Industrial artificial intelligence Industrial AI with a person who decides.

Industrial artificial intelligence (industrial AI) is the use of machine learning models on the data a plant already produces —PLCs, sensors, cameras, systems and what people record— to count, detect, predict and explain what happens in production.

It differs from consumer AI in three ways: it answers plant questions rather than general ones, it is measured against an operating record and, when done well, it cites the source of every data point and lets a person decide before anything touches a machine.

Who offers it in Chile? The comparison of industrial AI companies in Chile reviews seven options by what they do, where the data comes from and where it runs.

What industrial artificial intelligence is

Industrial artificial intelligence is the use of machine-learning models on the data a plant already produces (PLCs, sensors, cameras and systems) to detect, count, predict and explain what happens in production, with the latency and security a physical process demands. Unlike consumer AI it does not answer general questions; it answers plant questions. How many units left line 3 this shift? Will this motor fail before the weekend? Why did OEE drop on Tuesday?

Three traits set it apart. It works on live process data, not internet text. It runs where the machines are, at the edge or on a plant server, because a decision that takes half a second arrives late. And it has to integrate with what already exists (SCADA, MES, ERP) instead of replacing it. In mid-sized plants running equipment from several generations, that third point usually decides whether a project ships or stays a pilot.

At iGromi, industrial AI is built from the modules of iGromi OS —vision, Edge AI, production (OEE), maintenance, IoT and the Second Brain— on top of the equipment the plant already has.

The three pillars of a plant: why AI fails without context

A plant's problem is not a lack of data. Its data lives in three pillars that do not talk to each other:

• Assets. Machines, PLCs, sensors, cameras and GPS emit signals all shift long, in protocols management cannot read: Modbus, OPC-UA, Profinet, LoRa.
• Systems. SAP, ERP, MES, SCADA and spreadsheets each know their part and none knows the whole. Between them, someone types it by hand.
• People. Operators and management. They hold what the machine never records: knowledge, judgement and how each problem was solved. Today it lives in a WhatsApp thread or in someone's head, and it is gone at shift change.

What is missing between the three is context: what happened, where, when and with what evidence. A model that sees only one pillar produces alerts nobody can explain, and an alert nobody explains gets muted. That is why useful industrial AI starts by connecting the pillars and only then by training models. It is the idea behind the industrial second brain: a plant memory that people and agents query with evidence.

Where it works today: six areas with measurable results

• Predictive maintenance. Vibration, current, temperature and sound from motors, gearboxes and pumps. The model learns each asset's normal signature and warns days ahead instead of minutes. See predictive maintenance and the IoT predictive maintenance guide.
• Machine vision for quality and counting. Ordinary IP cameras, often the security cameras already on the wall, processed on site with networks such as YOLOv8: YOLOv8 for defect detection and counting and automated counting.
• OEE and production. Counting per sensor and per recipe, stops per machine, downtime explained. See the OEE guide.
• Energy. Consumption per batch or per unit produced, and electrical anomalies that anticipate mechanical failure.
• People safety. Local vision for exclusion zones, lifelines and PPE, with no footage leaving the plant.
• Knowledge and agents. Manuals, logbooks and meeting notes queried in plain language with evidence (RAG), and agents that explain a deviation instead of just flagging it. See agentic AI in manufacturing and physical AI.

How to start: a scoped pilot measured against reality

Not all industrial AI has to run at the edge. The practical rule is set by latency and by data:

• Edge. When the decision depends on a camera or a machine: inference with no internet round trip, read-only PLC access and measurement that continues if the connection drops.
• On-premise. A server in the plant to consolidate lines, run the production module (OEE) and the second brain. Data is processed on site.
• Cloud. The shift app, corporate reporting, plant-to-plant comparison and models retrained on months of history.

The company decides, not the vendor. With iGromi the free app lives in the iGromi cloud, separated per company; Premium adds a private, isolated cloud with the AI model inside it; Enterprise moves processing into the plant on Edge or on-premise. IT conversations tend to be simple: no inbound ports to the internet and IEC 62443 as the framework. Cameras that capture people process personal data under Chile's data-protection law: processing in the plant reduces what is transferred, but legal responsibility stays with each company. More in edge computing for industrial networks.

Before choosing a vendor, check the six criteria of secure industrial AI.

The mistakes that cost the most

• Starting with the model instead of the data. Without a reliable count there is no prediction worth having.
• Replacing equipment that works instead of connecting it.
• Sending everything to the cloud and discovering the latency and the bandwidth bill afterwards.
• Alerts without context. A model that says “anomaly” without where, when or evidence is muted within two weeks.
• Measuring the pilot with the vendor's metric instead of the process's.