Why AI training is not being applied to actual work
This is something I see pretty regularly. When organizations deploy AI training, completion rates are fine, and six months later, employees in the field are using the tools exactly as they were before taking the course. That’s not it at all. The training worked. This is not designed to change the way certain people work. There are three design decisions that explain most AI training failures. None of this is obvious when building a course. All of them are fixable once you know what the problem is.
The scenario is written for the wrong people
Most AI training scenarios are built around desk jobs. Someone reviews documents, drafts emails, and summarizes meetings. AI helps with that. Are you okay.
Now imagine an agency salesperson standing at a counter whose job is to tell contractors what products to use on their jobs. Or, a painter is wondering which coating system will hold up to outdoor wood in humid climates. Those people have AI training and are clicking through scenarios to summarize the project proposal, but what they’re doing is mapped to the actual day.
This is like teaching a child to drive a compact car by only teaching them how to parallel park, when the actual vehicle they are driving is a full-size pickup truck. Skills are involved. AI training fails because lessons are difficult to understand because the context is so disconnected.
A few weeks ago I came across a great example of what proper design looks like. I was using Claude as a live tool to run audio classroom sessions. This wasn’t a topic we were talking about, it was something we were actually using together in the room. One of the students was in a band and was having trouble getting a reservation at a local bar. So instead of doing the standard AI immediate response exercise, we used that problem. Claude played a bar owner who had a certain hidden reason for not booking the band. The students didn’t know that until they entered the store. Students had to actually have a conversation with this character, understand what the hesitation actually was, and pitch them towards a trial booking.
He finally arrived there. And what he learned from reading resisting customers, adjusting the pitch of his voice, and not giving up even when the first answer was “no” was directly applicable to real bar owners and real rooms. AI was not a demo. It was a practice partner whose role matched his real world.
That’s the difference in design. Not a typical office situation, but a real problem that this particular person is trying to solve by making a bet that makes sense to him.
practice takes place in the wrong place
Consider how trade workers physically learn something. Finishing technicians don’t watch videos and go to the job site to learn how to use a new spray system. They learn it on the job, next to the surfaces they are painting, and have real results for which they are responsible. Skills and context are formed simultaneously. This does not interfere with classroom learning. It’s just the extent to which skilled manual labor is actually mastered.
The same problem exists with the use of AI tools. The habit of checking tools at specific steps in a workflow is not formed within a learning management system. It’s formed when you practice it over and over again with real steps in real workflows, repeating it until it no longer feels like a new behavior.
Most eLearning isn’t built that way. Training modules exist independently and independently of everything else. You complete it, you go back to work, but the habit has nowhere to go because you never practiced it where you actually work. For people who spend their days in front of a computer, the gap is smaller and they can usually close it themselves. Most people, even those who spend all day on their feet, don’t.
The solution is to make practice feel like real work. If you use this tool when creating a quote, you should practice it in the atmosphere of creating a quote, not in a blank prompt field on a white background. The closer the practice context is to the actual workflow, the more likely the habit will actually stick.
No one tells learners when not to trust tools
Think about how you decide when to trust GPS navigation. You haven’t taken the course. You followed it into the construction zone or routed out of the way. And I learned how to override it for such situations. Trust is conditioned through small failures in moments that don’t cost that much. And thanks to them, we know when to follow them and when to make our own decisions.
Employees in industries that use AI tools through formal training programs lack such experience. They get one confidently wrong answer, such as a product specification that doesn’t match what’s on the label, or a part number that looks correct but isn’t, and the tool is discarded before it can get the credit it deserves. It’s not because they’re unreasonable. Because they apply the same standards they apply to any professional information source. If you give me bad information without flagging it as unsure, I probably won’t ask you again. Honestly, this is a fair standard to hold. The training wasn’t giving them the low-risk failures they needed before reaching that failure.
The training industry is primarily concerned about the opposite: that learners will trust AI output too much. That’s a big concern for some viewers. In my experience with industry and industry learners, failure often goes in the opposite direction. One wrong answer early on will kill the tool before it can produce any legitimate results.
The solution is to incorporate adjustment exercises into your training before the first actual failure occurs. Deliberately provide the learner with incorrect AI output in a way that is consistent with how the tool actually fails on this type of task. It’s not obvious nonsense, but a subtle error that makes it seem plausible. Ask them to find out what is wrong and think about how to check it. This requires more design work than standard modules. Because constructing a plausible wrong answer requires a good understanding of the domain, and someone needs to review it. That’s the real cost. Instead, you have learners who either trust everything or trust nothing, but they’re not paying for either.
The common denominator in AI training is failure.
All three of these AI training failures stem from the same place. The course was initially designed without anyone sitting with real learners in a real workflow. That afternoon changes what you build. Without this, training will be completed on schedule and nothing will change on the floor.
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