Does AI strengthen or weaken action learning?

As AI generates ever more answers, business schools may need to prize judgement as highly as knowledge. 

Generative AI can now produce a plausible answer to almost any management problem in seconds. For those of us involved in action learning, this raises a pointed question: what happens to leadership development when intelligent answers are available on demand? 

Action learning has never been about the provision of answers. Since the pioneering work of Reg Revans, it has been built on the combination of programmed knowledge and questioning insight, developing reflection, challenging assumptions, and cultivating judgement through structured inquiry. The paradox is simple. As answers become easier to access, the ability to ask meaningful questions becomes more important, not less. 

The original promise of action learning 

In practice, participants present real workplace challenges to their peers. Instead of receiving advice, they are asked questions. Through careful inquiry, they often discover that the issue they initially presented is not the issue they need to address. 

Those who have experienced action learning will recognise that its greatest value frequently lies in reframing a problem rather than solving it. A manager may arrive seeking a strategy for dealing with an underperforming team member and leave with an entirely different understanding of their own leadership behaviours. Answers are important, but insight often matters more. 

What AI does exceptionally well 

There is much to celebrate about generative AI. Used thoughtfully, it can support leadership development in meaningful ways. A learner can ask an AI tool to summarise a leadership theory, identify risks, or suggest approaches to a challenge, accomplishing in minutes what once took hours. It can also support reflective practice by identifying themes within written reflections, generating coaching-style questions, and prompting deeper thinking. We should resist treating AI as an educational threat to be excluded from learning environments. 

But AI's strengths expose a real weakness. The danger is not that it gives poor answers, but that good answers may encourage learners to stop thinking too soon. 

The risk of lost inquiry 

Much of leadership development involves productive struggle: learning that emerges from uncertainty, ambiguity, and discomfort. Leaders routinely confront competing priorities, conflicting values, political tensions, limited information, and human dynamics. Action learning helps participants remain within that uncertainty long enough to develop insight, examining assumptions they had previously taken for granted. AI may inadvertently short-circuit this process. 

Consider a participant arriving at a set, the small peer group at the heart of action learning, with concerns about declining staff engagement. Within seconds, an AI tool can produce ten credible interventions drawn from established practice. It may be helpful, but it risks bypassing more important questions: 

• Why is engagement declining? 

• What assumptions am I making about motivation? 

• How might my own leadership behaviour be contributing? 

• What organisational dynamics am I overlooking? 

The shift from ‘What should I do?’ to ‘How am I understanding this problem?’ is often the most important learning moment in the room, and it is precisely the shift that gets skipped when solutions arrive instantly. 

Why human judgement remains central 

Many of the most important dimensions of leadership remain stubbornly human. Leaders must navigate ethical dilemmas, interpret context, build relationships, manage conflict, and decide under uncertainty, tasks that cannot be reduced to information retrieval. 

An AI platform may suggest several plausible courses of action, but choosing between them requires an understanding of culture, stakeholders, power, and consequences. It requires practical wisdom rather than computational capability. As information becomes abundant, the distinctive contribution of business schools may lie less in delivering knowledge and more in developing judgement. 

Towards AI-enhanced action learning 

The answer is not to exclude AI from action learning, but to be deliberate about where it sits. One useful principle is to position AI as a resource before and after the set, but not during it. 

Ahead of a meeting, a participant might use AI to map the stakeholders in their situation, surface their own assumptions, or organise scattered thinking, improving the quality of what they bring to the group. During the set itself, the focus stays on questioning, listening, and collective inquiry, with participants sitting with uncertainty rather than rushing to resolve it. 

Afterwards, AI can support action planning, reflective journaling, and synthesising what was learned. This preserves the developmental value of action learning while harnessing AI as a tool for reflection rather than a substitute for it. However, facilitators should be candid that phones in pockets make a hard boundary difficult to enforce, and the norm may matter more than the rule. 

A challenge for business schools 

The growth of AI invites us to reconsider what we are developing in our learners. 

• Are we rewarding the production of answers or the quality of questions? 

• Are we creating space for reflection in increasingly crowded curricula? 

• Are we helping students engage with ambiguity rather than simply resolve it? 

The future value of management education is unlikely to rest on access to information alone; AI has already settled that question. It will rest instead on our ability to cultivate curiosity, empathy, and critical inquiry. In an age of intelligent answers, the enduring purpose of action learning is to remind us that transformative leadership development still begins with a thoughtful question.