AI and the Future of Industrial Automation: A Deep Dive into PLC Standardization and the Engineer’s Evolving Role

Posted by on



The conversation around AI in automation often oscillates between utopian promises and dystopian fears. But for those of us knee-deep in PLC programming and commissioning, the real question is pragmatic: How do we harness AI without becoming its hostage? Let’s strip away the buzzwords and explore what’s actually changing—and what engineers must do to stay ahead.

Part 1: The Dirty Secret of PLC Code—Why Standardization Is Non-Negotiable

Imagine handing a stack of handwritten, coffee-stained circuit diagrams to a machine learning model and expecting it to design a flawless control system. That’s essentially what many companies are doing today with PLC code.



The Problem:

  • Tribal Knowledge Traps: Most PLC programs are undocumented, inconsistent, and locked in proprietary formats (e.g., Siemens TIA Portal projects). One automotive supplier I worked with had 200+ nearly identical "FB1" blocks across projects—each subtly different.

  • Technical Debt Tsunami: Legacy logic (think PLC-5 conversions to ControlLogix) often carries forward design flaws. AI trained on this data will replicate those flaws at scale.

The Solution: Data-Centric Engineering
PLC standardization isn’t about rigid templates—it’s about treating logic as structured data. Here’s how to break it down:

  1. Code Extraction & Tag Normalization

    • Use tools like TIA Portal Openness API or CODESYS Automation Server to parse projects into machine-readable formats (XML, JSON).

    • Enforce naming conventions (e.g., ISA-88 hierarchical tags: Area1_LineA_Conveyor2_Speed).

    • Example: A German packaging OEM reduced integration errors by 40% after normalizing 15,000+ tags across 50 machines.

  2. Modular Architecture Design

    • Layer 1: Base libraries (e.g., motor control FBs with standardized I/O).

    • Layer 2: Process-specific modules (e.g., batch sequencing aligned with ISA-88).

    • Layer 3: Machine templates (e.g., palletizers with pre-validated safety interlocks).

  3. Version Control & Governance

    • Treat PLC code like software: Git for logic, CI/CD pipelines for validation.

    • One U.S. systems integrator now auto-tests AI-generated code against digital twins before deployment.

Part 2: Building an Industrial-Grade AI Model—Beyond the Hype

Let’s dissect the Suzhou IT engineer’s challenge (and why it’s universal):

Step 1: Foundation Models—The Double-Edged Sword

  • Tools like DeepSeek excel at generating generic logic (e.g., SCL for FIFO buffers). But industrial code requires context: Which safety standards apply? What’s the maintenance history of Valve X?

  • Pitfall Alert: A European pharma company’s AI kept generating code using deprecated function blocks because its training data included 10-year-old projects.

Step 2: Domain Adaptation—Where the Real Work Begins

  • Fine-Tuning with Guardrails:

    • Limit AI’s “creativity” with rule-based constraints (e.g., “Always use TON instead of custom timers”).

    • Embed company-specific standards (e.g., “Motor start sequences must include 500ms ramp-up”).

  • Human-in-the-Loop Validation:

    • Engineers at a Texas oil rig supplier now spend 70% less time coding—but 50% more time refining AI training datasets.

Step 3: The Feedback Loop

  • AI-generated code must improve with every commissioning cycle.

  • Example: A Dutch robotics firm feeds field data (e.g., valve wear patterns) back into their model to optimize preventive maintenance logic.

Part 3: The New Industrial Engineer—From Coder to Systems Architect

The Skills Shift:

  • Old World: Mastery of ladder logic, HMI design, and troubleshooting.

  • New World: Hybrid expertise in:

    • Data Engineering (SQL for querying PLC metadata, Python for cleaning training data).

    • Model Debugging (Interpreting why AI chose a MOV instead of a SWAP).

    • Cross-Domain Collaboration (Translating OT requirements to IT teams).

Case Study: The 60-10-30 Rule in Action
A Midwest food processing plant redesigned their workflow:

  • 60% Design: Engineers now define modular architectures and validate AI outputs against P&IDs.

  • 10% Coding: AI handles boilerplate logic (e.g., alarm handling).

  • 30% Debugging: Focus shifted to edge cases (e.g., “Why does the AI fail when Sensor Y degrades?”).

Part 4: The Road Ahead—Where IT/OT Collision Creates Opportunity

1. The Rise of “Industrial Prompt Engineers”

  • Crafting precise instructions for AI tools will become a critical skill. Example:

    • Bad Prompt: “Generate a conveyor control program.”

    • Good Prompt: “Generate a Siemens S7-1500 routine for a bidirectional conveyor with E-Stop Category 3, using FB500 motor control blocks, and tag naming per ISA-88.”

2. PLC Code as a Living Asset

  • Future systems will auto-update logic based on real-time data. Imagine:

    • AI adjusting PID loops dynamically as pump efficiency declines.

    • Predictive logic rewriting itself after a bearing failure pattern is detected.

3. The Ethics of Automation

  • Who’s liable when AI-generated code fails? A recent EU draft law proposes “explainability mandates” for critical control systems—engineers must document why AI chose specific logic.


Final Word: Survival Tips for the AI Era

  1. Start Small, Think Modular
    Begin with a single machine type (e.g., palletizers) and build your first AI-ready library.

  2. Embrace the Grind
    Standardizing legacy code is tedious—but so was manually debugging 10,000 rungs of ladder logic.

  3. Learn to Speak DevOps
    Tools like Azure DevOps for PLCs aren’t coming—they’re here.

  4. Never Trust AI Blindly
    As one engineer in Detroit told me: “AI writes code. Humans ensure it doesn’t blow things up.”

The Bottom Line:
AI won’t replace automation engineers. But it will amplify the gap between those who adapt and those who cling to outdated workflows. The future belongs to engineers who treat PLCs not as “black boxes” but as data-driven systems—where every rung of logic is a building block for smarter, safer, and more sustainable industry.

P.S. To the IT team reading this: Yes, we’ll always need your help. No, we won’t let you near the emergency stop circuits.