Editor’s Note: This is Part 2 of a four-part series on how AI and open-source robotics are changing industrial manufacturing in 2026. Today’s post provides a Guide to Physical AI for Industrial Manufacturers.
A new digital toolkit on the production floor is giving industrial manufacturers unprecedented control over how their robots see, reason, and react.
The collection of digital tools allows different brands of hardware and software to work together, plugging into an open-source ecosystem of shared resources and using AI to make production faster and more precise.
Together, the tools create powerful new Physical AI technology expected to drive robotics on industrial manufacturing floors over the next two years.
As many as 87 percent of 1,500 manufacturing leaders across 12 countries surveyed for the Cisco 2026 State of Industrial AI Report released in March say they expect open-source robotics to define their operational success over the next 24 months.
“We’re entering a period of rapid advancement in the industrial manufacturing sector,” said Brad Tompkins, President of Automations Solutions, Inc. (ASI). “This is not the time you want to fall behind.”
What Industrial Manufacturers Need to Know: A Practical Guide to Physical AI
The good news? You don’t need a degree in robotics to understand how Physical AI works on production lines.
Here, ASI breaks down the technologies driving industrial manufacturing’s shift to Physical AI and highlights what you actually need to know.
What is Physical AI in manufacturing?
Physical AI is the term for artificial intelligence systems that allow an industrial robot to use its sensors and control systems to “see, think, and react” to the real world in real-time.
It’s much more advanced than digital AI (like Chat GPT), because it brings a physical layer to the process. Physical AI allows a robot to perceive what is happening around it and adjust its behavior on the fly.
Why it matters: A context-aware robot can handle tasks like sorting mixed parts, even by color, or reacting to a person nearby without having to stop production for a manual reset.
How does Physical AI power industrial robots?
Artificial intelligence requires massive amounts of data and incredible speed to work. Adding the physical component for robots requires an additional set of specific “tools” (or technologies).
The “Big 3” technologies needed for Physical AI are:
- ROS 2: An interface connection to the ROS open-source robot operating system. ROS 2 is a global framework of shared software libraries, schematics, blueprints and other resources. It’s called “open source” because it is available to anyone, anywhere, for free.
- Python: A widely used, general purpose “language” for AI development.
- Stream Motion: An interface that allows external applications to send real-time, high-speed motion commands to a robot.
Together, these three technologies provide the high-speed “infrastructure” that AI needs to take the wheel and operate industrial robots.
Why it matters: These three technologies allow a production line to respond to AI commands, and handle variations in parts or environment without human intervention.
Are these Physical AI technologies new?
No. The technology itself is not new. The foundations for these tools have existed for years. What’s new is that the technology is moving from lab to live production, as robot manufacturers bring to market the first robots specifically designed for Physical AI compatibility.
Why is everyone talking about the Fanuc ROS 2 driver?
In late 2025, Fanuc became the first major robot manufacturer to build into its industrial robots a software interface that supports ROS 2 when it rolled out its new ROS 2 driver specifically for the CRX Collaborative Robot Series.
While third-party drivers have existed for years, this was the first time a major manufacturer provided a high-speed interface with full technical support. This means Fanuc’s robots can now “talk” easily to advanced AI platforms.
Why it matters: Fanuc’s move is widely believed to signal the new industry standard, with others following. The future of manufacturing may be less about proprietary software than how easily the hardware can plug into the global AI ecosystem.
Why is ROS 2 important for industrial robots?
ROS 2 (Robot Operating System) is an open-source project that allows different brands of robots, sensors, and cameras to talk to each other using one common language. No single manufacturer owns it, which means the community – and the users – drive the innovation.
Why it matters: It ends “brand lock-in,” and gives industrial manufacturers the freedom to mix and match the best hardware for specific needs without expensive, time-consuming integration. Automation projects can scale faster.
Why does Physical AI use Python programming language?
Python removes language barriers between brands and manufacturers. Most AI development happens in Python and many robot controllers now support this language natively. Using Python allows instant connectivity and cross-brand communication, so that different brands of hardware and software can share data without the need for custom-coded translations. Python instantly translates the AI’s “thinking” to the robot’s “doing.”
Why it matters: New tools or peripherals can be integrated into an existing production line quickly and smoothly, making automation more flexible and much easier to scale.
What is Stream Motion in robotics?
Stream Motion is a high-speed interface that allows AI to send new motion commands to a robot every 1 millisecond (1,000 times per second), significantly faster than previously seen and necessary for AI control.
Why it matters: This speed provides the “reflexes” required for safety. If an object moves or a person enters the workspace, the AI can adjust the robot’s path instantly.
How quickly are manufacturers adopting Physical AI?
The industrial world is moving toward a Physical AI standard more quickly than many predicted.
According to the Cisco 2026 State of Industrial AI Report released in March, 2026, 61 percent of companies surveyed in 19 countries are already using AI in their daily operations.
Perhaps most importantly, Cisco’s data reveals that 20 percent of the surveyed operators have already moved past the pilot phase of Physical AI into what they call “mature, scaled adoption.” These are leaders who have already fully integrated AI across their entire production line.
Why it matters: Waiting another year to explore these tools could mean trying to catch up to competitors.
If I buy an AI-ready robot, where do I get the AI software for it?
You don’t have to build AI from scratch or hire a team of data scientists. In 2026, you can access “factory-ready” AI through platforms like NVIDIA or specialized industrial AI providers.
The key is having a partner who knows how to “tune” that AI to your specific processes and production needs.
At ASI, we help manufacturers assess solutions and adapt to an evolving market. We can help you select the right AI “brain,” integrate it with your ROS-2 adapted hardware, and ensure the entire system meets your safety and throughput requirements.
Why it matters: You get the cutting-edge power of global AI with the local, hands-on support of an integrator who understands your daily production goals.
Ready to see what Physical AI can do for you?
Uncertain about what Physical AI can do for your operation? Don’t want to fall behind the competition? Our ASI engineers are standing by to help you evaluate your goals and unique production needs. We work with manufacturers to build a sound roadmap to automation success, making technology accessible and reliable.
Next in this series: How Physical AI is revolutionizing control, bringing smarter vision and voice command systems to the production floor.
Did you miss our Day 1 report? Next-Gen Robotics: Opening the ‘Black Box’ of Manufacturing
