Rockwell Automation's plant in Singapore, which builds industrial automation equipment, has cut the time new technicians need to learn to troubleshoot its hundreds of machines from nine months to three, by Rockwell's own estimate. Much of that comes from an AI maintenance assistant, one of many digital tools at the plant, so not all of the gain is the assistant's.
When a machine shows an error, a technician types or speaks it, and the assistant answers with the likely causes, most likely first, and the steps to fix each one. It draws on notes from engineers with 20 to 30 years on the shop floor, the equipment manuals and the plant's machine data. The plant built it partly because veterans retire and take what they know with them.
The beginner gains most
Learning a plant like this is mostly waiting. A technician finds out what a machine does when it fails only when it fails, and with hundreds of machines that takes most of a year. The assistant lets a new technician meet an old fault already knowing the answer a veteran would give.
Customer support shows the same pattern. When a company gave its agents an AI assistant, they resolved 14% more issues an hour on average and 34% more for the least experienced, while the most experienced barely changed. The veterans already had the knowledge. The beginners got it.
What it looks like in an office
Picture the accounts payable team at a distributor. Its best clerk knows that when a supplier's invoice is off by a few cents, it is usually that supplier's rounding on pallets, and when a whole line is missing, the call goes to the warehouse, not the supplier. Nobody has written it down. If an assistant held her notes and the team's past cases, a new clerk could ask and get her answer in seconds.
Suppose that cuts the time a new clerk needs to work alone from nine months to three. Each hire is dependable six months sooner, and a team that hires four people a year gets back roughly two years of experienced work every year. The same arithmetic applies to a recruiter's screening calls, a school department's timetable or a repair shop's diagnoses.
The veterans decide how good it is
The clerk in that example has to be willing to hand over what she knows, and that is harder than it sounds. At Xerox, technicians in districts that were judged against each other held their tips back. What worked was a system where a tip, once checked, carried its author's name, so sharing made a technician known across the company.
An assistant fed by people who think it will make them replaceable gets thin notes and gives thin answers. So the companies that get a three-month start for new hires will be the ones that treat veterans as authors: credited, paid for the time and asked what they check first and why.
You can start without any AI. Pick the ten problems your best person is called in for most often, and ask them to write down what they look at first and why, with their name on it. That page is useful to the next hire on its own, and it is the raw material for an assistant when you build one.