Industrial AI

Industrial AI refers to the application of artificial intelligence across industrial environments — factories, gigafactories, fabs, data centers, and energy plants. Unlike enterprise AI, which is focused on knowledge work and decision support, Industrial AI is embedded into machines, processes, and control systems, making operations more autonomous, efficient, and resilient.

Key Layers

IoT (Industrial Internet of Things)

Networks of connected sensors, actuators, and edge devices that collect real-time data from equipment and processes.

▢ Vibration, temperature, and pressure sensors.

▢ Smart meters and energy monitors.

▢ Connected robotics and AMRs.

▢ Edge AI devices for local inference.

OT (Operational Technology)

The backbone of industrial control and automation systems.

▢ SCADA (Supervisory Control and Data Acquisition).

▢ PLCs (Programmable Logic Controllers).

▢ DCS (Distributed Control Systems).

▢ Robot and motion controllers.

▢ Microgrid and BMS controllers.

IT/OT Convergence

Integration of operational systems with IT platforms and cloud/AI services.

▢ Data pipelines (MES, ERP, PLM).

▢ AI platforms and industrial digital twins.

▢ Predictive analytics and machine learning models.

▢ Cybersecurity controls bridging IT and OT.

Applications

Predictive Maintenance

▢ Motors, drives, pumps, chillers, compressors.

▢ Reduce unplanned downtime and maintenance costs.

Process Optimization

▢ Yield optimization in fabs and gigafactories.

▢ Real-time process control in chemical and pharmaceutical plants.

Energy & Sustainability

▢ AI-driven microgrids and BESS optimization.

▢ Demand response and peak load management.

▢ HVAC and cleanroom optimization.

Quality Control

▢ Computer vision for defect detection.

▢ Adaptive inspection and inline process adjustments.