AI arrives in industrial automation
For decades, PLC programming has followed a manual and highly specialised process: the engineer analyses the process, designs the control logic and writes the code in languages such as Ladder, FBD or Structured Text. This requires years of experience and deep knowledge of both the hardware and the industrial process being controlled.
The emergence of artificial intelligence in the industrial world is changing this reality. The goal is not to replace the engineer, but to amplify their work with tools that reduce time, minimise errors and open up new possibilities previously unthinkable in industrial control.
Automatic PLC code generation
One of the most promising AI applications in PLC is automatic code generation from natural language descriptions or process diagrams. Tools based on large language models (LLMs) are already capable of:
- Converting a textual description of a process into IEC 61131-3 function blocks
- Generating Structured Text or Ladder code from functional specifications
- Proposing control templates for typical sequences (star-delta start, PID control, alarm management)
- Translating code between IEC 61131-3 languages (e.g. from Ladder to ST)
Manufacturers such as Siemens already include AI assistants in TIA Portal that suggest function blocks and detect programming errors in real time. Beckhoff, for its part, is exploring the integration of LLMs into TwinCAT to accelerate the development of motion control applications.
Predictive diagnostics and anomaly detection
Beyond code generation, AI brings enormous value in the monitoring and diagnostics of PLCs in production. Machine learning models can analyse historical PLC data to:
- Detect anomalies before they become failures: subtle variations in cycle times, fluctuations in analogue signals or unusual activation patterns that an operator would not perceive.
- Predict failures in actuators, sensors and I/O modules by analysing trends over time.
- Automatically optimise PID parameters by adjusting gains based on real process behaviour.
- Reduce false positives in alarm systems, avoiding "alarm fatigue" in the control room.
AI applied to code debugging and optimisation
Debugging a complex PLC program can take hours or days. AI assistants can dramatically accelerate this process:
- Static code analysis: detection of unused variables, race conditions, infinite loops or contradictory logic.
- Functional safety review: identification of dangerous states or missing safety conditions (e-stop, interlocks).
- Optimisation suggestions: proposals to reduce cycle time or improve code readability.
- Automatic documentation: generation of comments and technical documentation from existing code — work that is often neglected in projects with tight deadlines.
Real-world use cases in 2025–2026
| AI Application | Tool / Platform | Key benefit |
|---|---|---|
| ST code generation | TIA Portal AI Assistant (Siemens) | −40% programming time |
| Predictive diagnostics | MindSphere / Industrial AI | −60% unplanned downtime |
| PID optimisation | ML on SCADA / Edge computing | +15% process efficiency |
| Anomaly detection | Azure IoT / AWS Lookout for Equipment | Alerts weeks in advance |
| Code review | Integrated LLMs (GPT, Claude) | Less technical debt |
Current challenges and limitations
Despite the enormous potential, integrating AI into PLC environments presents challenges that should not be underestimated:
- Functional safety: in SIL applications, AI-generated code must pass the same validation as manually written code. Ultimate responsibility remains with the engineer.
- Data quality: ML models require clean, labelled historical data — something not always available in legacy plants.
- Connectivity: exploiting AI in real time requires OT/IT connectivity and edge architectures that many plants have not yet implemented.
- Cultural adoption: automation teams, accustomed to highly deterministic environments, need to adapt to working with probabilistic systems.
The automation engineer's role in the AI era
AI does not replace the automation engineer; it makes them a more productive and strategic professional. Knowledge of the process, field experience and technical judgement remain irreplaceable. What changes is that repetitive tasks — generating standard code, documenting, searching for errors — are progressively delegated to intelligent tools, freeing up time for higher-value work.
At Bluemation we closely follow the evolution of these technologies and integrate AI tools into our PLC programming workflows to deliver higher-quality projects in less time. If you would like to know how this could be applied in your plant, get in touch with us.