It’s no secret that artificial intelligence (AI) has already had a significant impact on business — introducing new levels of automation and challenges for leaders to overcome. Until now, it has largely been limited to screens and data centers, but we are witnessing this technology advance beyond the digital world before our eyes.
In manufacturing, AI-based sensors and analytics enable factories to anticipate maintenance before failures occur, and in healthcare, smart diagnostic systems speed detection and personalize treatment. Even in global supply chains, smart grids are improving efficiency, reducing waste and promoting sustainability.
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The result is not just incremental improvement, but the creation of safer workplaces, higher quality products and greater customer confidence through consistently better results.
“Physical AI” represents the next frontier, transforming industries by embedding intelligence directly into the systems that power our daily lives. Examples include robots in hospitals, autonomous fleets, or AI-driven factories.
This new era not only unlocks an unprecedented wealth of possibilities for companies, it also brings new complications that C-suites need to prepare for. Furthermore, successful implementation requires business structures to adapt.
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A call to leaders for action
Rapid advances in robotics, combined with the considerable potential of these technologies, are positioning physical AI as a critical development in the AI revolution.
For executives, however, the challenge is to move from pilot projects to deploying physical AI at scale so that it becomes a sustainable growth engine for their organizations.
Piloting physical AI involves identifying workflows where embedded intelligence can generate immediate gains — whether by streamlining supply chains, improving workforce productivity, or enabling entirely new services.
Scaling is a more difficult challenge, as it involves substantial investment in infrastructure, data collection and management, and workforce transformation to build on the results of a successful pilot.
Without a clear strategy, even the most promising deployment of physical AI may stagnate or fail to realize its potential. For this reason, EY teams (executive consulting company) launched several internal physical AI projects, in collaboration with Nvidia, to navigate risks and develop a plan for success.
Building a solid data structure
Just like other AI systems, physical AI tools need high-quality, secure, and accessible data. Without this, a physical AI system is unable to perform well.
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Companies 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 must ensure data is high-quality, context-based, correctly formatted, and well-governed.
When this foundation is established, physical AI can perform tasks more safely and effectively, allowing companies to capitalize on the benefits of using the technologies while eliminating the associated risks.
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Navigating increasing complexities
The transition from the digital world to the physical world comes with new rules and regulations for companies adopting AI technologies. What’s more, adhering to these rules is not a trivial task. Compliance cannot be neglected.
As leaders prepare to integrate robots into their processes, they must consider human privacy rights.
Furthermore, what are the safety and protection procedures like in this new environment? And are there additional insurance requirements that need to be in place before robots can become operational?
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If overlooked, each of these considerations has the power to undo a physical AI deployment. That’s why a comprehensive risk management strategy is critical to success.
The good news is that companies don’t need to develop this strategy alone. By working with partners who are experienced in change management, leaders can access additional resources and gain the knowledge they need to successfully navigate the new challenges posed by the large-scale adoption of physical AI.
Enhancing your team’s capabilities
The integration of AI into business operations has already challenged professionals, with the current and future workforce being tasked with learning how to use the technology effectively.
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Adding a physical element to the AI equation further deepens this challenge by requiring employees to develop additional skills. It also raises difficult questions about how roles may need to adapt and change.
The truth is that 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 empower them to collaborate directly with the technology and perform complex tasks better.
Therefore, it is essential to develop and update training modules so that people can safely and effectively add these tools to their skills, opening the doors to continued exploration of emerging technologies.
As physical AI applications are increasingly deployed at scale, humans must stay in the loop.
A certain level of trust is necessary for operating heavy machinery or even for medical diagnosis — trust that can only come from 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 the way companies create value.
Simplifying tasks and creating efficiencies through the integration of robotics and AI can bring enormous benefits to companies.
So organizations that act now won’t just adapt to the future of work — they’ll define it.
The plan for success is clear: identify priority use cases, test and learn quickly, and build governance structures 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.
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