Industry
Manufacturing
Future-Ready Manufacturing: Partnering in your journey to a sustainable manufacturing enterprise
Highlights
- Physical artificial intelligence (AI) is a leap from hardware-centric manufacturing to more software-defined manufacturing and autonomous operations.
- It enables physical systems to become autonomous by perceiving, sensing the world around, planning tasks based on insights, and executing actions in the physical world.
- It is possible to bring human intelligence, tacit knowledge, and reflexes to machines for autonomous physical actions with minimal human interventions through physical AI.
- When combined with generative AI (GenAI) and agentic AI, physical AI can offer actionable insights and execute autonomously to optimize processes and operations.
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Physical AI: A game changer
Imagine machines acting autonomously or traditional robots finetuning actions based on changes in the physical environment without coding or re-programming.
Physical artificial intelligence (AI) is bringing a new rush of autonomous and self-learning ecosystems that merge traditional AI into the physical environment. Its interactive systems are tightly connected with robotics, enabling AI-powered robots to perceive, reason, and act autonomously in their environments.
Physical AI differs from traditional AI and large language models (LLMs) in its ability to interact with the physical world and the tasks in its scope. While traditional AI and LLMs focus on data processing, data pattern analysis, and predictions based on digital inputs, physical AI interacts with the environment around it.
The evolution of physical AI
Physical AI enables autonomous machines to perceive the world around them, plan tasks based on insights, and execute these planned tasks in the physical world.
Traditionally, shopfloor machines or robots use different onboard sensors such as high-speed digital cameras, proximity sensors, and process sensors to make sense of the environment. But the key difference with physical –AI-enabled robots or machines is that they collect sensory data and take decisions autonomously, in real time. This allows them to change their actions to sync with the new environment.
Self-learning systems or robots can learn from their own historical decisions and react in a better way to future scenarios. In the future, we expect these machines or robots to rely more on deep learning, neural networks, and fuzzy logic to self-learn and finetune their actions in real time.
A physical AI ecosystem (see Figure 1) comprises business applications and shared data applications with data storage. It also contains AI agents for various purposes such as manufacturing apps and engineering design. The actions in the physical environment are enabled by self-learning algorithms, edge processing, and sensors and actuators.
ecosystem physical AI
ecosystem physical AI
Physical AI is a revolution in its own right; it is a big leap forward in deploying the latest technologies. It uses devices such as actuators and sensors that are driven by advanced AI and ML algorithms to overcome technology design challenges and create a sustainable operations ecosystem for adapting to rapidly changing environments. For example, a welding robot performing repetitive tasks can use physical AI in unknown scenarios such as a different weld angle due to complex material sizes and contours. The robot can take autonomous decisions by analyzing surges and angles in real time.
The physical AI revolution – what it can do
Physical AI represents a transformative shift by bridging the gap between digital intelligence and the physical world.
AI is no longer limited to data processing and virtual tasks such as analysis, automation, and prediction. With physical AI, manufacturers can leverage AI and GenAI to their fullest potential in the manufacturing space. The next step in the artificial intelligence domain, it offers great and unexplored possibilities for factories of the future.
We believe that with more physical AI deployments, manufacturers can extract the maximum value from existing automation systems. This will make our processes more autonomous and flexible. Decision making will be faster at the edge or machine level. Physical AI will be able to reduce risks for human workers. For instance, humanoid robots can work in deep mines and complete complex drilling tasks without risking human lives.
Physical AI can bring a number of advantages to the ecosystem across the board with autonomy at the edge. Firstly, it is expected to make machines more autonomous in their actions. Secondly, this autonomous and self-tuning nature can increase efficiencies in machines, where human-centric errors are eliminated. This will bring a third advantage, where tacit human knowledge and adaptability will be enabled in machines, making them more productive and improving their utilization. Additionally, critical machines or robot-oriented ecosystems will become more intelligent and adjustable for real-world environmental changes. Machines will learn from their experiences over time, and their performance will improve to bring efficiency to manufacturing operations.
A convergence of generative AI, agentic AI, and robotics
Physical AI is an advanced AI version that has the capability to create actionable insights and finetune actions by sensing physical environmental changes.
In manufacturing, physical AI systems will work alongside humans to perform shopfloor operations. With edge intelligence, the physical systems can process data collected using physical sensors, thereby optimizing the production processes.
Physical AI will complement and supplement the human workforce. Manufacturers will not have to solely rely on the tacit knowledge or intuition of humans, with AI analyzing historical data and rules. Physical AI can predict the situation and take the most precise approach to handle the unknown condition. With training, humanoid robots and autonomous mobile robots can use real-time feedback from the sensors to perform tasks. They can pick objects and perform material movement and dynamic route optimization.
How physical AI will impact the manufacturing industry
Physical AI is set to bring about significant changes to the manufacturing industry in the near future.
It will transform millions of factories, thousands of warehouses, and a billion pieces of mobility apparatus. Additionally, we expect billions of future humanoid robots to be targeted for physical AI deployments. We estimate that the overall AI market will grow at a CAGR of 20-30% by 2030. Looking at the overall potential, we believe physical AI will witness a CAGR of 30-35% in the manufacturing industry alone.
With the advent of physical AI, particularly in robotics and automation, we are on the edge of a future factories revolution where robots and machines are not just seen as means to carry out routine tasks.
Physical AI emphasizes machines working and finetuning autonomously along with replicating human interactions with the physical world. It will enable decision making at the edge, where machines and their environment will work in tandem to perform operations.
In the future, physical AI will enable greater collaboration between humans and machines. By processing data at the edge – at the device or machine level – it will minimize latency and efficiently make real-time decisions. The future of AI is now not only restricted to the digital world but integrates the physical world as well.
