Why most AI training programs don’t work (and what to do instead)

There’s no shortage of AI training courses out there. From slick e-learning modules to multi-day workshops, the market is full of options promising to ‘upskill your team for the AI era.’

But talk to the people who attend these sessions and you’ll hear a different story: confusion, frustration, and a nagging feeling that none of it really sticks.

As one Reddit user bluntly put it:

“I took an AI course, but it was all math and no real-world application. I still don't know how to use AI in my job.”

Here’s the uncomfortable truth:

AI training is broken.

And for executives looking to make progress on digital transformation, that’s a bigger problem than it might seem.

What’s going wrong?

1. Too much theory, not enough application
   Most training focuses on how the tech works under the hood. Great for data scientists – useless for your sales, ops or finance teams. What people actually need is “What does this mean for my job?” and “How can I use this tomorrow?”
   Too many times I’ve seen people turn up to show off some amazing AI tool, and show an amazing output - but not spend the time to show how you actually create it, step-by-step. It’s like somebody just invented the pencil, and they show you this amazing Mona Lisa drawing they’ve created with it. Great, but when you get it home, the best you can get is stick figures

2. Generic advice, no business context
   AI is not one-size-fits-all. What’s useful for a retailer isn’t useful for a bank. Most training doesn’t adapt to sector or role, so learners come away with abstract knowledge, but no clear next step.
   Almost without exception, whatever group I’m talking to will hear about how AI is being used in their sector, and in organisations like theirs. It’s not difficult to find the right context and examples - there are thousands of case studies - but it is time consuming to prepare and make the context fit the trainee.

3. No follow-through
   Even when people learn something useful, there’s little support to help them apply it. It’s like sending someone to a cooking class, then never giving them a kitchen.
   I’ve trained teams where they weren’t even given access to AI tools after the training. Getting people motivated to experiment with AI is a key goal of any AI training (or should be!), so what a waste of time and money to not enable the experimentation

4. Outdated content in a fast-moving world
   With new AI tools launching every month, most course content is stale before it hits the LMS. The result? A disconnect between what’s taught and what’s possible.
   When I’m delivering face-to-face training I will check on the morning what’s happened overnight and whether any of it is relevant to the trainees. Ask me to provide my slides a month in advance? Hah! You’re guaranteeing that the training will be out of date. And imagine the lead times for a typical course in an LMS. While 90% of the content might still be up to date two months later, the 10% that needs to be bang up to date is so important to your learners.

5. No link to strategy or decision-making
   AI skills mean nothing if they’re not tied to business goals. Without clear alignment to priorities like customer growth, operational efficiency or compliance, AI remains an experiment rather than a driver of transformation.
   I once ran a workshop for a management team who asked “Why are we here? We aren’t allowed to use AI”, and yet it was the CEO that put the program in place. Having a strategy well communicated is also clearly helpful!

So what’s the alternative?

The most effective organisations are shifting away from standalone training and towards structured, applied learning experiences that directly support business outcomes.

Here’s what that looks like:

• Start with real business problems, not AI features
  Help teams identify friction points, inefficiencies, or risks they already face – and frame AI as one of several tools that might help solve them.
  In my experience, the team will need some training in AI to be able to identify the opportunities to use AI, so this is something that needs to get built into the training, not done beforehand

• Use methods like pretotyping and prioritisation frameworks
  Let teams experiment with low-cost, low-risk ideas before scaling. This builds confidence and de-risks the investment.
  I love pretotyping, and AI allows us to do much more of it really quickly and in a way that stays agile and keeps people engaged in the end goal

• Focus on practical fluency, not technical depth
  You don’t need everyone to become prompt engineers. You need people who can identify good use cases, ask smart questions, and lead responsible implementation.
  And you need people with a balance of IQ and EQ. Not just one or the other

• Embed AI into the rhythm of work
  Use coaching, live projects, reflection tools and AI copilots to help people apply what they learn as they go.
  AI training simply can’t be “one and done” - you need to create a community that enables and encourages continuous learning

Final thought

AI isn’t just a technical shift – it’s a cultural one.

If your teams are disengaged from AI training, the solution isn’t more training. It’s smarter, more contextual, more strategic support.

Because in this landscape, it’s not the most AI-literate companies that will win.

It’s the ones who know how to turn AI understanding into business impact.