Industrial AI systems have a subtly different engineering problem than many software-centric AI applications.
The system has to reason about assets, equipment, processes, locations, and measurements as real-world objects with constraints and physical outcomes
One helpful way to think about the overall architecture is as a cycle:
IDENTIFY
↓
SENSE
↓
DECIDE
↓
ACT
↓
VERIFY / FEEDBACK
↺
1. Identify
Before an AI can reason about anything, it needs to have a context
What asset are we talking about? Where is it? What process does it belong to? What person, vehicle, material, work order, or production event is associated?
There are many ways to identify physical objects, including RFID, BLE, UWB, GPS/GNSS, RTLS, NFC, computer vision, and more depending on the setting.
The point of identification is to create a concrete association with real-world objects.
2. Sense
Identification creates a context; sensing adds information about the current state
Temperature, vibration, pressure, electrical characteristics, environment, position, velocity, level, energy consumption, structural integrity, or other characteristics of interest.
All of this adds up to a comprehensive state description of the physical world.
3. Decide
The AI decision layer takes information from identification, sensing, history, enterprise data, and constraints to produce an actionable outcome.
This could take the form of
an anomaly detection,
forecast,
predictive maintenance,
quality assessment,
risk scoring,
demand forecast,
inventory projection,
or recommendation for downstream actions and processes.
Crucially, the decision can be advisory or require human confirmation based on the use case.
4. Act
At the simplest level, the act layer takes a decision from the AI and applies it to the physical world.
This could mean sending a message to an operator, generating a work order, recommending an action, or even sending a command to a robot or machine.
Physical systems often require access control, authorization, constraints, validation, interlocks, cyber-security, auditing, human confirmation, and fail-safes before taking any action.
Actuation should always be bounded and controlled within the larger system.
5. Feedback
Once an action has been taken, the system may need to observe the outcome.
This might mean evaluating whether the right outcome was achieved, the equipment responded, the asset moved, the process changed state, or any other relevant action.
New measurements and status reporting from the field can be used to close the loop and make new decisions.
This is why the diagram includes a feedback step that creates a closed loop from Act back to Identify.
Why architecture matters
A modular architecture is crucial because it enables the system to grow and change.
An initial pilot might be able to use identification and location data to create value.
As the use case matures, additional data from sensors can be added, connected to AI models that generate new insights, and lead to orchestrated actions or physical processes.
This is one of the ideas behind the AIoT and Physical AI architecture developed by Aperture Venture Studio.
The fundamental engineering insight is that industrial AI systems should not be viewed as standalone models, but rather as components within a larger system that incorporates physical context, operates within constraints, and has feedback from the real world.
Top comments (0)