Physical AI: How Artificial Intelligence Is Moving Beyond Digital Applications into the Real World

As concerns over rising artificial intelligence (AI) investments and their commercial returns continue to affect the technology sector, companies are increasingly exploring ways to apply AI to real-world industrial operations. One such development is the recent agreement between Abu Dhabi's state-owned oil company ADNOC and AI firms G42 and OpenAI to deploy advanced AI capabilities across its energy operations.

Announced at the ADIPEC conference in Abu Dhabi, the partnership aims to use AI to improve operational efficiency and address complex engineering challenges in the oil and gas sector. OpenAI CEO Sam Altman described the agreement as an opportunity for the AI industry to contribute to the energy sector.

The development also highlights an emerging technological trend known as 'physical AI', which involves deploying artificial intelligence in systems that interact with and operate in the physical world rather than functioning exclusively through digital platforms.

What Is Physical AI and How Does It Work?

Physical AI refers to the integration of artificial intelligence with machines, sensors, actuators and control systems to enable autonomous operations in real-world environments. It allows machines such as robots, self-driving vehicles and automated industrial systems to perceive their surroundings, understand situations and perform tasks independently.

Unlike conventional AI applications such as chatbots, image-generation tools and coding assistants, physical AI operates in physical spaces. Traditional industrial robots generally follow pre-programmed instructions and perform repetitive tasks in controlled environments. Physical AI-powered robots, however, can adapt their actions based on changing surroundings and new information.

These systems combine large language models (LLMs), reinforcement learning and multimodal data, including images, videos, speech and sensor inputs. This enables machines to develop a better understanding of their environment and improve their performance through experience.

Nvidia CEO Jensen Huang has described physical AI as the next major phase of technological development, predicting widespread adoption of robotics and automated systems. He has suggested that the emergence of large-scale robotic ecosystems could transform industries and create a new global economy.

Expanding Applications and the Importance of Real-World Data

Physical AI has applications beyond individual robots, including automated factories, smart energy grids, logistics networks and fleets of autonomous vehicles.

However, its successful deployment depends heavily on access to real-world data. Synthetic data alone may not adequately prepare machines for complex environments. Robots require actual operational information collected through multiple sensors to understand physical conditions and respond effectively.

As industries increasingly adopt autonomous systems, physical AI could reshape manufacturing, energy, transportation and other sectors by improving efficiency, adaptability and industrial decision-making.