which integrates sensing, connectivity, and the real-time inference abilities of AI models into real-world environments, is emerging as a key enabler that should drive further electrification, particularly in industrial manufacturing.
Figure 1 – Industry’s share of new energy demand growth
AI use cases: What the industrial leaders are building
Among leading industrial technology companies, there’s a race to own the physical AI stack. Siemens’s wheeled-humanoid robot is already live in factories, autonomously choosing, transporting, and handling containers.3 It’s also using AI agents and real-time engineering data to simulate and discover process and cost efficiencies before factory-level modifications.4
French industrial giant Schneider Electric is using AI-enabled digital twins of advanced power systems to simulate power requirements from the utility grid down to individual chips.5 Japan’s industrial conglomerate, Hitachi, deploys AI agents to diagnose industrial equipment issues with high accuracy and short response times.
Energy efficiency in both brains and brawn
Actuators, which turn energy into movement, are a less discussed but rapidly emerging dimension of factory energy efficiency. Actuators within robots can be characterised as ‘the brawn’ of physical AI in manufacturing, where AI-enabled data processing and inference provide ‘the brains’. Robots – from today’s collaborative arms to next-gen humanoids – are ultimately an assembly of motion-control systems. Their high energy demand makes improving energy efficiency a key focus.
Electric motors in robotic joints achieve roughly 80% efficiency in isolation, but that figure drops to approximately 40% when motion-control gear is included.6 Humanoids, which can contain dozens of actuators as well as other energy-consuming components, are full-bodied energy challenges. Tesla’s Optimus, Agility Robotics’ Digit, and Apptronik’s Apollo at Mercedes-Benz run for only two to four hours per charge, limited by actuator inefficiencies, heat drain, and low-energy-density batteries.7 Across a global installed base of some 4.3 million industrial robots,8 aggregate energy losses will be substantial, making AI-optimised motion planning a powerful area of efficiency and competitive advantage.
Figure 2 – Actuators form up to 60% of a humanoid’s high-impact components9
The investment imperative
As industry accounts for 40% of global final energy demand, the rise of physical AI is creating a powerful investment opportunity across both the intelligent ‘brains’ and energy-intensive ‘brawn’ of industrial systems. Success will depend not only on adopting AI, but on deploying it efficiently through advanced semiconductors, software, automation, robotics, electrification and data-driven optimisation. Beyond the factory floor, AI is also transforming energy distribution and management through smart grids, storage and power electronics. With long-standing exposure across the entire energy value chain, the Smart Energy strategy is uniquely positioned to identify the companies best placed to integrate AI, optimise energy flows and capture future growth opportunities.
All companies referenced in this article are for illustrative purposes only in order to demonstrate the investment strategy on the date stated. The companies are not necessarily held by the strategy nor is future inclusion guaranteed. This is not a buy, sell or hold recommendation, nor should any inference be made on the future development of these companies.
Footnotes
1. IEA website, “Industry — Energy Efficiency 2025.
2. IEA, Energy Efficiency Market Report, November 2025. Total final consumption in 2024 was over 450 EJ and has grown by around 25 EJ since 2019. Industry accounts for the largest share of this demand, at nearly 40%. Industry saw the strongest growth since 2019, contributing two-thirds of the total increase in global energy demand.
3. Robeco company research; Siemens management disclosures, Hannover Messe 2025. Wheeled humanoid performed pick, transport, and handling tasks autonomously in live factory.
4. Robeco company research; Siemens Digital Twin Composer announcement, 2025. Scheduled launch mid–2026 via Siemens Marketplace; built on NVIDIA Omniverse.
5. Robeco company research; Schneider Electric / AVEVA / NVIDIA disclosures. Grid-to-chip digital twin modelled with ETAP software.
6. Qviro, technical analysis cited in Articsledge, “AI Humanoid Robots 2026,” 2026.
7. GlobalSpec, “Humanoid Robots Are Tripping Over Their High Energy Demands,” September 2025. Current runtimes: 2–4 hours per charge.
8. International Federation of Robotics, World Robotics 2024.
9. McKinsey & Company, Turning humanoid supply chain constraints into billion–dollar wins, April 2026.
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This is a sponsored advertorial from Robeco.