Beyond robotics: how AI is rewiring the physical workflows that power energy and infrastructure

When most people hear Physical AI, their minds jump immediately to robots: machines that sense, move, and act in the real world. But the bigger story is the thousands of engineering processes that already run our industrial world, processes that already depend on physical parameters such as heat transfer, pressure, load, flow, chemical reactions, degradation, and drift. These processes unfold continuously inside power plants, grids, factories, pipelines, and transportation systems, yet they remain governed by legacy models and human intuition. Physical AI technologies are increasingly able to learn these systems directly, predict their behaviour, and optimise them in real time.

Engineering is already seeing meaningful gains from applying AI to physics-driven workflows. AI-enabled simulation and design automation cut iteration times by 30-50% and reduce R&D costs by up to 20% (BCG). These results come from controlled design environments, but the same physics models are now moving into live industrial operations, where systems are more complex and the upside far greater. Physical AI can interpret real system behaviour, forecast future states, and optimise operations continuously and autonomously across energy and infrastructure.

Cracking the Grid Problem

Energy is the sector where the gap between data potential and current tools is most pronounced. Grids and plants now face physics-driven complexity that rules-based systems and human operators can't manage alone. Delivering real operational impact requires models trained on true system physics, supported by high-rate sensing, unified telemetry and simulation pipelines, and GPU/HPC compute. The scale of the opportunity is material: under widespread adoption, AI-enabled optimisation in power plant operations and maintenance alone could deliver up to USD 110 billion per year in global cost savings by 2035, while reducing forced outages, extending asset lifetimes, and improving system efficiency across increasingly volatile grids (IEA).

Adoption, however, is still in its early stages. Across core functions, AI adoption today remains uneven and largely task-specific. Adoption is stronger in asset operations and maintenance use cases, such as load forecasting, fault detection, image recognition, and predictive maintenance, where outputs tend to be advisory and decisions are easier to reverse. By contrast, adoption remains significantly lower in functions that shape system behaviour over longer horizons, including maintenance planning, activity scheduling, system expansion studies, and autonomous field operations, where decisions are system-wide and long-lived. This pattern suggests that the primary constraint is no longer technical feasibility, but the absence of shared, system-level infrastructure needed to support broader operational trust.

Validating the Thesis: Live Pilots & Use Cases

Selected pilots and deployments illustrate early proof points for Physical AI in production environments: AI surrogates that improve heat-transfer and cooling design upfront for battery systems, turbines and high-load industrial components; a unified data foundation giving real-time insight into asset health for power transmission and grid operations; edge orchestration enabling zero-touch deployment and remote management across distributed substations and renewable assets; AI copilots that standardise decades of engineering drawings and accelerate network-control workflows for grid engineering; AI analytics platforms that integrate factory data to boost throughput across industrial manufacturing; and energy-centred predictive maintenance that detects pump faults early in water utilities and critical infrastructure.

Market Scan

We have mapped the companies building Physical AI across the digital layer. Real defensibility is emerging in the centre of the stack, where simulation and domain specific AI models fuse into systems that learn real physics. This layer demands deep engineering knowledge, and heavy compute, which gives early leaders a compounding advantage.

The launch of Cosmos in January 2025 by NVIDIA, a platform for World Foundation Models, reached 5 million users within its first 12 months (as of January 2026). The market is being shaped now, and companies are taking positions across infrastructure, digital twins, AI models, and applications and agents.

Within energy specifically, the most value-accretive layers of the Physical AI stack combine physics-aware models and simulation with clear paths to deployment across core use cases: enterprise GenAI platforms paired with domain-specific model libraries for utilities; AI simulation models that replace or accelerate traditional physics solvers for specific engineering domains; AI acceleration layers that speed up certified, auditable simulation solvers rather than replacing them; cloud-native multiphysics simulation platforms for parallel design exploration; cloud/HPC operating systems that let engineers run existing trusted tools at scale; foundation models for energy operations that diagnose anomalies and recommend actions across upstream, downstream and renewables; full-stack operations AI for oil and gas combining software with managed drone data capture; and AI-powered substation automation that upgrades legacy substations without full hardware replacement.