Every major PLC vendor now has some form of AI sitting inside the IDE, watching you write logic and offering to finish your thought. Siemens has been layering copilot-style assistance into TIA Portal. Rockwell has pushed AI-assisted authoring into the Studio 5000 environment. Beckhoff has been building scripting and code-assist capability around TwinCAT, leaning on its already-strong structured text and IEC 61131-3 tooling. The demos all look the same: type a comment describing what you want, watch rungs or ST blocks materialize, feel a little uneasy about how fast that was.
The uneasy feeling is correct. These tools are genuinely useful for a specific, narrower slice of the job than the marketing suggests, and dangerous in ways that won’t show up until the code is running on a machine with people standing next to it. Having now watched these features move from trade-show demo to actual project use, the pattern is consistent across all three ecosystems: they’re excellent at boilerplate and terrible at judgment, and the entire discipline of using them safely is knowing exactly where that line sits.
Where the AI genuinely earns its keep
The strongest, least controversial use case across TIA Portal, Studio 5000, and TwinCAT is tag mapping and data structure scaffolding. Generating UDTs, mapping I/O lists into structured tag databases, building the repetitive shell of an AOI or function block instance — this is exactly the kind of pattern-matching, low-judgment work that large language models are good at, and it’s also exactly the kind of work that burns hours of a controls engineer’s week for no engineering value. If you’re bringing in a vendor’s I/O list for a skid and need three hundred tags mapped with sensible naming conventions and data types, letting the assistant do a first pass and then eyeballing the result is a legitimate time saver.
Comment and documentation generation is the second clear win. Structured text and ladder logic written by a rushed engineer under deadline pressure is notoriously under-documented, and that debt gets paid by whoever has to troubleshoot it two years later at 2 a.m. Asking the copilot to summarize what an existing rung group or ST routine does, or to draft header comments and revision notes, works reasonably well because the model is describing code that already exists rather than inventing new logic. The risk here is much lower — a wrong comment is embarrassing, not dangerous — which makes it a good low-stakes place to build trust in the tool and learn its quirks.
Interlock and permissive scaffolding sits in a middle zone. Ask any of these copilots to draft a standard start-permissive chain — the usual E-stop, guard, low-low level, drive-fault pattern — and you’ll typically get something structurally sane, because that pattern is extremely common in the training data and well-represented across countless public and vendor-provided code examples. It’s a fine starting skeleton. It is absolutely not a finished interlock, and treating the first draft as done is where things start to go sideways.
Where it quietly hallucinates logic that compiles
Here’s the failure mode every controls engineer needs to internalize: an AI-generated rung or ST block can be syntactically perfect, compile cleanly, pass a rungs-scan check, and still be functionally wrong in a way that only shows up under a specific process condition. The IDE’s compiler checks grammar, not intent. It has no idea that your tank’s low-level switch is fail-open, that your particular VFD faults on a rising edge rather than a level, or that the sequence you asked for skips a purge step your safety case actually requires.
This isn’t a hypothetical edge case — it’s the default behavior of these tools when the prompt is even slightly ambiguous, which real-world prompts almost always are. Ask for “an interlock that stops the conveyor if the guard is open,” and you’ll get logic that does that. What you won’t get, unless you specify it, is the right fault latching behavior, the correct reset sequence, proper handling of a sensor that’s stuck rather than genuinely triggered, or an understanding of what else is happening downstream when that conveyor stops mid-cycle. The model fills those gaps with statistically plausible defaults pulled from generic examples, not with knowledge of your process.
Timer and counter logic is a particular trouble spot. Off-delay versus on-delay behavior, retentive versus non-retentive counters, and edge-triggered versus level-triggered conditions are all places where a subtly wrong choice produces code that looks right in a static read-through and only fails when the machine actually cycles. These are also exactly the kind of details that get lost when a prompt is written in plain English rather than precise IEC 61131-3 terms — the model does its best to guess which one you meant.
Structured text seems to fare somewhat better than ladder in practice, if only because ST is closer to the general-purpose code these models were largely trained on, so hallucinations tend to look more like ordinary programming bugs — off-by-one errors, mishandled array bounds, missed null/uninitialized checks — rather than deep misunderstandings of machine behavior. That’s a mild comfort, not a reason to relax scrutiny.
A review checklist before anything AI-generated goes near a live PLC
None of this is an argument against using these tools. It’s an argument for treating AI-generated logic exactly like code from a junior engineer or an outside contractor: probably fine, needs a real review, never gets a pass straight to download. A working checklist:
- Trace every input to its physical source. For each contact, tag, or condition the AI used, confirm you know what field device or upstream signal actually drives it, and what its fail state is.
- Check fault and edge behavior explicitly. Verify timer types, counter retentiveness, and edge- vs. level-triggered logic against your actual sequence of operations, not against what “usually” makes sense.
- Simulate or bench-test before floor deployment. Use the vendor’s simulation environment or a test rig to force every interlock condition, including the ones you don’t expect to hit in normal operation.
- Validate against the safety and process design basis, not just the prompt. The AI answered the question you asked. Confirm the question you asked actually captures your P&ID, SIS requirements, and sequence documentation.
- Have a second engineer review anything touching safety-adjacent logic. AI-generated code doesn’t get an exemption from your normal peer-review or MOC process — if anything it deserves more scrutiny until your team has built a track record with it.
- Diff against the previous known-good version. When using AI to modify existing logic, review the actual delta, not just the new code in isolation — unintended changes to adjacent rungs are a real and common failure mode.
The honest read is that these copilots are good juniors: fast, tireless, occasionally brilliant on boilerplate, and completely unaware of what they don’t know. Used that way — for scaffolding, documentation, and first drafts that get real engineering review — they save meaningful time. Used as a shortcut past that review, on the theory that a clean compile means correct behavior, they’re a fast way to put logic on a live controller that nobody actually vetted. The vendors building these features know the difference. The engineers using them need to hold that line even when the deadline is tight and the demo made it look so easy.
This article was written with the assistance of artificial intelligence. While we aim for accuracy, the information may be incomplete, out of date, or incorrect, and should be independently verified before you rely on it for any decision. It is provided for general information only and does not constitute professional advice.
