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.
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.
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.
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.
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.
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.
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