Every major PLC ecosystem now ships some flavor of AI assistance inside the engineering environment. Siemens has been pushing TIA Portal toward a more cloud-collaborative, copilot-assisted model. Rockwell has been layering generative AI features into the Studio 5000 experience and its broader FactoryTalk design tools. Beckhoff has been quieter about it publicly but is clearly moving TwinCAT in the same direction, with code-completion and structured-text generation aimed at cutting engineering hours. The pitch from all three is basically identical: describe what you want in plain language, get a starting point in ladder, structured text, or function block form, and spend your time reviewing instead of typing.
That pitch is not wrong. It’s also not the point. The point is that plants are now going to have AI-suggested logic sitting in project files, sometimes committed and downloaded, with no established discipline for how it gets checked before it runs a real machine. Most shops have a mature process for reviewing a contractor’s ladder logic. Almost none have a process for reviewing a copilot’s ladder logic, and the two are not the same problem.
What these tools are actually good at
Strip away the marketing, and the current generation of PLC copilots is a large language model trained on code patterns, wired into an IDE that understands your project’s tag database and hardware configuration. That combination is genuinely useful for a specific, narrow band of work:
- Tag and data structure scaffolding. Generating a UDT for a conveyor zone, a valve manifold, or a recipe parameter set from a description is fast, mechanical, and low-risk. The AI isn’t making control decisions here, it’s doing data modeling, and that’s a task LLMs are structurally suited to.
- Boilerplate logic. Motor start/stop interlocks, standard alarm annunciation blocks, HMI faceplate wiring, comment generation for existing rungs — the repetitive scaffolding that every controls engineer has typed a thousand times. This is where the productivity claims are legitimate.
- Documentation and cross-referencing. Summarizing what a routine does, flagging unused tags, generating I/O lists from a project. Useful, and low stakes if it’s wrong, because a bad comment doesn’t stop a line.
- First-draft structured text for well-known patterns. PID wrappers, state machines for simple sequencing, communication handling for OPC UA nodes. Recognizable patterns that show up constantly in training data are the tools’ comfort zone.
Where it hallucinates, and why that’s dangerous in this domain
The failure mode that matters isn’t the AI writing code that doesn’t compile — that’s caught immediately. The failure mode that matters is code that compiles, looks plausible, and is subtly wrong in a way that only shows up under a specific machine state.
This happens most often in three places. First, interlock logic and safety-adjacent permissives, where the model will often produce something that looks like a correct interlock chain but misses a specific edge case — a manual-mode override, a fault-reset sequence, a two-hand-control timing requirement — because that edge case is specific to your machine and wasn’t in whatever pattern the model learned from. Second, sequencing and state transitions on anything beyond a simple linear sequence, where the model can silently drop a transition condition or leave a race condition between two steps that only ever manifests when a downstream sensor is slow. Third, anything involving physical units, scaling, or engineering constants — the model has no actual knowledge of your process, so a scaling factor or an alarm setpoint it generates may be a reasonable-looking placeholder rather than a value grounded in your equipment.
None of this is a knock on any specific vendor’s implementation. It’s a structural property of how these models work: they predict plausible continuations of code based on patterns, they don’t simulate physics, and none of them has access to your P&ID, your safety case, or the actual behavior of the actuator on the other end of that output. A model can be well-integrated into TIA Portal, Studio 5000, or TwinCAT and still not know that your press has a mechanical dwell that isn’t captured anywhere in the project file.
A review gate that actually fits how plants work
The mistake most plants will make is treating AI-generated logic as either fully trusted (because the IDE vendor built it in, so it must be fine) or fully banned (which just pushes it underground, since engineers will use it anyway and paste results in). Neither works. What works is treating AI-suggested code exactly like you’d treat code from a new contractor you’ve never worked with before: useful, plausible, and unverified until proven otherwise.
Before it ever reaches a test bench
- Tag every block of AI-suggested logic in comments or version control metadata. If your IDE or PLM doesn’t do this automatically, do it manually. You need to know later which rungs were human-authored from scratch and which started as a suggestion, the same way you’d flag a library block from a third-party vendor.
- Run a manual line-by-line read against the actual machine’s functional description or P&ID — not against what “looks right.” This is the step teams skip because the code looks clean, and it’s the step that catches the missing edge case.
- Check every constant, setpoint, and scaling value by hand. Assume none of them are grounded in your process until you’ve verified them against your own documentation.
Before it goes anywhere near a live line
- Simulate or bench-test with forced I/O covering the fault states and edge conditions your machine actually has — manual mode, e-stop recovery, out-of-sequence operator action — not just the happy path.
- Route it through whatever MOC (management of change) or code-review process you already use for logic changes on safety-related or production-critical equipment. AI-origin code doesn’t get a lighter review; if anything it gets a slightly heavier one until your team has enough experience with the specific tool to calibrate trust.
- Keep a human signature on anything that touches a safety instrumented function or a hardwired safety circuit, full stop. None of the current copilot tools are being marketed as safety-rated code generators, and none should be treated as one.
The real shift isn’t the code, it’s the review workload
The honest way to think about this generation of tools is that they move effort from writing to reviewing, not that they remove effort. A controls engineer who used to spend an afternoon writing a motor control routine now spends fifteen minutes generating one and forty-five minutes verifying it — and if your team treats that verification time as optional because “the AI probably got it right,” you’ve quietly removed the one safeguard that made the productivity gain worth having.
Siemens, Rockwell, and Beckhoff are all racing toward the same destination because the underlying pressure is real: there aren’t enough controls engineers to go around, and boilerplate is boilerplate regardless of which IDE it’s written in. That’s a legitimate reason to adopt these tools. It’s not a reason to skip building the review discipline that has to come with them. The plants that get value out of AI-assisted engineering over the next few years won’t be the ones with the flashiest copilot integration — they’ll be the ones that quietly wrote down, in their commissioning procedures, exactly what “reviewed” means for a rung nobody on the team originally typed.
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.
