imagining open collaboration

At work and in school we are pretty good at creating documentation to share explicit knowledge. This is the kind of knowledge that goes into training programmes. It’s the result of interviews with subject matter experts and reviews of the field of study. For the most part, it’s stuff that is easy to codify and share.

On the other hand, understanding implicit knowledge requires a lot of conversations. It means learning and working at the same time. The type of knowledge we need to make critical decisions is often emergent, in that it emerges over time through what my colleague Clark Quinn calls ‘open collaboration’.

“This is what decision-making looks like when it matters and it’s new: open collaboration … The details are not trivial, they’re critical.

And these situations are increasing. Whether life-threatening or not, and even with the power of data, we’re going to be facing increasingly challenging decisions. We need to learn when and how to collaborate. One person following a script (which should be automated) is increasingly less likely to be the answer. An individual equipped with models, and resources including others, is going to be the minimal necessary solution.” – Clark Quinn

My colleague Charles Jennings wrote a related post on the nature of training courses and programmes. The learning & development profession is tied to an increasingly irrelevant paradigm.

“Typical offerings to prepare our future professionals reflect the dominance of the course as virtually the only the mechanism to get any attention. As such, they are constraining our ability to deliver real impact by supporting learning in the daily flow of work. These ‘learning separate from work’ models are the antithesis of what my Internet Time Alliance colleague Jane Hart calls ‘Modern Workplace Learning’ and what my 70:20:10 Institute colleagues and I call ‘70:20:10 practices’.” – Charles Jennings

We need to design our workplace structures and systems so that open collaboration can help each and every worker make critical decisions. I have discussed several (18) ways of implementing network learning, as well as various (10) methods for organizational learning engineering. The biggest challenge, as Charles notes, is to ‘imagine the different’.