Organisations are investing more than ever in AI upskilling — courses, certifications, internal academies, external experts brought in to run workshops.

And that’s a good thing, because AI fluency is no longer optional, it’s essential.

But there’s a common frustration. People complete the training, attendance is high, feedback surveys score well, certificates are issued. Yet daily behaviour barely shifts.

Three months later, most people are back to doing things exactly as they did before the workshop, only now with one more AI tool icon sitting on their desktop that they rarely open.

The issue isn’t that the training doesn’t work, it’s that training alone isn’t enough. Real AI capability is built through a combination of:

• Structured learning, courses, prompting techniques and tool walkthroughs.
• Practice on real, current work, not sandbox exercises with fictional data, but this week’s actual proposal, actual report, or actual customer problem.
• Reinforcement from leaders who use AI visibly themselves, not just approving the training budget, but opening the tool in front of their teams and thinking out loud with it.
• Systems and incentives that reward the new way of working, performance conversations, promotion criteria and team goals that recognise how people use AI, not just whether they achieve the same outcomes in the old way.

When any one of these elements is missing, progress stalls. If people learn new tools but return to a team that still rewards the old way of working, change will be limited.

Imagine someone finishes a two-day AI workshop feeling energised, tries a new approach on Monday and is quietly told by their manager to “just do it the normal way. We don’t have time to experiment.”

The lesson lands quickly: the training was decorative.

If leaders talk about AI but never open a prompt in front of their teams, adoption fades. People notice the gap between what leaders say and what they actually do far more than what’s written in a training deck.

If there’s no opportunity to practise on real work, capability never leaves the training room. Skills exercised only in workshop environments, disconnected from real deadlines and real stakes, fade almost as quickly as they were learned.

This doesn’t make the training ineffective, it highlights something more important: AI capability needs to be connected to how work actually gets done and how performance is measured.

The most effective organisations don’t separate AI learning from performance. They embed it into the system. They:

• Link AI use to real business problems, not generic use cases, a workshop built around this quarter’s actual pipeline review, not a hypothetical case study.
• Hold leaders accountable for modelling AI, not just mandating it, making visible AI use a genuine part of how senior leaders are expected to lead.
• Create space for people to practise on live work, safely, giving them permission to get a first attempt wrong without it counting against them.
• Redesign performance evaluation so that judgement about when and how to use AI becomes an explicit part of what “good” looks like.

Consider two organisations that invest exactly the same amount in AI training this year.

In the first, the head of learning treats the budget as the deliverable: courses purchased, seats filled, satisfaction scores collected and a tidy report presented to the board. Ninety days later, no one asks what has actually changed in the way work gets done.

In the second, the training budget is only one part of the investment. The larger investment goes into redesigning key workflows around the new capability, coaching managers to reinforce AI use during weekly check-ins, and adjusting performance reviews to reward new behaviours.

By year-end, both organisations can report identical training completion rates. Only one will have meaningful business results to show for it.

Because AI capability isn’t a training event. It’s a leadership system.

And when that system is aligned with how work actually happens, AI becomes more than another technology initiative. It becomes a lasting competitive advantage, one that compounds quarter after quarter instead of fading by the next one.

Arinya Talerngsri is Senior Vice President, Local Partner and Managing Director at BTS Thailand, part of the BTS Group, a leading global strategy implementation firm. She is passionate about revolutionising education and creating opportunities for Thais and people worldwide. Executives and organisations looking to collaborate or learn more about leadership and talent development, succession planning and organisational transformation can contact her at arinya.talerngsri@bts.com or visit her LinkedIn profile.