Is physical AI the same as robotics?

No. Robotics is one of the ways physical AI acts on the world, but a robot with fixed trajectories neither perceives nor reasons. Physical AI requires the full cycle: perceiving with sensors or cameras, reasoning over data with context and acting. In a factory that cycle starts with the data from the machines that already exist.

What does a plant need before considering physical AI?

The three previous levels of the ladder: connected equipment speaking a common language, with data in a single place with time and context; real-time indicators such as OEE, energy, stoppages and quality; and rules or workflows that already act under supervision. Without that comparable history, autonomous perception has nothing to reason on.

Which levels of the ladder does Captia work on?

On levels 1 to 3: connecting equipment with protocols, edge and normalisation; making the plant know what is happening with real-time analysis and alerts; and closing the loop with rules, workflows and work orders executed under supervision. Level 4, with robots, vision and autonomous agents, describes where the sector is heading.

How do I know which level my factory is on?

With one question per level. Can each machine’s data be seen outside that machine? If not, level 0. Does it reach a single place with time and context? Level 1. Are OEE and energy visible per line in real time? Level 2. Are there rules or work orders that execute on their own under supervision? Level 3. A data-source diagnosis confirms it in a few days.

What is Physical AI: definition and uses

Physical AI does not start with the robot. It starts with the data from the machine you already have. This entry explains what the term covers, what it is not, how it is ordered in five levels and why the first step in a real factory is capturing good data from existing equipment.

What physical AI is

The term describes artificial intelligence systems that perceive their environment through sensors or cameras, reason about what they perceive and act on the physical world. The NVIDIA glossary defines it as the AI that lets autonomous systems such as cameras, robots or self-driving vehicles perceive, understand and reason about the real world. In an industrial plant that perceive, reason and act cycle does not appear all at once: it is built in layers. Perceiving requires machine data to exist, reach a single place and be comparable. Reasoning requires history and context. Acting requires rules, supervision and traceability. So before talking about robots it is worth looking at what the plant can do today with its data.

What it is not

It is not a synonym for robotics, humanoids or a factory without people. A robotic arm programmed with fixed trajectories is not physical AI: it executes but neither perceives nor reasons. A camera that records without deciding anything is not either. And a language model that answers questions about the plant does not act on the physical world. Nor is it something bought as a closed product: the cases presented today as physical AI in the sector, such as the Siemens factory in Erlangen, which the company itself describes as built on the NVIDIA physical AI stack (Siemens press release, 16 April 2026), rest on years of connected, normalised process data with history. That is the part that usually stays out of the headline.

The Physical AI Ladder, in five levels

To order the conversation, Captia uses its own framework: the Physical AI Ladder. It places a plant by what it can do with its data, not by the technology it buys.

Level 0, Isolated. Data lives in each machine. It is read on a screen or on paper and there is no comparable history. Diagnosis is the entry point.
Level 1, Connected. Equipment speaks a common language and data reaches a single place with time and context. This is the work of protocols, edge and normalisation.
Level 2, Aware. The plant knows what is happening: OEE, energy, stoppages and quality in real time, per line. Analysis and alerts on the history.
Level 3, Acting. The system decides and executes under supervision: rules, workflows, energy setpoints, maintenance work orders. This is the closed loop.
Level 4, Physical. Perceiving, reasoning and acting autonomously on the real world: robots, vision, agents. It is where the sector is heading and it is only possible with levels 1 to 3 solved.

Captia works on levels 1 to 3. Level 4 describes the sector horizon, not a capability of its own. The full ladder, with examples per level and how to tell which one a plant is on, is developed in the guide industrial physical AI.

Why it starts with data

Because most plants are on the first rungs. According to the third barometer of industrial digitalisation and automation in Spain, presented at Advanced Factories 2026 and reported by Metalindustria on 22 May 2026, only 3.3% of companies say they have fully digitalised smart factories. The picture for artificial intelligence use is similar: the Spanish statistics office INE published on 22 October 2025 that 21.1% of companies with 10 or more employees use AI, and the Cotec Foundation analysis of the same date puts industry at 17.5%. On that base, investing in autonomous perception before having connected, comparable data is building the roof without foundations. The sensible path is the opposite: first data acquisition and connectivity, then real-time indicators, then rules that act under supervision, and only then autonomous perception. Each rung produces value on its own and prepares the next one.

Related terms

The technical base of level 1 is OT/IT convergence and edge computing; when the model runs on that same edge we talk about Edge AI. Level 2 is measured with indicators such as OEE. Level 3 is where industrial AI agents appear, with human-in-the-loop as the guardrail. The architecture that supports the three levels is described in the industrial data platform.

What is Physical AI