EY announced the rollout of a new physical AI platform accelerated by NVIDIA infrastructure and software, the opening of EY.ai Lab, and a key leadership appointment Thriveholdings, enabling enterprises to simulate, test, and deploy AI-driven robotics systems before committing to full-scale production. The platform uses NVIDIA Omniverse libraries, NVIDIA Isaac, and NVIDIA AI Enterprise software to help organizations plan, test, and manage AI systems operating in real environments from factory robots to drones and edge devices OpenAI.

The EY.ai Lab in Alpharetta, Georgia represents the first among a global network of EY facilities fully dedicated to helping organizations integrate AI into physical environments Artificial Intelligence News. Equipped with leading-edge robotics, sensors, and simulation capabilities, the Lab offers organizations a rapid R&D environment to prototype, test, and deploy scalable physical AI solutions Artificial Intelligence News.

EY appointed Dr. Youngjun Choi as EY Global Physical AI Leader effective immediately, overseeing next-generation robotics workstreams and positioning EY as trusted advisor in this evolving field Thriveholdings. Choi brings nearly two decades of experience developing strategic partnerships and advancing solutions with industry executives. The platform builds on earlier EY-NVIDIA collaboration, including an AI agent platform launched earlier this year OpenAI.

AI is moving deeper into the physical world, prompting organizations to seek structured approaches for working with robots, drones, and other smart devices OpenAI. Omniverse libraries support the creation of digital twins so firms can model and test systems before deployment, while NVIDIA Isaac tools offer open models and simulation frameworks to design and validate AI-driven robots in detailed 3D settings OpenAI.

By integrating Omniverse libraries, EY will support clients with developing digital twins for modeling, testing, and improving physical systems before deploying them into the real world, helping reduce risk and accelerate time-to-value Thriveholdings. The platform addresses three foundational elements: generating synthetic data to simulate physical AI scenarios, leveraging NVIDIA frameworks to bridge digital and physical worlds with real-time insights, and providing secure foundations for advanced AI workloads.

This expansion reflects broader industry recognition that physical AI deployment requires systematic validation environments where financial viability and operational feasibility can be comprehensively assessed before capital-intensive production rollouts.

Raj Sharma, EY Global Managing Partner for Growth and Innovation, stated that physical AI is already transforming how businesses operate and create value, bringing automation while lowering operating costs, and that combining EY's industry experience with NVIDIA's infrastructure is expected to speed how companies move from experimentation to enterprise-scale deployment OpenAI.

John Fanelli from NVIDIA noted that enterprises are bringing robotics and automation into real settings to adapt to shifting demographics and boost safety for people working in factories and industrial facilities, with the EY.ai Lab helping organizations simulate, optimize, and safely deploy robotics applications at enterprise scale, accelerating the next phase of the AI industrial revolution Artificial Intelligence News.

Joe Depa, EY Global Chief Innovation Officer, emphasized that clients want better ways to use technology for decision-making and performance, and that physical AI requires strong data foundations and trust from the start OpenAI.

The Lab allows organizations to design and simulate physical AI systems in virtual testbeds to validate financial viability and operational feasibility through comprehensive what-if simulations, develop solutions across diverse form factors including humanoids and quadrupeds, and improve logistics, manufacturing, and maintenance workflows through digital twins Artificial Intelligence News.

For manufacturing, energy, logistics, and industrial operations executives, this platform provides critical de-risking mechanisms before committing capital to physical automation infrastructure. The combination addresses client demands for more structured deployment methodologies that establish strong data foundations and build stakeholder trust before operational rollout OpenAI. Organizations can now systematically evaluate robotics investments against operational requirements, reducing implementation failures that have historically plagued early-stage physical AI deployments while establishing baseline performance metrics before scaling across enterprise facilities.

Decision-makers should monitor whether digital twin validation methodologies demonstrably reduce physical AI deployment failures and accelerate return-on-investment timelines compared to traditional pilot programs. The platform's focus on simulation before deployment addresses the critical gap between theoretical robotics capabilities and practical operational constraints OpenAI. Success metrics will likely center on whether organizations using structured testing environments achieve faster scaling, lower integration costs, and higher reliability than competitors deploying physical AI through conventional approaches. The Lab's research outputs may establish industry standards for robotics validation protocols across manufacturing and industrial sectors.

Source & Date

Source: Artificial Intelligence News, EY Global, PR Newswire, The AI Insider
Date: December 3, 2025