Learning Fragments: Fracturing the Learning Function

Today i am reflecting on a systems view of learning within Organisations, to ask if the shifts and trends we see have negated or invalidated the structural model of Organisational learning at scale. The premise is relatively simple: we build out systems that serve a need, but as the need changes, at what point do we evolve or dismantle the system? Some change can be met through adaptation, but sometimes the structure can inhibit our evolution. I’ll start by considering some (by no means a definitive list) of the changes, then move onto consider how this emergent context impacts the design and support of learning within, and beyond, Organisations.

Systems (Organisational and social) reflect need, or more precisely can be viewed as a shadow of need. A?shadow?that is cast into the future. So our journey up to this point shapes our shape and structure going forward: change in what we need inevitably falls into the shadow of the old, hence why agility, the ability to re-author the structure, matters. But sometimes change within a system (call it evolutionary) is not enough: we may need a more fundamental and structural shift, one that crosses the legacy domain based structures of Organisational design.

There is a complication around this in that the systems of the Organisation are surrounded by feeder structures of society: systems of education, residency, distribution, support, all of which themselves not only serve the structure but perpetuate it.

So let’s consider this in terms of learning: what’s changed is the relationship we have with?knowledge?(codified, validated and fixed towards socially co-created, mixed validation – and contextual validation – and dynamic), our view of capability (individual to collective, and enhanced through tech), the location of learning (defined by time and place to ‘in the flow’ and distributed), aspects of power (learning as a spoon fed function to democratised and distributed learning – learning as directed to learning as exploration), learning as owned towards learning as permeable feature of community, and learning insights being human derived to?AI?derived.

In practice, this means that we have learning functions that are rooted in infrastructure: originally classrooms, but latterly online spaces, and probably in a ‘meta’ future we will pivot back to infrastructure (headsets and devices). Latterly?learning?has moved to be more agnostic of infrastructure: ‘place’ has become remote, distributed, democratised, dispersed. So people are tending to connect and collaborate on a far broader suite of technologies than ever before, but you are likely to own fewer of them.

We have also shifted in terms of the relationship between learning functions and knowledge itself: broadly away from codified resources (assets, programmes, ‘things’) where ownership and validation is held centrally, towards either fully, or partially, socially co-created content, and learning that is less centred around a single asset or space.

Learning certainly retains a role around record keeping, but that’s a largely passive one, especially as narrative analytic engines, as well as pattern spotting and diagnostic AI tools become better able to measure or report on social and distributed activity.

Learning has tended to be fixed into, or parallel to, structure of hierarchy, and hence how ‘important’ you are, or how ‘new’ you are, or ‘where’ you sit has dictated ‘what’ you see. Today the expectation is more around opportunity and purpose, and we see this expectation to some extent feeding into engagement. With the backbone of career fractured and outsourced, we will need to trade more readily in opportunity to maintain engagement and alignment with key talent over time, in a range of mechanisms of alignment.

In terms of the new: learning is increasingly?social, either through the incorporation of socially collaborative or socially co-creative elements, or dialogue based enquiry, or methodologies around access to tacit and tribal knowledge and so on. It’s increasingly permeable, utilising networks and socially validated knowledge.

More fundamentally, and this is part of my own current focus of thought, we may be seeing the creation of?meaning?(as opposed to retention of knowledge, or acquisition and rehearsal of behaviours) as of increasingly importance. This is probably best described in part as the metacognitive aspect of learning, or the ability to use learning, or discover through learning, a new frame of reference and understanding, and to locate behaviour or action within that. This probably partly ties into the enhancement of learning through AI too (in various applications and contexts).

Learning is tending to be more fluid, and individual, and contextual, which is not to say ‘shorter’ (although it may well also be shorter! Overarching narrative frameworks of complexity are valuable too, and the ways we take individualised pathways through to our final (or interim) understanding challenges the historic view of learning too.

The more exploratory nature of learning also means that the ‘what’ we discover challenges legacy notions of assessment too (it does not have to but it does tend to), and hence we can end up curtailing curiosity in service of safety or familiarity.

Finally, i’ve given ‘insights’ a separate corner, because the funamental nature of our insights is shifting from human derived to AI derived or narrated, which is an exponentially scaled shift and will require a similarly sized shift in mindset and capability of design and exploitation. If we simply reduce the output to that which we already understand, we are buying a Porsche to plough a field.

So what does this all mean?

At one level, it means we should ask if we need a learning function at all. Or if we need a new type of learning function. Or an evolved type. Or maybe we need multiple functions: one of which is focussed around infrastructure, but the other of which is about enablement, or productivity. Or forgetting. Or connecting. Or supporting.

There could be a compelling argument for Learning to align with Quality, or?Innovation.

Maybe we need more of a function of ‘Sense Making’ – one that owns collaborative spaces, teaches methodologies, resources communities, and has the ability to teach and assist in storytelling at scale?

Maybe we need a function that simply acts as an aggregator of distributed and diverse approaches – or perhaps we need a marketplace of social currencies and connection?

What will the market give us? Possibly more of what we already have, or an evolution of it. The marketplace is a subsidiary function of the Domain model. It serves what we need, sometimes in new flavours. It does not automatically pay to be speculative.

There are interim steps in all this: an evolution of roles even within existing functions: data scientists, learning scientists, storytellers, throw in some anthropologists and i would advocate for an economist too. A little social psychology and behavioural science too. You can do worse than finding an artist to see the world a little differently too.

We are already seeing the emergence of the journeyman teams: often alumni from the trans-nationals, moving leaders and teams almost wholesale. To some extent this already represents a disaggregation of innovation in learning from utilitarian servicing. But i sense we are at the start of this journey.