The AI training mistake costing businesses the biggest return

Businesses are investing more in AI than ever before, and boards expect to see measurable returns. At the same time, growing AI usage and rising costs are putting every licence, pilot and technology investment under scrutiny, making effective adoption more important than ever. That’s why AI training plays a bigger role in that than many organisations realise.

A single AI training workshop delivered to every employee may introduce the technology, but it doesn’t prepare people for the different decisions they make or the challenges they face every day. And when training isn’t aligned to individual roles, adoption slows, confidence drops and the return on investment becomes much harder to achieve.

In this article, we’ll look at why role-specific AI training delivers better business outcomes and what organisations should consider when building an AI capability that lasts.

In this article
By the end of this guide, you’ll understand:
> Why many AI training programmes fail to deliver measurable business value.
> How generic training slows adoption across different teams.
> The different AI capabilities leaders, technical teams and frontline employees need.
> Why confidence is one of the strongest indicators of successful AI adoption.
> The key ingredients of an AI training programme that supports long-term business outcomes.
> How to connect AI training to operational goals and return on investment.

More AI investment doesn’t always mean more business value
It’s safe to say, organisations are investing heavily in AI. New tools are being rolled out, licences are being purchased and teams are being encouraged to explore new ways of working. Yet many businesses are still wondering: Where’s the return?

Providing employees with access to AI tools doesn’t automatically change the way they work. Some teams quickly find ways to improve productivity or automate routine tasks. Others use AI once or twice before returning to familiar processes, while some avoid it altogether because they aren’t sure how it applies to their role.


Recent research highlights the issue. According to McKinsey, 88% of organisations now use AI in at least one business function, yet many are still working towards organisation-wide adoption rather than consistent business value. 

Similarly, research from EY shows that while 88% of employees say they already use AI at work, only 12% feel they receive enough training to use it effectively. 

That’s why for many organisations, the focus now is on helping people use it confidently, consistently and in ways that deliver measurable business outcomes.

The biggest training mistake businesses keep making
Organisations approach AI training with the best intentions. A workshop is organised, employees attend, everyone leaves with the same slides, the same prompts and the same examples.

The difficulty comes later.

People across an organisation make different decisions, solve different problems and use different systems. A finance manager won’t use AI in the same way as a customer service adviser, and neither of them need the same level of technical understanding as an IT team. Expecting one training programme to meet every need makes it much harder for employees to see how AI fits into their day-to-day work.

The result is often inconsistent adoption across the business: