The challenges of managing in today’s worlds are tough, very tough and demanding. It is so volatile, potentially disruptive and full of risk. Organizations are simply struggling to shed their clothes in the 20^th century and find a way to smoothly manage to become more adaptable and agile in form. They are adjusting to offer consistent responses to instability in the most effective ways, to keep adapting to the consistent market challenges, and in so doing profiting from meeting that latest challenge or disruptive opportunity.

The problem is you simply can’t manage this smoothly, it will be highly disruptive as the organization re-equips themselves and learns, often in the hardest way possible, through failure, through experimentation, through risk-taking. Innovation is increasingly seen as the pathway forward in capturing growth and grabbing any advantage, even if these are increasingly transient. Yet as we look towards building our innovation capabilities we need to work in totally different ways and see ‘things’ in new ways.

Innovation in itself is also a force of instability and we need to find ways to embrace much of its uncertainties by understanding its dynamics. We need to have a major shift in our organizational thinking, needed to find the appropriate new balance within those dual ‘tensions’ of ‘stability’ through efficiency, with its opposite, ‘change’ driven by innovation. It is these dynamic forces within the world we work that need us to respond by building that capacity for managing those ‘dynamic’ innovation capabilities, that today’s markets are requiring and organizations are needing to master.

The emphasis needs to point more to building dynamic innovation capabilities

Dynamic capabilities emphasize management capabilities and the inimitable (unique) combinations of resources that are constantly at work across all functions to make them distinctive and valuable.

Distinguishing innovation capabilities that are relevant are based on the type of knowledge they contain. Functional capabilities allow development of technical, practical knowledge. Integrative capabilities allow firms to absorb knowledge from external sources and this is where absorptive capacity and its structure comes into our required thinking. Innovation requires us to constantly learn, develop, mold and manage multiple situations and challenges. This requires the integration of critical capabilities to successfully stimulate innovation for effective and improving performance.

Many of the identified resources in capabilities have some level of complementary, improving one triggers improvement in another. Equally ignoring one can impact negatively on others. So each component, on their own, have limited effect, it is the combined effect that generates the improving performance. Equally, the needs do change over time and you have to constantly ‘refresh, challenge and stimulate’ these, otherwise, you can have core rigidly set in.

Integrating back or making anew

To this end, I have produced a conceptual model of what I believe is the ‘make up’ for providing an ongoing innovation performance engine – the innovation fitness landscape and the dynamics of core capabilities. I felt it needed a dedicated website and this is where you have ended up to be the ‘go to’ source of reference. It is a work-in-progress so please recognize this when you find the time to explore it. The incentive to do this ‘exploring’ is quite simple, you are dealing in increasingly complex environments and you need to know your capabilities and capacities to innovate. If you don’t you will simply get lost and perhaps forgotten. Innovation offers relevancy and a path towards a sustainable future but it needs fresh approaches. I believe I can offer some insights to help.

The need for all of us is recognize and embrace the need to build an organization-wide innovation capability that should pervade all aspects of the organization’s existence, forming its new core innovation value.

The post The world is working within increased complexity, are you? first appeared on Building Your Innovation & Ecosystem Intelligence.

They lead this article with “one view of complexity that holds that it is largely a bad thing- that simplification generally creates value by removing unnecessary costs”. Certainly we all yearn for a more simplified life, structure, organization, approach to systems or just reducing complexity in our daily lives to find time for what we view as improving its ‘quality’.

Within the article they argue there are two types of complexity – institutional and individual.

The former concerns itself with the interactions within the organization, the latter is the way individuals or managers deal personally with complexity.

The real important take away from this article for me was when organizations treat complexity as something they must overcome, reduce or try to ignore they miss opportunities. Complexity, the authors argue, should be seen as a challenge to be managed, managed well, and its full potential exploited, not as a problem to be reduced or eliminated. It is through the nature of these complexities we achieve competitive advantage and can exploit more of the flow of knowledge for those new sources of new profit and wealth creation.

They suggest organizations need to decide on where to hold complexity within any design and build the right capabilities where they matter. I would argue innovation certainly matters, and it is complex and needs to be understood as exactly that, and managed accordingly not in piece meal fashion. Complexity matters in building the right processes, skills and culture but because they don’t behave in linear ways and any ‘messing’ with the complexity and relationships within this can have an awful lot of unintended consequences.

We have choices of complexity

There are different types of complexity to manage. Work conducted by Julian Birkinshaw and Suzanne Heywood suggested four types of complexity. I only summarize these here.  Imposed complexity, those interventions both internally and externally that require ‘higher’ insight. There is the inherent complexity found with any organization and presently managed through striving to be more efficient and effective. There is designed in complexity, where innovation needs to fit more. These are choices about how, where and why an organization sets about its operation. These can be constrained, under invested in, even jettisoned but do have lasting consequences for the future of the organization. This is the area of strategic consequence as these can limit competitive advantaged from the level of innovation intensity chosen as an example. The fourth is unnecessary complexity where increased misalignment resides, it is sometimes easy to recognize but often hard to let go as it sometimes makes up “the way things are run around here” and have a richness in history.

The challenge of complexity within innovation

If you can begin to identify complexity that hampers effectiveness you can begin to remove it. What you have to be very clear upon is having a complete understanding, or a well informed one on all the effects if the complexity part you are removing is not the route to value and often innovation. The more creative side certainly does get constrained and caught up in this often shorter term pursuit of effectiveness for effectiveness sake and you suddenly don’t have the bandwidth for innovation exploration and more.

Do recognize that innovation is complex, recognize it does have to be handled carefully but it needs to also be fully understood for what it is, a complex adaptive system. It cannot be treated in the same way as effectiveness or efficiency can. It  needs ‘actively’ managing differently, for all the future opportunities it holds by placing the emphasis on building greater innovation capabilities to make it ‘dynamically’ work. Otherwise you end up with unexplained consequences to poorer performance from your innovation activities and often at a loss to explain why.

We do need to relate more to complexity as it comes with the turf if you want really lasting innovation.

The post Cracking the complexity code first appeared on Building Your Innovation & Ecosystem Intelligence.

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.

****

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.

The post Understanding Challenges Within Innovation Complexity first appeared on Building Your Innovation & Ecosystem Intelligence.

Firstly a very brief explanation of the Cynefin Model and why I find it highly valuable for innovation management.

Innovation has many characteristics of a complex adaptive system as I have crudely attempted to explain here.

The three primary states within the Cynefin framework are Ordered Systems (including Obvious and Complicated), Complexity and Chaos.

Order is split into two, as this handles a key difference in human knowledge between those states, where the cause and effect relationship is obvious and those where it requires greater analysis or expertise.

Exploring a process of emergent discovery for innovation

Most innovators are working in and certainly are far more familiar with the ordered domains, for ‘obvious’ innovations that extend, enhance or evolve their existing products and services.

Equally, they understand their more specialised place and contribution to be growing in their comfort, in the part they play in the more ‘complicated’ domain, where expertise, dedicated focus and specialization are often required or called upon.

The real challenges come from the really ‘new’ in innovation as it becomes greater in new designs, changing functionality, different technology or significant improvements on the existing that involved greater research and development the more you travel around the cynefin framework.

Here within this ‘ordered’ domain, the innovation activities are a lot more predictable, and more straightforward in their management. This is more about being trained, using expertise where needed, applying skills, making appropriate decisions, having established structures, processes and measurements and then systematically working towards clear objectives.

When you move across into the ‘complex’ or even ‘chaotic’ domains this takes on far more new learning, more situational assessments, looking and combining capabilities to manage emerging patterns and knowledge, applying experiences, looking for diversity of opinions and searching for new wisdom or insights.

Expertise and experience, collaboration and relationships need significantly leveraging. You often diverge/converge constantly as you work through the potential answers. The mindset here is different and it is more one that is based on the detection. Innovation is far more demanding, pushing frontiers, exploring discoveries, dealing in a series of exchanges and recognizing emerging patterns to piece together as real ‘new to the world’ innovation activities.

Let’s describe the domains within the framework

In the obvious domain of order, you assess the facts, categorize them, and then apply established practice. The system is well set up and you work through this in very controlled ways, you tend to adhere to the practices, always looking to improve and apply best practices. You sense- categorize- and respond as your work through the approach. Most of our incremental innovation ‘sits’ here as an extension to what we have established and where most systems, processes, technology understanding and controls are already established, all we are required to do is manage within a defined set of controls (constraints).

The complicated domain is where you tend to need greater expertise, initially, you might have multiple answers to your emerging concepts and you need to work through a better cause and effect so all involved can see the relationships. This is often termed the “known unknowns”. You sense- analyse- respond. This domain requires degrees of expertise and investigating various options and working eventually through the trade off’s. If a decision remains elusive then perhaps you have a more complex issue.

In the complex domain, you need to really work to extract the right answers. This is regarded as the realm of the “unknown, unknowns” and where much of our organizations have to work. Why simply because situations and decisions are far more complex as much is swirling around in unpredictability and flux. You are looking for more for the emerging patterns and you should avoid the attempt to impose a course of action or place ‘increased’ constraints here, you should allow the pathway to reveal itself.

For innovation here, you firstly probe- then sense- and then finally- respond. A solution or breakthrough will emerge and then you can begin to impose growing constraints, looking to move unordered thoughts or patterns into more orderly ones to evolve into final or potential solutions.

Avoid imposing order too early as this can preempt the opportunities for these ‘greater’ patterns to emerge. Leaders need to set the stage, step back, allow patterns to emerge and then determine which ones are more attractive or desirable.

Design thinking works well in this domain, where you visualize, synthesise, create narratives, build on learning stories, and explore multiple prototyping and safe-to-fail experiments.

When you are faced with a chaotic domain, this needs a rapid response. Searching for right answers can waste time, this is the realm of the “unknowables.” Cause and effect are impossible to determine as no manageable patterns seem to exist, only turbulence, challenge and threat.

The need is to quickly act, gain an early sense and respond. It tends to be the domain for top-down decisions, there is limited time for seeking input in ‘normal’ ways. This is the space where you ‘impel’ or ‘impose’ innovation.

Run a crisis team in parallel with an innovation solution solving team. Have one team managing the immediate to manage constantly changing issues incoming and another team working exclusively on exploring doing things differently to move from this chaos back into complexity.

Your innovation solutions have to counter the disruption or move beyond but the toughest part is ‘getting others to let go’. This requires clear leadership and direct communications to attempt to impose immediate actions and re-establish order.

There is a fifth domain, shown in the middle and that is disorder, not knowing which domain you are in and we often struggle to recognize which domain we want to be in for innovation and this disorder stays with us, until we impose order, structures or clarity to the thinking. The Cynefin model is highly contextual for innovation in my opinion.

Finally, to complete the understanding, the Obvious domain is next to Chaos because complacency (the extreme of Obvious) can all too easily produce catastrophic failure.

Thus that boundary is represented by a cliff, or more technically a catastrophic fold. You can find yourself moved from an orderly, steady-state, the business that is obvious to you and then thrown suddenly into chaos, by the sudden change. You fall off the cliff.

Recognizing and applying the different practices to each domain

Another important distinction within the model is around the type of practices each domain seeks out. In the obvious domain best practice rules, you are looking to constantly improve the innovation activity with repeatable, efficient structures and processes. Whereas in the complicated domain it is the search for good practices, the expertise deployed should bring this into the thinking.

On the other hand in the complex domain, you are searching for emergent practices, as you clarify, evaluate, probe and sense.

Finally, in the chaotic domain, this is more the application of novel practice, as you are ‘reacting’ to numerous situations where judgement and experience are applied to find original solutions to tackle mostly unique situations where you are learning as you go.

 You achieve a greater depth in discussions and sense-making around different innovation needs using this framework

Cynefin thus contrasts how things are, with how we know them, with how we perceive them. The overall idea is that the more you allow these three to interact with each other the more likely it is that your actions with be authentic to the situation.

Cynefin goes well beyond a categorisation model that many try to use it for; it is about dynamic movements. So in the model shown above the prime dynamic is shown by the blue arrow.

Sometimes you can move between all four domains and this is shown as the green line (in the above depiction of the framework) and as innovation tends to be more in the complicated and complex space, you ‘operate’ more in these two modes.

So where does the model come from? Dave Snowden from Cognitive Edge. David is regarded as an authority on the application of complexity theory to organisations. David offers so much that is relevant to our work within or for organizations, to listen and learn from.

Innovation is complex and works well within the Cynefin model
I like this as a discussional and resolution solving framework for innovation, as innovation can move across and between complex or complicated at different times, if it is more than ‘obvious’ incremental innovation. It attempts to place orders into the often unordered.

New ideas of innovation emerge in the complex domain and are often dependent on the constraints you impose to ‘extract’ and ‘yield’ choices.

You ‘toggle’ from exploration to exploitation across domains. The more you relax the more constraints can allow new possibilities to emerge. You want to eventually bring the unordered more in control and become more ordered.

From time to time the dynamic within an innovation concept may have ossified in which case a reset is needed; a shallow sudden dive into chaos. You are looking to bring the innovation out of this chaos domain as quickly as possible and impose some order (or constraints).

Only when change is no longer significant is it shifted to Obvious (formerly Simple) as the domain where incremental innovation resides as it relies more on repeatable structures and established practices.

We need to recognize different types of innovation need to be managed differently

I believe truly new (to the world) innovation is in mostly the complex domain and should be treated as such, often we don’t. We need to start with very open minds and then bring the thinking down.

We try to apply our existing practices and experiences best suited to regular or incremental innovation, found in the complicated and obvious domains, where practices constrain the idea more than we often want but sometimes need, as the ‘demands’ are imposed. We start to immediately probe and do not take the necessary time to analyse. We reduce the potential for greater innovation impact in applying constraints.

When you look at the four domains described here think of incremental innovation for working in the obvious, distinctive innovation for the complicated domain, radical or breakthrough for the complex domain and disruptive innovation when it comes to the chaotic domain. Think disorder when you have no established innovation system in place, just expecting innovation to happen!

Each of the above has significant innovation implications

The Cynefin model helps determine the ‘type’ of innovation and its complexity and the constraints you impose upon ‘it’. Recognizing the complexity that can hamper the effectiveness to innovation is very different from our usual management of effectiveness and efficiency.

Innovation requires boundaries but it needs the appropriate thinking, analysis and probing, this framework provides the ‘mechanism’ to promote this.

You need to build flexible systems that adapt, are dynamic and responsive and need that bandwidth for innovation exploration. It is a series of challenges evolving to be exploited, not reduced or eliminated.

Using the Cynefin model allows you to begin to think about the appropriate innovation and what is needed in practice and thinking approach.

It allows you to begin to be prompted in what the possible constraints might be, to allow you or not to explore innovation in these different domains.

To exploit innovation you need to manage it, manage it well, not as simple problems but within a better understanding of being a complex system, to extract the most from the investigation and discoveries.

It is deciding what complexity your innovation might have within the innovative design and then setting about building the right capabilities and framing these appropriately, where they matter.

My endnotes: I have drawn from different material associated with the work and explanations of David Snowden and Cognitive Edge extensively.

My innovation interpretations are built on this growing understanding and by using this model in my innovation discussions, by seeing its value and power for myself and through others. I just hope I have given justice to this fantastic model here.

BTW– The name Cynefin is a Welsh word whose literal translation into English, as explained by David is “as habitat or place fails to do it justice”. It is more properly understood as “the place of our multiple belongings” or “a sense of place” It is pronounced cunevin. The frame’s intent is to explore the relationship between man, experience, and context.
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The post The Use of the Cynefin Model for Innovation Management first appeared on Building Your Innovation & Ecosystem Intelligence.

The challenges of managing in today’s worlds are tough, very tough and demanding. It is so volatile, potentially disruptive and full of risk. Organizations are simply struggling to shed their clothes of the 20^th century and find a way to smoothly manage into becoming more adaptable and agile in form. They are adjusting to offer consistent responses to instability in the most effective ways, to keep adapting to the consistent market challenges,and in so doing profiting from meeting that latest challenge or disruptive opportunity.

The problem is you simply can’t manage this smoothly, it will be highly disruptive as the organization re-equips themselves and learns, often in the hardest way possible, through failure, through experimentation, through risk-taking . Innovation is increasingly seen as the pathway forward in capturing growth and grabbing any advantage, even if these are increasingly transient. Yet as we look towards building our innovation capabilities we need to work in totally different ways and see ‘things’ in new ways.

Innovation in itself is also a force of instability and we need to find ways to embrace much of its uncertainties by understanding its dynamics. We need to have a major shift in our organizational thinking, needed to find the appropriate new balance within those dual ‘tensions’ of ‘stability’ through efficiency, with its opposite, ‘change’ driven by innovation. It is these dynamic forces within the world we work that require us to respond by building that capacity for managing those ‘dynamic’ innovation capabilities, that today’s markets are requiring and organizations are needing to master.

The emphasis needs to point more to building dynamic innovation capabilities

Dynamic capabilities emphasize management capabilities and the inimitable (unique) combinations of resources that are constantly at work across all functions to make them distinctive and valuable.

Distinguishing innovation capabilities that are relevant are based on the type of knowledge they contain. Functional capabilities allow development of technical, practical knowledge. Integrative capabilities allow firms to absorb knowledge from external sources and this is where absorptive capacity and its structure comes into our required thinking. Innovation requires us to constantly learn, develop, mould and manage from multiple situations and challenges. This requires the integration of critical capabilities to successfully stimulate innovation for effective and improving performance.

Many of the identified resources in capabilities have some level of complementary, improving one triggers improvement in another. Equally ignoring one can impact negatively on others. So each component, on their own, have limited effect, it is the combination effect that generates the improving performance. Equally, the needs do change over time and you have to constantly ‘refresh, challenge and stimulate’ these, otherwise you can have core rigidly set in.

Integrating back or making anew

To this end I have produced a conceptual model of what I believe is the ‘make up’ for providing an ongoing innovation performance engine – the innovation fitness landscape and the dynamics of core capabilities. I felt it needed a dedicated website and this is where you have ended up to be the ‘go to’ source of reference. It is a work-in-progress so please recognize this when you find the time to explore it. The incentive to do this ‘exploring’ is quite simple, you are dealing in increasingly complex environments and you need to know your capabilities and capacities to innovate. If you don’t you will simply get lost and perhaps forgotten. Innovation offers relevancy and a path towards a sustaining future but it needs fresh approaches. I believe I can offer some insights to help.

The need for all of us is recognize and embrace the need to build an organization-wide innovation capability that should pervade all aspects of the organizations existence, forming its new core innovation value.

The post The world is working within increased complexity, are you? first appeared on Building Your Innovation & Ecosystem Intelligence.

They lead this article with “one view of complexity that holds that it is largely a bad thing- that simplification generally creates value by removing unnecessary costs”. Certainly we all yearn for a more simplified life, structure, organization, approach to systems or just reducing complexity in our daily lives to find time for what we view as improving its ‘quality’.

Within the article they argue there are two types of complexity – institutional and individual.

The former concerns itself with the interactions within the organization, the latter is the way individuals or managers deal personally with complexity.

The real important take away from this article for me was when organizations treat complexity as something they must overcome, reduce or try to ignore they miss opportunities. Complexity, the authors argue, should be seen as a challenge to be managed, managed well, and its full potential exploited, not as a problem to be reduced or eliminated. It is through the nature of these complexities we achieve competitive advantage and can exploit more of the flow of knowledge for those new sources of new profit and wealth creation.

They suggest organizations need to decide on where to hold complexity within any design and build the right capabilities where they matter. I would argue innovation certainly matters, and it is complex and needs to be understood as exactly that, and managed accordingly not in piece meal fashion. Complexity matters in building the right processes, skills and culture but because they don’t behave in linear ways and any ‘messing’ with the complexity and relationships within this can have an awful lot of unintended consequences.

We have choices of complexity

There are different types of complexity to manage. Work conducted by Julian Birkinshaw and Suzanne Heywood suggested four types of complexity. I only summarize these here.  Imposed complexity, those interventions both internally and externally that require ‘higher’ insight. There is the inherent complexity found with any organization and presently managed through striving to be more efficient and effective. There is designed in complexity, where innovation needs to fit more. These are choices about how, where and why an organization sets about its operation. These can be constrained, under invested in, even jettisoned but do have lasting consequences for the future of the organization. This is the area of strategic consequence as these can limit competitive advantaged from the level of innovation intensity chosen as an example. The fourth is unnecessary complexity where increased misalignment resides, it is sometimes easy to recognize but often hard to let go as it sometimes makes up “the way things are run around here” and have a richness in history.

The challenge of complexity within innovation

If you can begin to identify complexity that hampers effectiveness you can begin to remove it. What you have to be very clear upon is having a complete understanding, or a well informed one on all the effects if the complexity part you are removing is not the route to value and often innovation. The more creative side certainly does get constrained and caught up in this often shorter term pursuit of effectiveness for effectiveness sake and you suddenly don’t have the bandwidth for innovation exploration and more.

Do recognize that innovation is complex, recognize it does have to be handled carefully but it needs to also be fully understood for what it is, a complex adaptive system. It cannot be treated in the same way as effectiveness or efficiency can. It  needs ‘actively’ managing differently, for all the future opportunities it holds by placing the emphasis on building greater innovation capabilities to make it ‘dynamically’ work. Otherwise you end up with unexplained consequences to poorer performance from your innovation activities and often at a loss to explain why.

We do need to relate more to complexity as it comes with the turf if you want really lasting innovation.

The post Cracking the complexity code first appeared on Building Your Innovation & Ecosystem Intelligence.

Maybe I’m taking on more than I can chew here but I’m going to attempt to explain why innovation can be so complex and requires an adaptive system.

I apologise if it does not work for you, or you simply just give up on this but I am going to try to explain innovation as a complex adaptive system.

Why- I like the pain involved!  I’m certainly not in any shape or form an expert or even that much of a student of complex systems, and what it fully consists of, but I do need to explore this more, and a little shared pain helps in this as I go.

This issue is one that I consistently come across many references to innovation being a complex system. The trouble is I’ve never been fully clear on what determines the make up a complex system for innovation. I’m not sure anyone does for complex systems either!

So my aim here is to establish a direct and clear set of links across innovation and complexity without it involving me in ploughing through incredibly ‘dense’ academic papers on this subject.

It is amazing how Wikipedia is becoming rapidly the first call of reference, is it because it takes away all this density found in academic papers, or that the academic papers are written mostly for an informed group and for those of us, obviously sitting on the outside of this ‘elite’ group, we gravitate to where we seem welcome to gain a ‘reasonable’ and quick understanding. So this is my starting point.

Irrespective our starting point has to be definitions

So borrowing from Wikipedia  let’s define:

A complex system is a system composed of interconnected parts that as a whole exhibit one or more properties (behaviour among the possible properties) not obvious from the properties of the individual parts.

A system’s complexity may be of one of two forms: disorganized complexity and organized complexity. In essence, disorganized complexity is a matter of a very large number of parts, and organized complexity is a matter of the subject system (quite possibly with only a limited number of parts) exhibiting emergent properties.

Complex adaptive systems are special cases of complex systems. They are complex in that they are dynamic networks of interactions and relationships not aggregations of static entities. They are adaptive in that their individual and collective behaviour changes as a result of experience

So did that help?

Thankfully whoever wrote the Wikipedia entries kindly gave some examples of complex adaptive systems. These include the stock market, social insect and ant colonies, the biosphere and the ecosystem, the brain and the immune system, the cell and the developing embryo, manufacturing businesses and any human social group-based endeavour in a cultural and social system such as political parties or communities.

There are close relationships between the field of CAS and artificial life. In both areas, the principles of emergence and self-organization are very important.

So does innovation also fit within complex adaptive systems?

If we take the suggested feature list presented on Wikipedia’s entry for complex systems (http://bit.ly/nF5F3G ) I feel innovation fits. Let’s make some comparisons and this is my attempt to quantify innovation for being a complex adaptive system in the table below. It is a work-in-progress.

Components of an innovation complex adaptive system compared.

The source for the features used for a complex adaptive system has been taken from: http://en.wikipedia.org/wiki/Complex_adaptive_system and for the innovation complex adaptive system is my thoughts on where the feature does apply in innovation to fit.  W-I-P 09 02 2012

Do you agree, do you see other ones, or have I lost you?

Cracking the complexity code

There was a good article within the McKinsey Quarterly, published in 2007 entitled “Cracking the complexity code” written by three authors Suzanne Heywood, Jessica Spungin and David Turnbull that leads with “one view of complexity holds that it’s largely a bad thing- that simplification generally creates value by removing unnecessary costs”.

Certainly, we all yearn for a more simplified life, structure, organization, approach to systems or just reducing complexity in our daily lives to find time for what we view as improving its ‘quality’.

Within the article, they argue there are two types of complexity- institutional and individual. The former concerns itself with the interactions within the organization, the latter is the way individuals or managers deal personally with complexity.

The real important takeaway from this article is when organizations treat complexity as something they must overcome, reduce or try to ignore they miss opportunities.

Complexity, the authors argue, should be seen as a challenge to be managed, managed well, and its full potential exploited, not as a problem to be reduced or eliminated. It is through the nature of these complexities we achieve competitive advantage and can exploit more of the flow of knowledge for those new sources of new profit and wealth creation.

They suggest that organizations need to decide on where to hold complexity within any design and build the right capabilities where they matter. I would argue innovation certainly matters, and it is complex and needs to be understood as exactly that, and managed accordingly not in a piecemeal fashion.

Complexity matters in building the right processes, skills and culture but because they don’t behave in linear ways and any ‘messing’ with the complexity and relationships within this can have an awful lot of unintended consequences.

The other correlations that work for me

The late Everett Rogers offered us the diffusion of innovation, which gave us a frame to understand the process by which innovation spreads within social systems.

Complex systems are equally about relationships among the members of a system. You move into more the emergent behaviours that become increasingly adaptive in response to the environment and what interact within it.

Diffusion occurs in complex systems where networks overlap, exchange and learn. Both Diffusion and Complex Systems adapt and adopt with the endpoint of making ‘it’ into more of an ordered system.

The more you work the system, the fitter for the purpose it becomes, the more it diffuses out, the more dynamic it becomes and increasingly valuable from these interactions.

Complex adaptive systems don’t operate in equilibrium conditions

I’ve been also in a set of debates in recent days around management looking for stability, for predictably, looking to take as much complexity out of the system as possible- often sometimes labelled as ‘variance’.

This leads to enforcing business as usual as the modus operandi for innovation to ‘fit’  but we are faced with the very opposite in today’s world, the need to ’embrace’ reoccurring change.

We need to manage complexity and we do need innovation so we do need to obtain as much diversity and non-linear structure in what we do to allow diversity and all possible options. Our innovation systems are being forced ‘open’ making them even more complex and our energies will have to turn from ‘containment’ to more ‘adaptive’ and responsive ones to manage going forward.

We need to not reject complex systems we need to understand them, we need to embrace them and learn to determine what needs to be complex and what doesn’t. This requires a real ‘flow’ of different energies to maintain the organization of the system, it needs active managing. It will only become harder.

For innovation to work, to thrive, and to provide a sustaining payback, it needs to be seen as a complex adaptive system. We can’t keep hiding and pretending the ‘bits and pieces we play with and constantly fiddle with, called our innovation system, will be sufficient. We do need to understand innovation in its entirety.

We have choices of complexity

There are different types of complexity to manage. Work conducted by Julian Birkinshaw and Suzanne Heywood suggested four types of complexity. I only summarize these here.  Imposed complexity, those interventions both internally and externally that require ‘higher’ insight.

There is the inherent complexity found with any organization and presently managed through striving to be more efficient and effective.

There is designed in complexity, where innovation needs to fit more. These are choices about how, where and why an organization sets about its operation. These can be constrained, underinvested in, and even jettisoned but do have lasting consequences for the future of the organization.

This is the area of strategic consequence as these can limit competitive advantages from the level of innovation intensity chosen as an example.

The fourth is unnecessary complexity where increased misalignment resides, it is sometimes easy to recognize but often hard to let go as it sometimes makes up “the way things are run around here” and has a richness in history.

The challenge of complexity within innovation

If you can begin to identify complexity that hampers effectiveness you can begin to remove it but be really clear on the effects if the complexity part you are removing is not the route to value and often innovation, which certainly does seemingly get constrained and caught up in this often shorter-term pursuit of effectiveness for effectiveness sake and you don’t have the bandwidth for innovation exploration.

Recognize innovation is complex, recognize it does have to be handled carefully but it needs to also be fully understood for what it is, a complex adaptive system. It cannot be treated in the same way as effectiveness or efficiency can.

It needs ‘actively’ manage differently, for all the future opportunities it holds by placing the emphasis on building greater innovation capabilities to make it ‘dynamically’ work.

Otherwise, you end up with unexplained consequences to poorer performance from your innovation activities and are often at a loss to explain why.

We do need to relate more to complexity as it comes with the turf if you want really lasting innovation.

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