Industrial applications of AI have one fundamental challenge that does not exist for digital applications: namely, that the model must understand a changing physical environment.
A good architecture will tie together observations of the physical world, AI reasoning, operating constraints and – if applicable – physical execution.
One possible way to organize such an architecture is as follows:
Identify → Sense → Decide → Act
1. Identify
Before the AI can make sense of an event, it needs context.
Identification establishes:
Asset identity
Location
Movement
Traceability
Relationships between people, equipment, material and processes
Enabling technologies can include RFID, BLE, UWB, GPS/GNSS, RTLS – or any technology that contributes to identification.
The system will not be able to effectively evaluate the meaning of an observation without being able to identify what it is observing.
2. Sense
The next step is to capture the condition of the physical world.
Depending on the application, relevant observations can include:
Temperature
Pressure
Vibration
Humidity
Electrical characteristics
Flow
Level
Speed
Position
Structural characteristics
The sensing layer enables the transformation from physical observations to machine-readable format.
3. Decide
What does the AI do with its observations?
A decision layer takes identification, sensing, history, enterprise information and operating constraints and transforms them into:
Anomaly detection
Forecasting
Diagnosis
Risk prioritization
Optimization
Recommendations
Operational decisions
The critical insight is that decisions are best made inside the context of constraints rather than as an end in themselves.
4. Act
Physical AI is about bringing AI models into the physical world.
An authorized decision can be acted upon by:
An operator
A work order
A procedure
Industrial equipment
Controllers
Robotics
Connected systems
Note that an AI architecture does not require autonomous action – human authorization can always be part of the process.
5. Verify
The pipeline is not complete until we bring it back to the physical world.
New observations and status information can confirm whether a desired physical effect has been achieved.
This creates a loop:
Observe → Interpret → Decide → Execute → Verify
Verification is critically important, as the physical world tends to be less cooperative than a well-structured dataset.
Why Architecture Matters
Industrial AI is not simply about choosing an AI technique.
A production-level system will account for data quality, device identity, connectivity, latency, cybersecurity, authorization, operating constraints, human elements, auditability and fail-safety – among other considerations.
Aperture Venture Studio’s AIoT and Physical AI architecture reflects this more comprehensive view, with the first three capabilities providing the AIoT foundation and an action layer adding the elements of Physical AI.
For developers and engineers, this means that the design of the AI layer should not be decoupled from the system it is intended to support.
The effectiveness of the overall solution will depend on the interactions between sensing, context, intelligence, control and verification.
Top comments (0)