Physical AI—artificial intelligence embodied in machines that sense and act in the real world—is drawing significant investment as manufacturers race to automate the factory floor. Much of the attention has centered on humanoid robots and advanced robotic arms, which many see as the future of manufacturing. But this view overlooks a critical application for AI: generating the control logic that every automated machine on a plant floor runs on. Today, that critical code, which decides what happens, in what order and under what conditions, is almost always written by hand.
Massimiliano Moruzzi, CEO of the physics-based AI firm Xaba, estimates that about 80% of automation cost comes from manually developing controller logic, and that manual work carries a steep price tag. By Moruzzi’s estimate, programming and deploying industrial robots costs the industry $7 billion a year.
That cost pressure is one of the many reasons why manufacturers are increasingly turning to AI to improve this process. In many cases, physics-based AI is the answer. Instead of just encoding a decision, physics-based AI learns from the process itself and adapts as conditions change. This shift does more than cut engineering costs and speed up deployment. It makes the plant floor responsive and instantly turns intelligence into execution.
Understanding Physics-Based AI
Physics-based AI is fundamentally different from both the AI chatbots most people are familiar with and the physical AI now being implemented on factory floors. Rather than learning from language, it learns the physics of a process from real signals—force, vibration, voltage, RPM and load—and uses them to generate executable control logic, including PLC code. As conditions on the floor shift, it keeps tuning that logic in response.
Physical AI and physics-based AI are related but distinct. Physical AI describes where the intelligence operates, embodied in robots and machines that sense and act in the world. Physics-based AI describes how the intelligence is built, learning from the physical laws that govern a process, rather than from language or generic data patterns.
For example, with physics-based AI, a production line can automatically slow down or adjust its movements to keep parts within spec, or a machine can detect that a cutting tool is wearing down and adjust its settings in real time, before that worn tool starts producing scrap.
Physics-based AI draws on more process data than one person could accumulate over an entire career, and it keeps refining that logic long after the machine has been commissioned. Because that learning happens automatically instead of through manual coding, manufacturers get adaptive performance without the months of engineering time, accelerating ROI and freeing engineers to focus on higher-value work.
Implementing Physics-Based AI
Implementing physics-based AI doesn’t require tearing out existing machines. Instead, they can be retrofit. A 15-year-old machine can be taught to be adaptive.
That teaching starts with the signals the machine already produces: force, torque, vibration, current, temperature, load and more. Those signals then get ingested into the model. However, on their own they don’t mean anything. The model needs context to know what’s normal, what’s a failing bearing and what “good” looks like. That context comes from two places: engineering knowledge—design intent, part geometry and tolerances, material specs and the “why” behind the numbers—and operations knowledge—quality limits, standard procedures, run history and the judgment of the people who know why a step exists and when a reading can be trusted.
With that context in place, the model grounds what it learns in physics: how the machine actually moves, wears and responds, rather than just patterns in data. That’s what allows it to learn from far less data than a purely data-driven system, and why it generates control logic accurate enough to trust.
That logic is then validated in simulation and deployed onto the PLC, PAC or SCADA/HMI layer already on the floor.
The result is adaptive performance without the capital expense of new equipment.
Integrating Physics-Based AI into Workflows
To work well, physics-based AI requires a new division of labor between humans and machines. That means manufacturers must clearly define which decisions the system handles, which stay with the engineer and where control passes from one to the other.
In an ideal state, engineers refocus on high-value work that requires judgment and expertise like process design, edge cases and decision-making. Meanwhile, AI delivers speed, completing coding and tuning in a fraction of the time.
In this division of labor, the expertise doesn’t leave the building; it concentrates where it has the greatest impact on outcomes.
Where Insight Meets Execution
Physics-based AI allows the controls layer to become dynamic, so intelligence can finally meet execution.
That matters because intelligence only has value if machines can act on it. Physics-based AI closes that gap. It turns insight into executable logic in near real time, so the plant floor doesn’t just know what to do—it does it, continuously, as conditions shift. For manufacturers, that translates directly into results: increased scalability, agility and improved quality.
Learn how you can start implementing physics-based AI in your facility. Contact us today.
