The Top Five Real-World Use Cases for AI in Industrial Automation Today / Artificial Intelligence in Automation
Industrial automation has entered a phase where AI is no longer experimental — it’s a practical, daily tool for engineers, technicians, and integrators. Used correctly, GPT-5 and similar large language models (LLMs) can dramatically speed up learning, troubleshooting, planning, and optimization on the plant floor. Here’s how forward-thinking teams are using it right now, with tactical prompts you can try immediately.
- Learn New PLC, Robot, and Vision Platforms on the Fly with AI Code-Assist
Why it matters:
Shifting between Siemens TIA Portal, Rockwell Studio 5000, Fanuc robots, or Cognex vision systems used to mean hours (or days) of combing through manuals. GPT-5, trained with examples and guided by your own standards, can bridge those knowledge gaps in minutes.
Best mode: Text or agent mode (for multi-step code generation and review).
Prompt to try (Text):
You are an expert Siemens TIA Portal V18 programmer. Create a rising-edge-latched start/stop circuit in LAD for a conveyor with fault inhibit. Tag names: Conv_RunCmd, Conv_StopPB, Conv_FaultOK, Conv_Motor. Then generate the equivalent Structured Text version and explain it so a junior tech can follow.
Expected result:
A ladder diagram logic description plus a clean ST equivalent, both explained line-by-line. This allows you to paste into a learning project, run in simulation, and peer-review before deployment.
Reference: Siemens Industrial Copilot enables this style of interactive, code-aware engineering inside TIA Portal (Siemens Support, 2025).
- Debugging Programs Faster with Safe Test Snippets and Checklists
Why it matters:
When a production line is down, time is critical — but so is avoiding untested logic changes. GPT-5 in agent mode can isolate a minimal failing block, propose a reversible test, and log the change for compliance.
Best mode: Agent mode (for automated code diffing, safety checklists, and multi-file handling).
Prompt to try (Agent):
Agent, open the file ‘PickAndPlace_Job1.mod’ and find the faulting routine. Propose a safe, reversible test patch that logs part rejects to a DINT array without affecting normal cycle. Generate a before/after diff and a step-by-step checklist for rollback.
Expected result:
A minimal code patch you can test offline or in simulation, plus a rollback plan and documented changes. Ideal for PLCs, robot jobs, or vision inspection scripts.
Reference: Rockwell’s FactoryTalk Design Studio Copilot demonstrates similar debugging workflow (Rockwell Automation, 2025).
- HMI Screenshot-Driven Troubleshooting
Why it matters:
Many engineers overlook that most live production data is already displayed on an HMI somewhere — alarms, recipe parameters, quality stats. Capturing relevant screens and feeding them to GPT-5 Vision mode can shortcut root-cause analysis.
Best mode: Vision + voice (snap and talk).
Prompt to try (Voice with attached images):
“Look at these three HMI screenshots from the paste-filler line. Compare production counts, fault logs, and temperature graphs. Identify any anomalies that could explain intermittent overfill alarms in the last two hours.”
Expected result:
An anomaly report cross-referencing multiple live views — like spotting a temperature spike in the tank heat zone that correlates with the overfill alarm window.
Tactic tip: Store an internal gallery of “normal” HMI states so GPT-5 can spot deviations faster.
- Fault Mining from Torque Guns, SCADA, and Machine Logs
Why it matters:
Vast amounts of diagnostic gold hide in CSV exports from tools and SCADA systems. GPT-5 can parse millions of lines in seconds to uncover hidden bottlenecks or early signs of drift.
Best mode: Agent mode with file parsing.
Prompt to try (Agent):
Load torque_data_shift1.csv and scada_microstops.csv. Identify any fault or quality patterns tied to time-of-day, operator, or ambient temperature. Suggest at least two control changes or SOP updates to reduce downtime.
Expected result:
Actionable insights like “Torque drift increases in the first 15 minutes after maintenance” or “Palletizer microstops occur when upstream buffer drops below 20%.”
Reference: Atlas Copco ToolsNet + Open Protocol integrations make this type of analysis production-ready (Atlas Copco, 2025).
- AI-Driven Safety Compliance and Audit Prep
Why it matters:
Audits often mean days of chasing down SOPs, risk assessments, and training records. GPT-5 can pre-assemble compliance packets from your document library, highlight gaps, and suggest corrective actions.
Best mode: Agent mode (document search + summarization).
Prompt to try (Agent):
Search the ‘SafetyDocs’ folder for all LOTO procedures, safety training logs, and risk assessments from the last 12 months. Compile them into a single audit packet, flagging any missing annual review dates.
Expected result:
A ready-to-submit audit binder in PDF, plus a punch list of documents to update — cutting prep time from days to hours.
Industry Momentum
The major OEMs are shipping copilots now, not “someday”:
- Siemens: Industrial Copilot links TIA Portal V19/V20 to Azure for code generation and explanations (Siemens Support, 2025).
- ABB: Ability Genix Copilot contextualizes plant data for predictions and guidance (ABB Group, 2025).
- Cognex: Edge learning vision tools train in minutes for defect detection (Cognex, 2025).
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Thanks for building the future with us.
Tony Wallace
Text 586-770-8083
Email info@automateamerica.com
