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AI·6 min read·

The one AI task in automation with no downside

Documentation is the most disliked, most billable and least risky thing to hand to a machine.

Short answer

Yes, and it is the strongest current use case because the risk is close to zero. A model reading a program can produce an I/O schedule, a control narrative and rung comments faster than a person, and errors are visible on the page rather than in the machine. It cannot decide what the machine should do, so it cannot get that wrong.

The loop that makes it usableModel writesany providerCompiler answerspass / failYou read itonly if it passedfailures go back, with the errorsthis is what makes a free model good enough

If you are looking for a place to start with AI in an automation business, it is documentation, and the reasoning is entirely about risk.

Why it is the safe one

The model is describing something that already exists rather than deciding what should exist. It reads the tag table and writes the I/O schedule. It reads the rungs and describes the sequence.

Errors land on a page where a reviewer sees them, not in a machine where a condition nobody tested reveals them.

What it produces well

  • I/O schedules. Directly derived from tags and addresses. Tedious, mechanical, and exactly what a machine is for.
  • Rung comments. A program with no comments is a program nobody wants to inherit, and a first pass is far better than the nothing that usually exists.
  • Control narratives. A plain-English description of what each routine does, as a first draft for the engineer to correct.

What it cannot do

Explain *why*. A narrative can say the interlock exists. It cannot say it was added after an incident in 2019, and that is often the most important sentence in the document.

Generated documentation is a floor, not a ceiling. It gets you from nothing to something, and the engineer's job becomes editing rather than writing.

Common questions

What PLC documentation can AI reliably produce?
I/O schedules from the tag table, rung and network comments from the logic, and a first-draft control narrative describing what each routine does. All three are derived from the program itself, so the model is describing rather than deciding, which is where it is strongest.
Should AI-written documentation be reviewed?
Yes, but it is a fast review because errors are visible. A wrong sentence in a narrative is obvious to anyone who knows the machine, unlike a wrong rung, which may only appear under a condition nobody tests. This is exactly why documentation is the low-risk starting point.

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