AI Training For Employees is Lacking

I’ve noticed a few problems with AI training for employees.

Before I go deeper, I should mention that its great firms ARE doing training, and the speed of generative AI adoption is fantastic.

Most employees have had some level of AI training. And most are experimenting with it in some form. I met a senior lawyer the other day who was vibe coding with Claude Code. She was also getting a Mac Mini so she could try OpenClaw. That kinda blew my mind.

On the other hand, I see some really obvious issues.

1: Vendors Are Biased at AI Enablement

First, a lot of folks are just doing the standard vendor training. Its great that vendors are offering this, but keep in mind that most vendors have an agenda: they want you to buy - and use - more of their stuff.

All the little decisions they make thereafter in regard to you happen with that in mind. What frameworks does the sales engineer learn? What are the customer success manager’s KPIs? (Many will say “to help you succeed” but I guarantee their manager is asking them each week how your renewal / upsell / implementation is going).

I’ve also heard more than a few professionals say they felt the vendor did not understand their domain very well and it seemed more of a box-ticking exercise.

2: Safety isn’t properly addressed in most AI training for employees

There’s a ton of research on risks of AI use. Cognitive atrophy of users. Loss of ownership. Training tends to only partially cover this. The most pernicious hazard of LLMs - which some refer to as “a feature” - is hallucinations.

AI training for employees, in my experience, rarely goes into this deep enough. The solution, all too often, is “check AI outputs.” Which, if you’ve read the Pinsent Mason’s case (or the hundreds of others like it), doesn’t solve the issue.

The problem is understanding how LLMs actually work. I’m not going to cover that because there’s 1000s of videos and posts on it - more just the side effect. The machine predicts the most likely acceptable answer. As we all know from math, when a system predicts the most likely outcome, a % of the predictions will be wrong. Which in turn leads to bad answers, and the LLMs unfortunately confidently state this as fact.

Referring back to point 1, I think sometimes in the industry we’re guilty of overhyping the tech - “they’ll get more accurate over time” (this is somewhat true tbf) or even “they should have used a better model / tool.” Referring back to the Pinsent Mason’s case in #1, the users were actually using a top-shelf solution. Which brings me to my next point.

3: The standard AI story for non-techos is confusing

AI will take your jobs. But you should use it at work.

AI is dangerous. But you can use it to help you complete complex - even high stakes - initiatives.

AI is super powerful and can make you more efficient. But you need to babysit it to make sure it doesn’t screw up simple tasks (its your fault if it does).

If you want people to get onboard with a new way of doing things, the story has to be compelling for them.

4: AI users are not created equal

Several data points suggest that AI adoption is much faster in some fields than others.

Anthropic’s own Economic Index report in 2025 shows very high usage among software professionals and some reasonably high usage among creatives. Workday’s 2026 Future of Work report showed AI productivity gains (once rework has been factored in) are much higher among IT employees.

5: There are serious compliance and reputational risks

This isn’t the most exciting topic, which is perhaps why its often overlooked.

In just 2 months, the EU will start enforcing the EU AI Act. What that actually looks like (in terms of fines) is unclear. However, they have the OPTION to fine you millions of euros (minimum) for breaches.

Most people aren’t worried about the Act because they think “well I’m not an AI vendor, and I’m not making killer robots or totalitarian surveillance so I’m cool, right?” Others in countries like the UK or US will say “well, I’m not in the EU.”

The messy bit is the High Risk Use cases, and the Deployer rules.

Under the Act, if you use AI in your business, you are likely to be considered a Deployer. Deployers are liable under the Act. I keep thinking of the analogy of self-driving cars: if you’re in a Tesla and it mows down some people on Autopilot, the person in the driver seat is liable.

High Risk cases are more common than you’d think. The most obvious to me is anything to do with “screening.” If you are using AI to screen loan applicants, or job candidates, or help grade students’ answers… you’re probably High Risk. Which means you can expect more scrutiny by the regulators.

OK, so, what does good AI enablement look like?

AI training needs to include safety considerations - the actual risks (vs the FUD). Hallucinations - and how to avoid them. Cognitive atrophy and forcing functions (I’m not talking about code) to prevent that.

Good AI enablement means being realistic with your team. What are the things the tool will be awesome at? What will it be trash at?

You also need to look at your existing processes. Building in ways to ensure that your staff don’t make mistakes easily. Sometimes that’s a custom tool, like a case law citation checker for immigration lawyers. Other times its reviewing KPIs and the job descriptions to factor in new ways of doing things. In some cases it might even mean revisiting your business model.

As with change management in general, the most important thing is to meet folks where they’re at, and make sure your training is highly tailored to that audience. These seem like things that are obvious in a philosophical sense, but hard to do. There’s a lot of material out there on adult learning; behavioural change and enablement. Many people know what _bad_ enablement looks like - the good stuff is harder to spot.

For what its worth, doing _some_ training and support is helpful. Google’s own research found that a 2H investment in AI training for employees is likely to yield a 2X increase in AI usage. I’ve seen similar stats from other players who aren’t necessarily vendors.

We’re piloting all the above - and more - at monboard. Head over to our services page to find out more - or drop us a ping on the Contact section.