Being a Learning Organisation Means More Than Good Intentions

In their Harvard Business Review article, David Garvin, Amy Edmondson, and Francesca Gino use the phrase ‘learning organisation’, to define organisations that do not just talk endlessly about growth, curiosity, and innovation; but rather, they lead tangible change because they have a consistent learning mindset. In their article, they suggest that these types of organisations are built on, and require, three foundations: a supportive learning environment, concrete learning processes and practices, and leadership that reinforces learning.

The authors also discuss the importance of factors such as psychological safety, openness to difference, experimentation, information gathering and analysis, education, and reflection, as key attributes that support the approach and learning mindset.

I love this article, and these findings resonate with me – not only because of my experience as an employee (I’ve got experience of working in both learning organisations and the whatever you want to call the opposite! 😊) – but more specifically from what I’ve seen in of the hundreds of organisations I’ve been lucky enough to work with and in over the past 7 years.

The idea of a learning organisation matters in my world, because I reckon plenty of leaders and teams like the idea of being a learning organisation, but far fewer are willing to behave like one when it counts. For example, it is easy to say you value learning when things are stable; however, it is much harder to prove it when a decision underperforms, when a strategy has to be questioned, or when the data tells you something inconvenient. Those hard, or tricky moments are the real test. A learning organisation is not one that collects insights as a performance and confirmation to continue doing what they are doing… It is one that is willing to let those insights interrupt its certainty.

If your organisation wants to learn, it needs to take both pre-decision and post-decision data seriously. Pre-decision data helps you decide what to do. It includes the information you gather before acting, such as baseline measures, stakeholder perceptions, customer feedback, operational trends, past patterns, risk indicators, and the assumptions sitting underneath your preferred course of action. Post-decision data is different, because it happens AFTER the decision and change has started, and it helps you understand whether the decision worked, for whom, under what conditions, and at what cost.

The problem is that many organisations are far better at using data to inform and justify a decision than they are at using data to interrogate the effectiveness of one. And I think that should make us uncomfortable. If your team gathers data only before a decision, you may be informed, but you are not necessarily learning. Or if you gather data after a decision, but treat it as a scorecard instead of a source of insight, you are not learning either – you are just measuring.

A genuine learning organisation needs, and actively pursues both pre- and post-decision data, but the core defining feature is that they learn from the data and adapt. They do not continue doing what they’ve always done - they do things differently.

We need valid and reliable pre-decision data because good learning does not start with action alone – it starts with disciplined curiosity. At this stage we can ask questions such as: What do we know? What do we not know? Whose perspective is missing? What assumptions are we making because they are comfortable, familiar, or politically convenient? And that kind of data and questioning supports one of Garvin, Edmondson, and Gino’s central ideas: learning organisations have concrete learning processes and practices, including information collection and analysis.

But we also need post-decision data because learning is not complete at the moment of choice. It only becomes real when people examine consequences of the actions honestly enough to adjust behaviour and approaches into the future. That requires reflection, experimentation, and leaders who reinforce learning rather than punish bad news. Without that, teams become performative; they talk about agility while quietly contradicting themselves and rewarding consistency. They claim to value evidence but they ignore or manipulate the data that complicates the story.

So perhaps the better question is not whether your organisation says it values learning… But what happens when the post-decision data disagrees with the decision, the leader, or the plan? When this happens, do people feel safe enough to name what is not working? And are different views genuinely invited, or merely tolerated? Do leaders model reflection, admit uncertainty, and change course publicly when needed? Those are the kinds of signals that reveal whether learning is embedded or merely advertised.

A learning organisation is not one that knows the most – it is one that notices, examines, and adapts the best. And in that kind of organisation, data is not just a decision-making tool, it is the mechanism through which learning becomes visible.