The VUCA Framework
The rapid growth of globalization, information technology, and a changing environmental context are all working to take us into a much more complex world. This new world is interconnected, interdependent, nonlinear and volatile. Over the past few decades, an acronym has emerged within the business community to identify the fundamental internal and external conditions that affect organizations within this more complex environment. This acronym is called VUCA, and it stands for volatility, uncertainty, complexity, and ambiguity.
The common usage of the term VUCA began in the 1990s and derives from military vocabulary, where it refers to the experience of officers in operations. It tries to capture the uncertain and dynamically changing situation of a military engagement, where there is a lack of information. Events often just happen in a chaotic and unpredictable fashion in what is also called the “fog of war”. Military commanders sometimes describe this as being in a world of unknown unknowns – this is the extreme version of a VUCA world. This article talks about how to develop strategies for operating within this VUCA environment. So it might be helpful to start by talking a bit about strategy and our traditional approach to it in a non-VUCA world.
Strategic management involves the formulation and implementation of major goals and initiatives taken by an organization’s top management. These are generally based on an assessment of the internal and external environment in which the organization operates. Thus, how we approach strategy will obviously change in response to the environment within which our organization is operating. Owing to its origins within industrial age manufacturing, much of our modern management theory is focused on stable, predictable, routine environments, where there is one relatively clear objective. These factors make possible an approach to strategy that is based on a few key concepts and methods.
Firstly that there is one optimal goal or solution. Within these relatively static environments where there is one clearly defined metric for performance, such as maximizing revenue, there can be one right answer, one optimal solution. And thus, the aim is to obtain all the information required and perform analysis upon it to identify this optimal solution. The objective being to guide the organization towards this desired outcome through the control of the elements within the organization.
Secondly, fixed future projections. Due to limited volatility and the high level of predictability within the environment, we can project past experience and data onto the future and use risk-based analysis to calculate and ascribe quantitative payoffs to a limited number of future possibilities.
Lastly, the use of progress planning. The progress towards our ultimate goal can be broken down into stages. We can create clear metrics for each stage, and the movement through these stages is typically seen to be a linear incremental process.
This approach to strategy works fine until we turn up the volatility, uncertainty and complexity within the environment. Turning a rapidly changing external environment to our advantage requires a recalibration of many dimensions across how we develop, deploy and deliver our strategies. And this is where complexity management finds its application in offering us an alternative paradigm with which to lead organizations. So let’s take a quick look at the different dimensions of the VUCA world and the response complexity management offers to this challenging environment.
In systemically volatile environments, change is a constant and our strategy needs to evolve from resisting it to working with it. This means creating organizations that are resilient through their agility and capacity for adaptation. The strategic emphasis shifts from creating fixed well-defined goals and plans to trying to create agile organizations led by a clarity of vision and effective communications. That way, the organization can be very clear about its values and objective, but very flexible in how it implements capabilities and achieves its vision.
Complex systems are said to exist at a far-from equilibrium state, meaning that they consume a lot of energy so that they are able to avoid their lowest energy state (otherwise known as equilibrium) and are able to continuously change and evolve. This is what physicists call a dynamic system and it is one of the key properties of complex systems. Biological systems are good examples of dynamic systems since they require a constant input of energy in order to maintain homeostasis (live) and grow. Another feature to complex systems is that they can flip from one state to another very rapidly, through what is called a phase transition. A phase transition doesn’t just change some of the systems properties, it changes the structure and makeup of the system itself, the actual parameters that define it. Think about a caterpillar changing into a butterfly. This is a phase transition, it is a systemic change. The metrics and the vocabulary we used to describe the creature before and after the transition are fundamentally different.
The result of this is that complex systems are volatile, meaning they are constantly changing. They can change very fast and, more importantly, their state can change drastically. That is, shift from one regime to another – Dubai’s transition from traditional fishing village to global metropolis within a few decades is an example of a rapid phase transition or regime shift.
The rapid pace of change within the global economy has been driven largely by technological development, as the rate of technological innovation has greatly increased over the past few decades with the advent of information technology and today the rise of clean technology. Coupled with this has been the expansion of the global economy as many more countries have joined, creating an environment of heightened competition. Within this environment, corporations are eager for a competitive advantage through innovation. The net result is a rapid drive forward of technological development.
As globalization takes us into this expanded global environment, the distribution of possible states to organizations and their environment is greatly broadened. For example, in this global environment a business may be operating in both an economy with a GDP per capita of six hundred dollars or fifty thousand dollars, working under a dictatorship or alongside a pluralistic democracy.
Heightened interconnectivity and interdependency means volatility is no longer just a local event, but it is increasingly systemic. Financial crises are no longer contained to a particular country, they affect the entire global financial system. Climate change is another example that alters some of earth’s most fundamental systems and thus creates systemic volatility. With these systemic shocks, no one is too big to fail. By definition, complex systems are always greater than any one of their constituent elements, meaning every element is dependent upon and vulnerable to changes within the system as a whole.
“Back then (2010) about 60% of our business was in the predictable area where we were able to use statistical forecasts to predict the future and it was a relatively low effort in those days to be able to demand plan. In a VUCA world, we are in an unpredictable world and our strategy has moved so that there is less use of statistical forecast to predict the future and we are having to really rely heavily on business intelligence which requires a totally new level of effort” – James Lennon, Procter & Gamble
Uncertainty is the inability to know everything fully. This uncertainty is derived from the large number of elements within the system, their nonlinear interactions and their capacity to adapt to local events as they evolve over time. This means that, in these complex systems, the future emerges. The outcome to an emergent process can not be known beforehand. Trying to compute the actual details and define a single future scenario is a lost endeavor. In environments where uncertainty is pervasive, our traditional risk-based analysis of the future breaks down. The only way to respond to this is to perform multiple simulations and experiments that will allow us to explore how things will really play out on the ground and to maintain a diverse and complementary system that is capable of responding to a number of different possible environmental conditions.
Uncertainty is a fundamental feature of complex systems. In other articles, we have already discussed a number of reasons why this is so, such as the very large number of elements we are typically dealing with and the often unknown nonlinear interconnections and interdependencies. But beyond this, the uncertainty is owing to the fact that we are not dealing with a deterministic machine. Complex systems are more like living organisms, they evolve over time and this is a key source of inherent uncertainty.
Determinism is the idea that events and actions are already predetermined. Thus, to know the future, we just need to discover the rules by which they operate. Such an idea is the default assumption within many economic and business theories. Determinism does not allow for individual agency to create the future. In contrary, the idea that the autonomous actions of agents within the system create its overall state is a key assumption within complexity theory. Thus, the future is not determined, it is created through the local actions and interactions of elements.
The elements within a complex system are capable of autonomous adaptation. Whether we are dealing with traders within a financial market, voters within an election or employees within a business, we cannot fully control their behaviour. Their behaviour is a product of their adaptation to events within their local environment. Every time they adapt, they change the environment that other elements are responding to, this is called co-evolution.
Co-evolution describes the interconnected and interdependent development of an agent and its environment. The actions the agents take effect their environment and this, in turn, feeds back to affect the actions of the agents. Co-evolution leads to a very rapid growth in the number of possible future states to a system that are very hard to determine. There are many examples of co-evolution within ecosystems, such as between the honey bee and the plants they pollinate, which in turn provide them with nectar. Each is dependent upon the other – a change in one will trigger a change within the other. Computer software and hardware can be considered as two separate components but tied intrinsically by co-evolution. these systems depend upon each other and advance step by step through a kind of evolutionary process. Changes in hardware or operating system may introduce new features that are then incorporated into the corresponding applications running alongside and vice versa.
Under the paradigm of determinism, the future can be predicted and known for certain. This will inevitably lead to an approach that invests heavily in data, information and statistical analysis. The net result will be a future represented as a set of probabilities upon which we can apply our traditional calculations (risk-based analysis, ROIs, etc.) in order to determine which is the optimal strategy.
If the organization and environment are truly stable, nonvolatile and deterministic – as some are – then this method works well. But in more complex environments it can be no less than dangerous and delusional as it can blind us to the underlying complexity that is really governing how the system functions.
Uncertainty means there is no one optimal solution as there may be many different states to the future environment. The main way to respond to this is to develop a diverse system that is capable of operating within as many different environments as possible, resilient and robust through its multi-functionality. Thus, our strategy shifts from defining one environment in the future that is most probable and creating a single focused strategy in response to this projection, to one of investing more in understanding the major trends that shape the environment and developing projects and organizations that will be able to operate within a wide set of parameters by maintaining diversity within the system.
Our aversion to uncertainty is deep and primordial. It implies the lack of control that is required for our survival, it signals danger. Our strategies and organizations are traditionally designed to function within one well structured and relatively ordered environment – so as to be able to maintain control. Outside of this, their capacity to operate often diminishes very rapidly. Uncertainty and volatile environments require us to develop organizations that are less dependent upon a single structured and ordered environment, but are better able to operate within multiple, even semi-chaotic, environments. Diversity allows systems to maintain operations outside of their usual operating parameters and not to fail gracefully.
According to a recent survey of 1,500 chief executives conducted by IBM’s Institute for Business Value, Global complexity is the foremost issue confronting these CEOs and their enterprises. The chief executives see a large gap between the level of complexity coming at them and their confidence that their enterprises are equipped to deal with it.
When people talk about complexity within the VUCA framework, they are referring to interconnectivity and interdependence. The nonlinear interactions and interdependencies within complex organizations render our capacity for control over the system through direct intervention limited. In complex systems, we cannot always know what the outcome to our interventions will be due to these nonlinear interactions and interdependencies. Thus, our capacity to directly align the elements of the organization towards some desired future goal is limited. The main response to this is for a leader to focus on creating the context that enables the organization to succeed.
Complex systems are highly interconnected and interdependent, but added to this is the fact that things are interconnected synergistically – that is to say, we can’t just simply summate the differing strands of a problem because strands are interdependent and affect each other. This one of the true sources of complexity.
Whereas we may have a good understanding of an organization or environment in terms of its different components and their properties – departments and their budgets or countries and their GDP etc. – what we often lack is knowledge of the interconnections, the interdependencies and synergies. These are less tangible, more difficult to quantify and elusive to our tradition analytical models. But within complex systems, where the connectivity is deep they are very significant to the actual functioning of the system. The net result of this is that we do not always know what the outcome to a direct intervention within the system will be. The Iraq war of 2003 may be cited as an example of this. A nation such as Iraq is a complex system of multiple, densely interconnected social, political, cultural and economic institutions. The Allied invasion was a direct intervention in order to achieve a clear objective of removing the contemporary regime. The result – the output to the system – was a set of nonlinear interactions leading to an unintended chaotic scenario.
Traditionally, we try to exclude complexity – so as to be able to centrally control the system. We divide up the organization into well-defined components so that they will operate through simple cause and effect interactions in a mechanical fashion. Accepting and harnessing complexity means giving up the capacity to centrally control and directly influence the different parts of the organization. So how then can the organization achieve any collective, desired objective?
Managing complexity means, to a certain extent, giving up the traditional concept of strategy and leadership – that is creating goals and directly aligning the organization’s elements towards achieving them – and instead focusing more on creating the context that will enable organizations to be able to succeed, thrive and develop. This means having a vision of where the organization is going, its values and embedding this in the DNA of the organization so that it can adapt to change on the local level, reducing the need for interventions. Thus, it is about the creation of a context that enables the emergence of the desired outcomes. We may not be able to intervene or directly control the outcome to events, but we can manage the initial conditions, the tools, protocols and connections. All of which influences the context within which the organization’s elements generate outcomes.
“I would not give a fig for the simplicity this side of complexity, but I would give my life for the simplicity on the other side of complexity” -Oliver Holmes
“Ambiguity is not, today, a lack of data, but a deluge of data.” – Paul Gibbons
When environments become complex, simple linear cause and effect descriptions of events break down and ambiguity arises due to this lack of models to explain the observed phenomena. Resolving ambiguity means understanding the context within which the event takes place. It requires systems thinking to see the interconnections, to gain different perspectives in order to build up the full context within which an event can be properly understood.
Ambiguity is the quality of being open to more than one interpretation. It results in the haziness of reality; the potential for misreading and mixed meanings to conditions. We can no longer see what is behind things, events just happen and they remain open to a number of different interpretations as to why.
Traditionally, we search for linear cause and effect models to explain phenomena within our environment. Reductionism in management reduces our description of phenomena to a single dimensional perspective, this creates very brittle models that are black and white, either right or wrong. When environments become more complex, our traditional linear cause and effect models start to break down, become redundant and even worse – a hindrance to the acceptance of not knowing. The end result can be a shock, aka a reality check. Due to their black and white nature, linear models do not fail gracefully.
Complex environments require us to invest more in developing models that capture the context within which events play out. This means a switch from trying to analyze and understand the events themselves in isolation to understanding the space around them that gives them context (what artist call the negative space). This is where systems thinking comes in. Systems thinking places a greater emphasis upon understanding the relations that give an object or event its place within some broader environment it is a part of.
Instead of trying to describe and understand the event by describing its properties, systems thinking reasons backward. By first having an overview to the environment, we can understand a system through its connections to other systems. Thus, understanding it with respect to its place within the whole environment it is a part of. By doing so, we can gain multiple different perspectives (through each of its different connections). Each perspective will give us a richer and more robust multidimensional understanding.
The net result is a containment or confinement of ambiguity to a limited set of possible interpretations. Even if we do not fully understand the phenomena, by having a deeper understanding of the context, we are able to have some parameters within which to interpret individual event. Thus, it is still required that we learn to make decisions without absolute knowledge and information and are able to hold two contrasting ideas. Leaders in complex environments need to be able to handle ambiguity and make judgments when the ‘facts’ are unclear or evolving. In other words, not be overly dependent upon quantitative, fact-based methods of reasoning in supporting their decision making. But be able to respond to the overall context instead.