As the AI conversation moves beyond software demos toward real-world deployment, EY is drawing a clear line: the next phase of enterprise AI will be physical. With the rollout of a new physical AI platform built on NVIDIA’s infrastructure and software, alongside the opening of its dedicated EY.ai Lab, the firm is signaling a strategic shift- positioning robotics, digital twins and edge automation as emerging boardroom priorities rather than experimental side projects.
The platform combines NVIDIA Omniverse libraries for digital twin simulation, NVIDIA Isaac tools for robotics development and validation, and NVIDIA AI Enterprise for secure, scalable AI operations. The goal is straightforward: allow organizations to design, test and refine robotic and physical AI systems virtually before deploying them into complex, high-risk environments. EY positions the platform as an end-to-end toolkit, spanning strategy, safety governance, design, implementation and maintenance, targeting industrials, energy, consumer sectors and healthcare.
At the core of the platform are three priorities. First, the use of synthetic data to train physical AI systems across scenarios that are too costly or dangerous to recreate in real life. Second, tight integration of digital twins and simulation tools to bridge physical operations with continuous performance modeling. And third, what EY describes as “responsible physical AI,” focused on safety compliance, operational resilience and ethical deployment safeguards.
To anchor this expansion, EY has appointed Dr. Youngjun Choi as its Global Physical AI Leader. Formerly head of the UPS Robotics AI Lab and a research faculty member at Georgia Tech specializing in aerial robotics and autonomous systems, Choi will oversee EY’s growing robotics and physical AI workstream. He will also lead the newly opened EY.ai Lab in Alpharetta, Georgia, the firm’s first site dedicated fully to physical AI. The lab is equipped to simulate and prototype humanoid and quadruped robots, build digital twins for manufacturing and logistics workflows, and conduct what-if modeling to evaluate financial and operational feasibility.
For EY, the strategy reflects a recognition that enterprise demand for automation is being driven not by novelty, but by structural pressures, labor shortages, safety requirements, costs and the need to optimize complex industrial systems. NVIDIA echoes that view, framing physical AI as a practical response to shifting workforce demographics and risk reduction on factory floors and infrastructure sites.
What sets this move apart is the emphasis on scale and governance. EY is positioning physical AI as a production-grade discipline, grounded in validated simulation environments and governed from the outset by safety and compliance frameworks.
This initiative builds on the existing EY-NVIDIA collaboration, including the agentic AI platform launched earlier this year, and points to further expansion into energy systems, healthcare environments and smart-city development, with sustainability positioned as a parallel benefit through reduced waste and operational efficiency.
