AI is moving deeper into the physical world, and EY is building a more structured way for companies to work with robots, drones, and other smart devices. The organization is deploying a physical AI platform built on NVIDIA tools, opening a new EY.ai lab in Georgia, and adding new leaders to guide its work in this space.
This platform uses the NVIDIA Omniverse library, NVIDIA Isaac, and NVIDIA AI Enterprise software. EY says this setup will provide organizations with a clearer way to plan, test and manage AI systems operating in real-world environments, from factory robots to drones and edge devices.
The Omniverse library supports the creation of digital twins, allowing businesses to model and test systems before deployment. NVIDIA Isaac tools provide open models and simulation frameworks for designing and validating AI-powered robots in detailed 3D settings. NVIDIA AI Enterprise provides the compute base you need to run heavier AI workloads.
EY explains that the platform is built around three key areas.
- AI-enabled data: Synthetic data that mirrors a wide range of physics scenarios.
- Digital twin and robot training: Tools that connect digital and physical systems, monitor performance in real time, and support operational continuity.
- Responsible physics AI: Governance and management that addresses safety, ethics, and compliance.
The platform aims to support everything from initial planning to long-term maintenance in sectors such as industrial, energy, consumer, and health.
Raj Sharma, EY’s global managing partner for growth and innovation, said physical AI is already “transforming the way companies across sectors operate and contributing to value creation” and could bring further automation and help reduce operating costs. He said the combination of EY’s industry experience and NVIDIA’s infrastructure is expected to accelerate the speed at which companies move “from experimentation to enterprise-scale deployment.”
NVIDIA’s John Fanelli notes that more and more companies are deploying robots and automation in their environments to adapt to a changing workforce and improve safety. He said the EY.ai Lab, supported by NVIDIA AI infrastructure, helps organizations “simulate, optimize, and securely deploy robotics applications at enterprise scale,” and sees this as part of the next phase of industrial AI.
New leadership and dedicated physics AI lab
EY also appointed Dr. Youngjun Choi as Global Physical AI Leader. He will oversee robotics and physical AI efforts and help shape EY’s role as an advisor in this area.
With nearly 20 years of experience in robotics and AI, Choi previously led the UPS Robotics AI Lab, working on digital twins, robotics projects, and AI tools to modernize networks. Previously, he was a research faculty member in aerospace engineering at the Georgia Institute of Technology, where he contributed to aeronautical robotics and autonomous systems.
A key part of his role is leading the newly opened EY.ai Lab in Alpharetta, Georgia. This is the first EY site focused on physical AI. The lab includes robotic systems, sensors, and simulation tools so organizations can test ideas and build prototypes before deploying at scale.
Joe Depa, EY Global Chief Innovation Officer, says customers are demanding better ways to use technology for decision-making and performance. He added that physical AI requires a strong data foundation and trust from the beginning. With Choi leading the lab, the EY team is beginning to “beyond the surface of what is possible” and laying the foundation for a scalable operation, Depa said.
In the lab, organizations can:
- Design and test physical AI systems in virtual testbeds.
- Build solutions for humanoids, quadrupeds, and other next-generation robots.
- Improve logistics, manufacturing, and maintenance with digital twins.
The new platform and labs build on previous collaborations between EY and NVIDIA, including the AI agent platform launched earlier this year. Both organizations plan to expand their physical AI efforts into areas such as energy, health, and smart cities. We also aim to support automation projects that reduce waste and reduce environmental impact.
See also: Microsoft, NVIDIA, and Anthropic forge AI Computing Alliance
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