What's so funny 'bout context, knowledge and understanding

Most organisations accept that people learn more through their work than from any training programme. The evidence for this is not contentious. What has remained structurally unresolved is how to design for it. Giving AI tools a connective layer into institutional sources is not an answer to that question on its own. For the first time, though, there is a practical mechanism that can touch all three layers at once: environment, social practice, and wiring. Understanding why requires clarity about what those layers actually are.

We frame a learning ecosystem across three interdependent layers. They are not a hierarchy in the sense that one supersedes another. They are mutually constitutive, each layer either amplifying or undermining the others.

The environment layer

The Environment Layer is where what your culture actually rewards lives. It encompasses how feedback is structured into work processes, what behaviour is rewarded versus merely described in values statements, how decisions are made and made visible, and how mistakes are handled in practice. Organisations rarely design this layer consciously. But it is doing the heaviest teaching. If deadlines consistently override quality, people learn to optimise for speed. If dissent carries social cost, people learn to stay quiet. None of this requires a training budget, and none of it can be counteracted by a programme alone, because the environment keeps teaching its own lessons after the workshop ends.

The social layer

The Social Layer is where collective sensemaking happens. This includes shared experiences that create common reference points, structures that make disagreement productive rather than costly, and the deliberate protection of time for dialogue. Teams do not coordinate through everything each individual knows. They coordinate through what they can reasonably assume others recognise. Remove the social dimension and learning becomes efficient but shallow. Behaviour may change; judgement does not.

The technical layer

The Technical Layer is the infrastructure that makes institutional knowledge easy to reach and ties learning evidence to performance where that is appropriate. It includes wiring trusted sources into AI tools, sensible knowledge organisation, and honest measurement of how people engage with work. Critically, this layer is designed to serve the other two. It does not lead. When organisations treat that wiring as primarily a productivity shortcut rather than part of the ecosystem, they reproduce the usual AI outcome: individually faster tasks and collectively thinner sensemaking.

The neuroscience is relevant here and consistently underused in L&D design. Immordino-Yang, Nasir, Cantor, and Yoshikawa (2022) describe a principle they call sociocultural embeddedness: development and learning happen in relation to the norms, values, and expectations surrounding us across the many contexts we live in, even when we're working alone. Our work is woven with the work of others, and as we develop, we act on and change those contexts in turn.

The implication for the Environment Layer is structural, not motivational. Knowledge and skill are not stably stored in a person like a resource ready for deployment. They are dynamic potentiations enacted as active adaptations within situations. What someone can understand and do in any given moment is a function of both their prior experience and the current context, including its social, cognitive, and affective dimensions. A person can demonstrate complex reasoning in a context they feel agentic and comfortable in, and fail to access the same reasoning in a context they interpret as unsafe or irrelevant.

The same competency, expressed by the same person, varies with context in ways that skills taxonomies do not capture. The Environment Layer cannot be treated as background to the learning intervention: it is the primary determinant of whether learning transfers into performance at all.

Technical wiring's relevance to the Environment Layer is therefore conditional: it depends on the environment being configured to make surfaced knowledge useful rather than threatening. An organisation in which what your culture actually rewards punishes admitting uncertainty will not benefit from AI tools that surface competing decision frameworks. The environment must already reward the kind of reasoning the Technical Layer is meant to support.

George Siemens' Connectivism framework (2005) argues that in distributed, network-rich environments, learning is less about what an individual stores and more about the quality of the connections they can traverse. Knowledge is distributed across people, artefacts, databases, and tools. Competence is the capacity to navigate those connections effectively, to recognise which nodes are relevant, access them quickly, and integrate them into action.

At the Social Layer, Connectivism explains why shared experiences are not inefficiencies to be optimised away. They are the mechanism through which nodes in the network become mutually legible. Two people can have access to the same documentation and still be unable to reason together, because the network between them lacks the shared reference points that make disagreement productive. Common cases, contested decisions, collective reflection: these are not social niceties. They are the connective tissue of organisational intelligence.

At the Technical Layer, a server that indexes an organisation's decision histories, frameworks, and institutional know-how makes the knowledge network traversable at the moment of need. When a person working in their AI tool queries a problem and receives contextually relevant organisational knowledge, they are activating a node in a network the organisation has deliberately maintained. The connection strength depends entirely on how well that knowledge has been curated, what the Environment Layer rewards, and whether the Social Layer has kept it current and contested.

This is where Connectivism illuminates the risk of wiring-only deployments: a read-only relationship with organisational knowledge does not develop the capacity to contribute to or revise it. The Technical Layer can serve learning, or it can weaken the Social Layer by making individual access so efficient that collective sensemaking feels unnecessary. The difference is in the design.

The three layers do not operate sequentially. They are a complex adaptive system in which each layer generates feedback that modifies the others. This is where the Immordino-Yang framework of emergent holism becomes important.

The principle of emergent holism holds that the subsystems of humans and their contexts function not additively but mutually constitutively, giving rise to emergent potentials that are partially unpredictable from the sum of their parts. In organisational terms: collaboration is not an individual trait that can be trained in isolation. It emerges from team dynamics. Psychological safety does not live inside a person. It exists between people. Systems thinking emerges when the environment rewards cross-boundary reasoning, not when someone completes a module about it.

Immordino-Yang et al.

Applied to the three-layer model: wiring that surfaces high-quality institutional knowledge (Technical Layer) in an environment where that knowledge is actively discussed, contested, and updated (Social Layer), within an organisation that rewards using and contributing to institutional knowledge rather than hoarding it (Environment Layer), produces qualitatively different outcomes than any single layer could achieve alone. Capability grows in the interactions between layers, not in the optimisation of any one of them.

The most valuable contribution of the learning function is not controlling the small proportion of learning it directly delivers. It is influencing the far larger proportion that emerges every day through the conditions of work. Solid technical wiring offers a channel through which that influence can run closer to real time. Whether organisations use it that way is a design question, not a technology question.