How Today's Platforms Are Already Smarter Than You Think
We’ve written about AI in industrial automation before, detailing how it’s reshaping workforce demands, redefining operational efficiency, and expanding what’s possible across both the supervisory and control layers. In our post on the automation skills gap, we explored how AI is part of the solution to bridging engineering shortages. In our earlier trend overview, we examined how AI is influencing future-focused strategies.
AI in automation isn’t just a vision for the future; it’s here today, shaping the tools engineers use on the plant floor, inside control rooms, and across supervisory systems. At Pigler Automation, we see firsthand how AI is embedded directly into the platforms we use every day, including Siemens, Beckhoff, Rockwell, and Ignition. This post highlights the tools already driving smart decisions and system improvements in 2025.
Siemens: AI as an Integrated Strategy
Siemens has chosen a strategic approach to integrating AI within its automation stack, and powerful tools are already making an impact:
- SIMATIC S7-1500 TM NPU Modules: These specialized hardware modules integrate AI model inference directly at the PLC level, enabling applications such as real-time anomaly detection and predictive maintenance without relying on cloud or external computer.
- SIMATIC Edge AI: Through the Siemens Industrial Edge platform, trained AI models can be deployed near the data source. This facilitates quick decision-making, enhances latency, and ensures secure processing for tasks such as visual inspection or energy optimization.
- SIMIT Simulation with AI-in-the-Loop: Siemens’ SIMIT platform, which Pigler frequently uses for virtual commissioning, now allows integration of AI to simulate intelligent behavior. This helps operators and engineers test scenarios involving AI-based logic before deploying live.
- AI Workbench: A low-code environment that enables engineers to build and train machine learning models tailored to industrial processes, with simple export for deployment to Siemens edge devices.
Takeaway: Siemens isn’t just AI-capable; it’s integrating AI into the workflow of every controls and systems engineer.
Beckhoff: TwinCAT Machine Learning in Real Time
Beckhoff is transforming industrial controllers into AI inference engines, thanks to its open and high-performance architecture:
- TwinCAT 3 Machine Learning: Supports the use of standard AI models (e.g., ONNX, TensorFlow) natively within the PLC logic. This enables real-time predictions for quality control, predictive maintenance, or sensor data fusion.
- TwinCAT Machine Learning Creator: Empowers non-AI experts to easily develop efficient, high-quality AI applications by automatically generating AI models from data sets.
- TwinCAT Machine Learning Inference Engine: A high-performance execution module for trained machine learning algorithms that facilitates real-time inference within the TwinCAT environment.
Takeaway: Beckhoff is integrating AI into the control loop, not merely treating it as an external data science project.
Rockwell Automation: AI Inside the Control System
Rockwell Automation has incorporated AI into its ecosystem by integrating analytics and machine learning into existing platforms:
- FactoryTalk Analytics LogixAI: Formerly Project Sherlock, this tool delivers AI-based monitoring directly within ControlLogix PLCs. It continually learns from system behavior to identify anomalies, trends, or deviations.
- Pavilion8: A model predictive control (MPC) solution that optimizes manufacturing processes to enhance plant yield, improve quality, and reduce risk.
- ThingWorx IIoT Platform: By forming partnerships and acquiring companies, Rockwell has expanded its AI capabilities to include modeling, optimization, and digital twin-based forecasting.
Takeaway: Rockwell’s strength lies in integrating AI with existing control logic, making insights immediately actionable.
Ignition: A Platform for AI Integration
While Ignition by Inductive Automation isn’t an AI engine by itself, its strength lies in its openness and flexibility, making it a seamless AI companion:
- Python and Scripting: Built-in scripting enables engineers to execute custom Python-based AI logic, statistical models, or optimization algorithms.
- API and MQTT Connectivity: Ignition can act as a bridge between OT and IT, linking real-time plant data to cloud-based AI services (like Azure ML, AWS SageMaker, or local inference engines).
- Visualization and Action: AI insights can be displayed instantly in dashboards, alerts, or control decisions within Ignition’s SCADA interface.
Takeaway: Ignition is designed to be AI-ready—a powerful tool for integrating AI insight with HMI/SCADA control.
Final Thoughts: AI in Automation Is Already Here
If you’re picturing AI as a distant innovation in industrial automation, it’s time to adjust your timeline. The tools mentioned above are already available and actively used in production environments. More importantly, they are being integrated directly into the systems engineers currently rely on—no separate infrastructure or major overhaul is necessary.
At Pigler Automation, we continue exploring how these capabilities enhance reliability, improve decision-making, and support smarter industrial operations. Whether it’s integrating AI into PLC logic or using AI to enhance supervisory visualization, 2025 is the year we stop asking if AI belongs in automation and start refining how we use it.
Interested in AI-enabled automation or have a system you’re looking to modernize? Let’s talk. Our team can help you assess what’s possible with the tools already at your fingertips.
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