“It is the very uncertainty, unpredictability, and uncontrollability of organizational processes that signal the adaptive capability of complex systems; their capacity for the emergence of novel practices, processes, and routines is at the heart of an ecology of innovation.” -Goldstein, Hazy, Lichtenstein Complexity and the Nexus of Leadership: Leveraging Nonlinear Science to Create Ecologies of Innovation
Learning lies at the very heart of any complex, adaptive and innovative system. Especially those organizational systems that are able to continually grow and evolve. And while we can let that statement linger, it is not enough. It requires more, much more.
A diversity of learning
Curation and creation of new knowledge
Environments of experimentation where novel and innovative ideas and thinking can emerge
Spaces where ideas can collide to create new thinking and ideas
Engaging learning networks and idea flows, both internally and externally
Ongoing reflection of mental models, assumptions and cognitive biases
Ability to adapt and change in response to the ongoing emergence of the new
Unfortunately, in the midst of today’s organizational complexity and chaos that encompasses today’s accelerated and turbulent change cycles, leaders look to insulate and simplify, instead of embracing the opportunity and/or opportunities that begins to emerge from this complexity and chaos.
When change is needed most for an organization, it is often the status quo and stasis that is sought out and exemplified (both consciously and unconsciously).
In the face of change, whether incremental and/or disruptive, the comfort of the known is often held up as a model to stave off the fear of the unknown, even when the current model is proving to be ineffective. There is safety in the known. Unless the emergence of the novel and new can provide a strong promise of future success it is squeezed out in favor of the familiar and known.
Which is why the above “requirements” are necessary for any organization to be able to continually adapt and maintain innovative ecologies, environments, and ecosystems.
Or as the old adage puts forth, “you don’t know what you don’t know” remains true for our organizations, as well as individuals. For much of what happens in an organization is based on a “we’ve always done it this way” approach to working that has seldom been considered or questioned. Which then begets the question, are the individuals and the organization itself on a journey to continually seek out “what it doesn’t know?” Or is it happy to remain in the comfort of the known, often at its own peril and relevance.
For which Goldstein, Hazy, Lichtenstein add, “Since ecologies are driven by all of the exchanges, interchanges, interactions, and connectivities existing between its subsystems, whatever is essential takes place at these interfaces.”
Which reminds us that if individuals and organizations are not searching out and creating new learning and knowledge, engaged in internal and external learning flows and networks, seeking out and allowing for experimentation and the emergence of the novel and new, and constantly reflecting on their mental models as they collide with a diversity of learning and ideas, those “exchanges, interchanges, interactions, and connectivities” will do little to move individuals and organizations beyond and amplification of what is already known.
Or as Goldstein, Hazy, Lichtenstein share, “At the core of ecosystems are patterns of interactions – the vital exchanges – that connect all the subsystems together.” For which they continue, “Because a complex system is composed of interdependent, interacting subsystems, information about the functioning of the system is distributed throughout the networks of connection. This nexus of relations is the source of influence, the driver of innovation, and the regulator of change.”
Which reminds us that all individuals and organizations have their own borders, their own space where the known and the unknown intersect.
It is at this intersection, that the future relevance of our organizations is often determined and discovered.