AI Future Forged: Robots & EY.ai 🤖🚀

Tech & Science

AI is increasingly impacting the physical world, and Ernst & Young (EY) is establishing a structured framework to help companies effectively collaborate with robots, drones, and other smart devices. To this end, EY is introducing a dedicated physical AI platform, developed with NVIDIA tools, and establishing a new EY.ai Lab in Georgia. The platform leverages NVIDIA Omniverse libraries, NVIDIA Isaac, and NVIDIA AI Enterprise software, providing organizations with a clearer approach to planning, testing, and managing AI systems operating in real-world environments, ranging from factory robots to drones and edge devices.

Specifically, the NVIDIA Omniverse libraries facilitate the creation of digital twins, allowing firms to model and test systems before deployment. NVIDIA Isaac tools offer open models and simulation frameworks for designing and validating AI-driven robots within detailed 3D settings. NVIDIA AI Enterprise provides the necessary computing infrastructure to handle heavier AI workloads.

EY’s platform is structured around three core areas: AI-ready data—including synthetic data to replicate a diverse range of physical scenarios; digital twins and robotics training—which provides tools to connect digital and physical systems, monitor performance in real-time, and ensure operational continuity; and responsible physical AI—incorporating governance and controls to address safety, ethical considerations, and compliance requirements. The platform is designed to support organizations across sectors such as industrial, energy, consumer, and health, from initial planning through long-term maintenance. Raj Sharma, EY Global Managing Partner – Growth & Innovation, is leading this initiative.

Innovation experts believe physical AI is already transforming business operations across various sectors, enabling companies to create greater value through increased automation and reduced operating costs. Combining EY’s deep industry expertise with NVIDIA’s robust infrastructure, the collaboration is anticipated to accelerate the transition for companies from initial experimentation to large-scale, enterprise deployments. NVIDIA’s John Fanelli notes a growing trend of enterprises integrating robots and automation into real-world settings, driven by workforce changes and a desire to improve safety. The EY.ai Lab, backed by NVIDIA AI infrastructure, is specifically designed to “simulate, optimize, and safely deploy robotics applications at enterprise scale,” representing the next evolution of industrial AI.

Furthermore, EY has appointed Dr. Youngjun Choi as its Global Physical AI Leader, where he will direct robotics and physical AI initiatives and help establish EY’s role as an advisor in this field. Dr. Choi brings nearly 20 years of experience in robotics and AI, previously leading the UPS Robotics AI Lab, where he spearheaded digital twin development, robotics projects, and AI tools aimed at modernizing the company’s network. Prior to that, he held a research faculty position in Aerospace Engineering at the Georgia Institute of Technology, contributing to advancements in aerial robotics and autonomous systems. A key component of his responsibilities is the direction of the newly established EY.ai Lab in Alpharetta, Georgia – the first EY location specifically dedicated to physical AI, which includes robotics systems and sensors.

Simulation tools are now available to organizations seeking to test ideas and develop prototypes before widespread deployment. According to Joe Depa, EY Global Chief Innovation Officer, his clients are prioritizing improved methods for utilizing technology to enhance decision-making and overall performance. He emphasizes the necessity of robust data foundations and established trust when implementing physical AI. Under Choi’s leadership of the Lab, EY teams are beginning to explore possibilities beyond superficial applications, establishing a foundation for scalable operations. Within the Lab, organizations can design and test physical AI systems within a virtual testbed, develop solutions for humanoids, quadrupeds, and other next-generation robots, and leverage digital twins to improve logistics, manufacturing, and maintenance processes.