It’s no secret that artificial intelligence (AI) has already had a significant impact on businesses — introducing new levels of automation and challenges for leaders to overcome. Until now, it’s been largely confined to screens and data centers, but we are witnessing this technology advance beyond the digital world right before our eyes.
In manufacturing, sensors and AI-driven analytics allow factories to anticipate maintenance before breakdowns occur, and in healthcare, smart diagnostic systems accelerate detection and personalize treatment. Even in global supply chains, intelligent networks are improving efficiency, reducing waste and advancing sustainability. The result isn’t just incremental improvement but the creation of safer workplaces, more reliable products and stronger customer trust through consistently better outcomes.
“Physical AI” represents the next frontier, transforming industries by embedding intelligence directly into the systems powering our daily lives. Examples include robots in hospitals, autonomous fleets or AI-driven factories. This new era not only unlocks a wealth of unprecedented possibilities for businesses but also comes with new complications that the C-suite needs to prepare for. Further, successful implementation demands that business structures adapt.
A call to action for leaders
Rapid advances in robotics, combined with the sizable potential of these technologies, are positioning physical AI as a critical development in the AI revolution. For executives, though, the challenge is moving from pilots to deploying physical AI at scale so that it becomes a driver of sustainable growth for their organization.
Piloting physical AI involves identifying the workflows where embedded intelligence can drive immediate gains — whether that’s streamlining supply chains, enhancing workforce productivity or enabling entirely new services. Scaling is a tougher ask because it involves substantial investment in infrastructure, data collection and management, and workforce transformation to build on the outcomes of a successful pilot.
Without a clear strategy, even the most promising physical AI deployment may stall or fail to realize its potential. For that reason, EY teams have rolled out several internal physical AI projects, in collaboration with NVIDIA, to navigate the risks and develop a blueprint for success.
Building a strong data framework
Just like other AI systems, physical AI tools need access to high-quality, secure and accessible data. Without it, a physical AI system is incapable of performing well. Businesses must have appropriate data for the system to use, supported by cybersecurity and governance processes that protect the integrity and quality of that data.
AI-ready data is the foundation for deploying physical AI at scale — so leaders should ensure that data is high-quality, based on the proper context, formatted correctly and well-governed. When this foundation is in place, physical AI can perform tasks more safely and effectively, allowing businesses to capitalize on the benefits of using the technologies while eliminating associated risks.
Navigating increasing complexities
The transition from the digital world to the physical world comes with new rules and regulations for businesses that adopt AI technologies. What’s more, adhering to these rules is not a trivial undertaking. Compliance can’t be overlooked.
As leaders prepare to integrate robots into their processes, they must consider human privacy rights. Also, what do safety and security procedures look like in this new environment? And are there additional insurance requirements that need to be in place before the robots can become operational? If neglected, each of these considerations has the power to unravel a physical AI deployment. That’s why a comprehensive risk management strategy is critical to success.
The good news is that businesses don’t need to develop this strategy on their own. By working with partners who are well versed in change management, leaders can tap into additional resources and gain the knowledge they need to successfully navigate the new challenges posed by large-scale adoption of physical AI.
Enhancing your people’s capabilities
Integrating AI into business operations has already sparked a talent challenge, with both the current and the future workforce being tasked with learning how to use the technology effectively. Adding a physical element to the AI equation further deepens this challenge, since it requires the workforce to develop additional skills. It also raises difficult questions around how roles might need to adapt and change.
The truth is, the real value of physical AI technology is its ability to enhance human capabilities. When leaders give their teams the knowledge and resources to understand physical AI, they enable them to collaborate directly with the technology and better execute intricate tasks. So, it is essential to develop and update training modules so that people can safely and effectively add these tools to their toolbox, opening the door to the continued exploration of emerging technologies.
As physical AI applications are increasingly deployed at scale, humans must remain in the loop. A certain level of trust is needed for the operation of heavy machinery or even medical diagnostics — one that can only come from the responsible oversight and governance provided by a human.
Setting the pace of innovation
Physical AI is more than just the next stage of automation; it represents a strategic shift in how companies create value. Streamlining tasks and creating efficiency through the integration of robotics and AI can bring huge benefits to businesses. Hence organizations that act now won’t just adapt to the future of work — they will define it.
The blueprint for success is clear: identify priority use cases, test and learn quickly, and build governance frameworks that balance innovation with accountability. For C-suites ready to lead the charge, physical AI offers not just efficiency, but the chance to set the pace of global innovation and shape the future for the better.
The views reflected in this article are the views of the author and do not necessarily reflect the views of the global EY organization or its member firms.
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