We need to think differently about innovation and why it needs complexity and adaptive thinking as part of its design.
Complexity within systems challenge us to think differently, it pushes us to think outside often our normal experiences, to confront and understand and then restructure, often the unordered, into a new order.
Organizations are in need of understanding the complexities within their systems far more.
Complexity within innovation is always adaptive.
The challenge with managing complexity is that it is made up of many shifting and connected parts, that form much around interactions and relationships. These new ‘connections’ are shifting and challenging much of our previous understanding, built often on past practice and entrenched thinking.
We need to think differently to generate fresh insights and new-to-the-world innovation and this begins by us ‘seeing’ the potential by unravelling the complexities involved. To seek a more radical innovation agenda you need to grapple with much within a complex system to yield the ‘promise’ of really new and exciting innovation.
Innovation is made up of many connected parts when it eventually combines and becomes one, the finished article or concept. You gain constantly from its combining power.
Innovation is highly dynamic (or should be), full of interactions and relationships where the individual and the collective whole, changes as a result of the experiences gleaned from engaging within the process. That is a complex adaptive system.
I wrote a piece a couple of years back, as my initial attempt to explain that innovation is a complex adaptive system. There is so much to add to this but recently I was listening to Dave Snowden from Cognitive Edge and this struck me as a great starting point for many.
David is regarded as an authority on the application of complexity theory to organizations, and I think there is so much to learn from following David and his thinking, I’d recommend it.
Understanding complexity – peeling away some of its layers.
David recently gave a part talk on understanding complexity and how to manage it.
He offered these thoughts with my summary of what I heard as my add-ons relating more specifically to innovation.
If innovation is complex, we need to recognize and manage this. They make sense to me in the context of innovation and managing its complexity.
These thoughts from David are in explaining a complex system. They might provide you a better understanding of the complexity often found within innovation and help in your thinking about the implications through.
* Enabling constraints – without constraints you simply have randomness, you decide the level of constraints for the level of flexibility you are looking for (from innovation) and how much you are prepared for uncertainty. You need to strive for a ‘given’ coherence and place this within a structure.
* Complexity is highly sensitive to small changes (weak signals are too easily dismissed) – small things magnify to produce (over time) major impact. What seems trivial actually might be highly significant. Think new technology, it is often dismissed as you lack the skills or understanding so you quickly dismiss it.
Weak signals can provide the possible future directions you need to orientate around. Identifying the weak signals within the frame of the three horizon methodology, can trigger complexity of understanding.
* Granularity matters – if you make something to big and too connected, you can often struggle to adapt and find solutions. Getting the granularity right is key, so we can see the emerging opportunities and this often means we need to break the big ideas down a level or two, to seize perhaps multiple opportunities as well as allocate resources to ‘bite-sized’ pieces of work. It is from these smaller pieces of work you begin to piece the bigger ‘prize’ together.
* You need sufficient but not excessive gradients– the example David uses is the troubles within inequality. If we all had the same equality we provide little incentive for change, alternatively if we have excessive inequality you begin to get preconditions for revolution and potential catastrophic change.
So you need some gradient but not to much. For example everyone aligned with the same values might be a bad thing to do as it destroys variety, or diversity of opinion and conflict and that is unhealthy for innovation. Within any innovation activity some ‘creative’ tension equally can give that gradient.
* Proximity and connectivity can be managed – this is the who and what we interact with, we evolve through our engagements and interactions, these give us our cognitive structure. The key is defining who interacts with whom, not the outcomes. Relationships and networks need encouragement and managing.
* You need to shift from fail-safe design to multiple parallel safe-to-fail experiments – we need to stop trying to work out one (big) thing to get it right, we need to encourage anyone who has a contribution to make, a coherent hypothesis to be allowed to run with it, in parallel for other experiments in a controlled safe-to-fail experiment.
It is the patterns of potential success that evolves from all these experiments evolves and can ‘collectively’ reveal an emerging pattern or concept that ‘breaks the mould’, sees new solutions.
These can be evaluated in parallel to detect something new and value or presented to others so they can evaluate the emerging patterns of discovery to judge and take forward or not. This more discrete project thinking deploys resources better, gives greater agility and adaptability. You set out to stimulate evolutionary possibilities.
* The inherent uncertainty of complex systems means we have to navigate a fitness landscape of possibilities – You are looking for the evolutionary potential from dispositions (the inclination towards something, the attitudes and consequences) and the propensity (the natural tendencies) to give emerging patterns of beliefs, often called peaks and troughs.
Knowing your fitness landscape for innovation is important as I have previously suggest and laid out a possible pathway. You can use landscaping for capturing individual stories or needs to find solutions that are more relevant to those (their) specific needs. Jobs-to-be-done comes to mind here and mapping solutions to closer needs that derive from exploring real stories, through ethnography as an example.
Each of the above has significant innovation implications
If you treat complexity as something you must overcome, reduce it or try to ignore it, you miss significant opportunities within innovation. You can’t force it. To exploit innovation you need to manage it, coax out the possibilities, managing it well, not as simple problems but within a better understanding of a complex system.
It is deciding what complexity within the innovation design and then set about building and placing the right capabilities where they matter.
Avoid the “Business as Usual” Mentality for Innovation.
We cannot enforce ‘business as usual’ as the modus operandi for innovation to make it ‘fit.’ We are faced with the very opposite in today’s world, the need to ‘embrace’ reoccurring change.
We need to manage complexity within the systems and we do need innovation to exploit this to our advantage.
To achieve this we need to obtain as much diversity and non-linear structure in what we do to allow recognition of all the possible options. We need to move well beyond the obvious and move through the complicated, into the complex and once in a while cross over into the chaotic nature of innovation to extract all the innovation ‘juice’ possible.
We need to think differently about innovation and why it needs complexity and adaptive thinking as part of its design.
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End note and further reading: The Cynefin Framework of David Snowden and Cognitive Edge is most valuable to explore. I discussed this from an innovators perspective recently in this post that was describing the value of the Cynefin model towards innovation.
