AI can explain culture in 30 seconds. So what is left for the trainer?

An Australian manager notices that her team in India keeps saying “yes” to deadlines that are later missed. A German engineer becomes frustrated with what he sees as vague communication from colleagues in Japan.

Ten years ago, they might have attended a workshop to explore these questions. Today, they can ask AI and get a reasonably good explanation in less than a minute. And why wouldn’t they? The information is available exactly when they need it.

This raises an uncomfortable question for those of us working in learning and development:

If knowledge is available on demand, where does training still create value?

Over the past few years, many of us have noticed a common trend. Fewer people sign up for training, and fewer of those who do actually turn up. Clients tell me this isn’t limited to cultural agility. They are seeing it across leadership, communication and other workplace learning programmes.

It’s tempting to conclude that people no longer value learning. I don’t think that’s true. I think the value of learning is shifting. As AI makes knowledge increasingly accessible, the value of training lies less in helping people know more and more in helping them do something with what they know.

Information is not capability

Let’s return to our Australian manager.

She asks AI why her team might be agreeing to deadlines they later struggle to meet. Within seconds, she is given several plausible explanations: concern about disappointing a manager, different attitudes towards hierarchy, indirect communication, or uncertainty about how risks should be escalated.

The answer may be useful. But information is not capability. Knowing several possible explanations is not the same as knowing which one applies. It’s not the same as having the conversation. And it’s certainly not the same as building trust and changing outcomes.

AI can explain cultural frameworks, suggest alternative interpretations and even help rehearse a difficult conversation. What it cannot do is develop judgement on our behalf. Judgement develops through reflection, feedback, experimentation and experience. It develops when we test our assumptions, discover where we are wrong and learn how to adapt our behaviour.

Competence is not created by information alone.

What does this mean for training?

If training is primarily about transferring knowledge, AI will increasingly do much of that work faster, cheaper and on demand.

But if training is about helping people make sense of complexity, challenge assumptions and respond more effectively in real situations, then the need for learning has not disappeared. In some ways, it has become more important.

The challenge in intercultural work is rarely a lack of explanations. The challenge is deciding which explanation is most useful, what additional questions need to be asked and how to respond appropriately. Knowing is not the same as doing.

What we’re experimenting with

This thinking has led us to rethink how we design learning experiences. Rather than building longer programmes around concepts and frameworks, we’re experimenting with shorter, scenario-based workshops focused on application.

For example, participants might be presented with a missed deadline involving a multicultural project team. Working through the scenario, they explore different interpretations, challenge their assumptions and consider alternative responses as new information emerges.

The aim is not to teach a framework for its own sake. AI can explain a framework perfectly well. The aim is to help people recognise patterns, ask better questions and make better decisions when situations are ambiguous.

The scenarios are tailored to the client’s reality, using familiar roles, real business challenges and countries participants actually work with. People need to recognise themselves in the story before they can learn from it.

The workshop is not the learning

One thing has become increasingly clear. The two-hour workshop is the event, not the learning. Learning happens afterwards, when participants face a similar situation at work and decide what to do differently.

That’s why we’ve started building in pre-work based on real workplace challenges and follow-up sessions where participants reflect on what they tried, what happened and what they learned.  The goal is to create a bridge between understanding and application. Because that bridge is where capability develops.

The role of AI and coaching

Interestingly, AI is not only disrupting learning. It can also support it. Participants can use AI to prepare for meetings, explore alternative explanations or rehearse difficult conversations between sessions. In that sense, AI can become a valuable practice partner.

But AI’s strength is generating possibilities. The challenge is deciding which possibilities deserve attention. That’s where coaching remains important. A coach can question assumptions, test interpretations and help someone navigate the specific context, relationships and constraints that AI cannot fully see.

The goal is not to find the correct cultural explanation. The goal is to develop the judgement to ask better questions and make better decisions.

The opportunity ahead

I believe AI is forcing learning professionals to confront an important question:

If knowledge is becoming abundant, what unique value do we create?

For me, the answer is becoming clearer. Let the 30-second answer do the explaining. Our role is to help people make sense of it, practise with it and apply it.

As AI makes knowledge increasingly accessible, the value of training shifts from knowing what to do to being able to do it.

Because understanding something in thirty seconds is one thing. Knowing what to do with it on Monday is another.