I work with people who run organizations. They need to make numerous decisions everyday. How can we learn and improve in a complex environment that is always changing?
The Executive Directors of six local community health centres tell me “strategic planning has come to feel like added work, but not added value. Our current strategic plans are too static, too linear, we need to be able to be more flexible when things change.” Many organizations make 5–10 year strategic plans based on a snapshot of information that’s really only valid for a few months. For the community health centres, who offer primary health care to traditionally marginalized populations in service of equitable health for all, several major events impacted their operations in a way they hadn’t planned for — including the influx of Syrian refugees, the escalation of the opioid crisis, a marked shift in public understanding of the transgender community, a resurgence of racism and the rise of the alt-right, and changes in provincial health care legislation.
Our current strategic plans are too static, too linear, we need to be able to be more flexible when things change.
We live and work in an unpredictable environment, yet we try to predict it all the time. Disruptions like these occur regularly and often render strategic plans irrelevant, leading us to ask — are there other models or processes that can help us be more strategic?
Decision makers want effective strategies that lead, through action, to positive results. How might we better organize all the information coming at us to identify what is relevant? We are drowning in data, and turning these into valuable insights is difficult due to the sheer quantity we collect. What can we learn from the nature of the systems we are a part of that can inform how we make strategies, actions and plans? In unpredictable environments, how can we better manage despite incomplete knowledge? These questions led me into exploring the world of complexity theory.
3 basic types of systems
A helpful lens for understanding the nature of systems is the Cynefin framework, developed by Cognitive Edge. The Cynefin framework sorts issues into 3 basic domains based on the relationships between cause and effect: Predictable (Ordered), Unpredictable (Complex), and Absent (Chaotic). Predictable can be further subdivided into complicated and obvious domains, and some people refer to Disorder as a fifth domain — where we are when we don’t know where we are.
Knowing which system domain a problem lies in helps us employ the most effective management or problem solving strategy. As Dave Snowden, founder of Cognitive Edge describes, in an ‘ordered’ system, we can predict what will happen from an action. We can therefore either deal with the issue ourselves if it’s simple (following a recipe, using Best Practices), or get someone more competent to do it if it’s complicated (like building an engine; here we use Good Practices). In a complex environment, the cause-effect relationship is typically understandable in hindsight, but impossible to consistently predict what will happen when an action is taken. Which means we need to probe and test the response (using Emergent Practices). An example would be raising a child — it is not possible to make a detailed 5-year plan for what you’re going to do and what will happen because of it. Driving a teenager in a certain direction is not akin to driving a bus. The Chaotic domain, where there is no relationship between cause and effect, is typically unmanageable — we must seek first to find stability, and then manage sub-components of a task according to their respective domains.
How does this help us make better decisions and be more strategic?
The sources of the frustrations with strategic planning for many organizations arises from the fact that they exist in a complex system, but use assessment and planning tools that are more appropriate to a complicated system, where there is a right answer and an ability to be predictive. When we don’t know what to do, we are trained to go out and look for best practices to mimic, which simply don’t exist in a transferable way in complexity, since the context and cause-effect is always shifting.
The aspect of complexity science most applied to understanding organizations is the study of Complex Adaptive systems (CASs). A CAS is described as a cluster of individual elements that interact with each other, and those interactions produce system-wide patterns which in turn influence those elements so that they are more likely to fall into the pattern in question over subsequent cycles [Eoyang & Holladay 2013]. Behaviour is organic, non-linear and multi-faceted. All human systems are CASs. Complex environments are also often characterized as ‘VUCA’ (Volatile, Uncertain, Complex and Ambiguous), a term coined by the military. Information about context is a key factor in understanding human experience in a CAS/VUCA environment. Something that worked in one context won’t work in another.
There was a peak of interest and research in applying learnings and insights from complexity theory to organizational management in the 1990s but, in retrospect, the conditions in society were not yet conducive to broad-scale uptake and application. The internet was in its infancy, there was limited processing capability to support making sense of big data, and looking back it seems many managers were simply not ready or able to give up a command and control style of management.
Those conditions are changing, for example the connectivity enabled by today’s internet and other communication technologies has accelerated globalization and had a transformative effect on how we do business. Similarly, development of Artificial Intelligence and machine learning is leading to new breakthroughs daily — a trend which promises further radical and unpredictable changes and opportunities in the near future.
Potentially due to many of these context changes globally, the last 5 years has demonstrated a resurgence of interest in applying complexity principles to organizations. Executives today are required to make decisions about the future based on incomplete information, and constantly learn and adapt as things change.
How can we learn effectively in a CAS? I think we’ve tended to think about learning almost as a strategy for domination or mastery. I will learn how this works in order to master it. When I master it, I’m in control. When I’m in control I can be successful. I need as much information as I can get in order to fully understand something. This works well in a complicated system, where the way things work don’t change, but in complexity we are never fully in control. Perhaps it’s more about learning processes and how to read your context. The importance of learning and observing context is a key piece to being strategic and making good decisions in a CAS. What are the patterns and feedback loops that a leader needs to be watching and reacting to? How can we learn to notice patterns, over and over, rather than identifying 1 pattern and assuming it will continue forever? How can we learn on a personal level to better tolerate ambiguity and sit in uncertainty, without anxiously seeking more information?
How can we learn to notice patterns, over and over, rather than identifying 1 pattern and assuming it will continue forever?
How can we be more strategic in a complex domain?
Coming back to strategic planning, I take an overall attitude and approach that, in order to work together to be strategic in a complex system, we need to start first from a common point of success derived from a good enough understanding of the system, then move into the details, never forgetting our original framing for success. Having a principle-based concrete definition of success (think checkmate, not jigsaw puzzle) is key to formulating strategic principles everyone can get behind. This sounds intuitive, but in fact isn’t always practiced. There can still be things to learn from historical trends, but be careful not to assume they’ll continue, making a survival plan for where you’ll be taken rather than a travel plan for getting to where you want to be. Allowing a path to emerge still requires having an overall direction and compass.
Here are, at a high level, some of the top principles I’ve found useful for helping clients take a different lens to familiar challenges:
1. View your system as a Complex Adaptive System and craft energizing questions that recognize issues as such. The way we frame questions exposes our assumptions about the nature of a system. For example, in a 2002 discussion paper (№8) on Health Care Reform in Canada, authors Sholom Glouberman and Brenda Zimmerman outline examples of questions that assume the problem is complicated, versus questions that recognize it is complex.
Questions that assume a…
Notice the first column evokes the idea of a right answer. A yes-no response, or a clear list. The questions that recognize a complex problem elicit options, exploring, a number of possibilities, all which would need to be tested and the answers to which may change over time as context changes.
Your success is your north star — it leaves enough room to take multiple possible paths, and allow for navigating the unexpected.
2. Build a good-enough vision of success — an understanding of the basic system constraints or conditions required for particular patterns to continue can be reframed into a compelling, principle-based definition of success (as opposed to a picture or scenario), and minimize detailed planning. Principle-based definitions of success must adhere, as a package, to 6 criteria: each principle is necessary (to keep is as simple as possible), they are non-overlapping (to avoid confusion), sufficient together (there are no gaps), concrete (so as to be useful), general enough (so everyone can understand them) and evidenced-based (so as to actually produce success in a system). A great example of this type of definition at a global scale is the recently developed Future-Fit Business Benchmark, helping businesses align with a globally sustainable society (success). Your success is your north star — it leaves enough room to take multiple possible paths, and allow for navigating the unexpected.
3. Context context context. What worked in one context may not work in another, no matter how minor the differences may seem at first. Getting a good understanding of your current context requires harvesting multiple perspectives.
4. Encourage flexibility from management and leadership, rather than rigid control. Everyone in the organization needs to be able to have some room to learn to pivot.
5. Go for multiple actions at the fringes and let the direction arise. I recently heard Laura Henderson, SVP of marketing at Buzzfeed, challenge an audience of businesses to not make a strategy, but rather let the path emerge as you become responsive to your audience. When ‘success’ for your organization is dependent upon the moving target of the state of your clients, this is important. This might require creating vector-based targets rather than outcomes-based targets (i.e. we are moving in a certain direction, rather than achieving a certain hard number).
6. Compile a complexity-informed toolbox to support meetings, strategic thinking, pivoting, and responsiveness.
7. Plan to accommodate a certain amount of unpredictable issues. This improves your overall resilience and reduces the impact of the unexpected. It might look like holding 20% of your time for issues arising, or keeping a certain amount of budget aside for the unexpected.
Go forth and practice!
Then quickly stop and check what happened. Pivot if necessary. Look for patterns.
The next steps with the 6 Community Health Centres are emerging. Multi-Board strategic thinking meetings will host generative discussions across the boards of all 6 organizations using complexity-informed methods and tools that are nimble and allow for ambiguity as they seek to be successful in achieving Health Equity for everyone in Ottawa. We’re exploring how and where to reduce planning cycles, and how we can remain nimble enough to respond to the unpredictable. To improve the ability of leaders to make informed decisions in complexity, we are using research tools like SenseMaker®, which support mass capture of self-signified narrative fragments, offering exciting possibilities for reducing interpretive bias in evaluations and identify emerging patterns and weak signals. Decision makers are directly connected to the raw experiences of clients and staff as well as information about context that surrounds those experiences. This statistical data linked directly to micro-stories provides powerful and nuanced insights.
I’ve found the way out usually involves taking a deep breath and practicing tolerance of uncertainty.
Working with organizations to help them navigate the frustrations of unpredictability is to me a simultaneously elating and terrifying challenge. My brain can get lost in the fog as fast as you can google ‘complexity.’ I’ve found the way out usually involves taking a deep breath and practicing tolerance of uncertainty. From there we test, pivot and work together to bring our multiple perspectives and knowledge under the banner of a unifying framework and understanding of success, and our next steps emerge from the wisdom of the group, rather than any one individual.
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