Will AI replace a Control Systems Engineer?
AI risk 42/100Opportunity 86/100Future demand 79/100
How AI is affecting this role
- ›Instead of manually searching through 50,000 lines of alarm logs, an AI script clusters errors by frequency and correlation, instantly revealing that a specific servo drive fault always triggers a conveyor stoppage.
- ›Using ChatGPT to convert a PDF description of a new packaging machine requirement into a draft Structured Text (ST) function block, reducing coding time by 40%.
- ›An AI model trained on vibration data alerts the team 48 hours before a hydraulic pump failure, allowing maintenance during scheduled shifts rather than stopping production.
Ways to survive
- ›Master 'low-code' industrial data platforms like Ignition or Tulip to visualize data without heavy coding.
- ›Learn to audit AI-generated logic for safety hazards (e.g., ensuring no unintended start sequences are generated).
- ›Focus on cybersecurity standards (IEC 62443) as AI opens new attack vectors in OT networks.
Ways to get ahead with AI
- ›Build custom 'digital twins' of your production lines using simulation software fed by AI models.
- ›Develop internal AI agents that automatically draft shift reports based on SCADA tag data.
- ›Learn to deploy containerized AI models directly onto PLC hardware where hardware permits (e.g., Siemens SIMATIC S7-1500 with AI capabilities).
How ONROL helps
We will train you to build Industrial IoT pipelines, write Python scripts to analyze production data, and deploy AI models on the Edge (on the machine) to optimize manufacturing throughput.
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