In VUCA contexts, effective planning does not rely on forecasts but on scenarios based on subjective narratives, on the assessment of their plausibility, and on the analysis of their desirability
“For all of its uncertainty, we cannot flee the future”
BARBARA JORDAN“If there’s one thing that’s certain in business, it’s uncertainty”
STEPHEN COVEY
With traditional planning tools, a leader can study the evolution of a Stable, Predictable, Elementary and Certain (SPEC) context. To this purpose, he uses statistical tools to make forecasts on the expected development of the context and plans based on the results of these forecasts. In a VUCA context, whose development is unpredictable, forecasting makes no sense. The statistical forecasting tools used in the SPEC contexts are therefore inapplicable.
In order to study a VUCA context, the leader can create quantitative models to simulate the impact of different scenarios and of adopting various narratives in each of the identified scenarios. In light of this approach, planning in a VUCA context means exploring scenarios and validating narratives rather than meticulously identifying the sequence of carefully timed actions.
A volatile, uncertain, complex and ambiguous context (VUCA) evolves in an unpredictable fashion. The complexity, which originates from the high number of individuals in the system and from the dense network of interdependencies that characterize them, makes it virtually impossible for the leader to foresee the ultimate outcome of these interdependencies.
This situation differs sharply from the one emerging in Stable, Predictable, Elementary and Certain (SPEC) contexts. The evolution of the SPEC contexts lends itself to make predictions and assign to these forecasts a probability of occurrence. Forecasts made in a SPEC context, therefore, are characterized by risk and not by unpredictability, which instead characterizes a VUCA context.
It is useful to give an example to clarify the difference between risk and uncertainty. If we toss a coin, we know that there are two possible outcomes, head or tails, and if the coin is not rigged, we know that the probability that each face comes out is 50%. The case of the coin toss is an example of an event that gives rise to risky outcomes. Another example is the throwing of a dice. The dice has six faces. So, we can identify all possible outcomes, which are just six. Since we can identify all the possible outcomes, we can also determine the probability that a certain face comes out, for example, the face with the “1”, whose probability is equal to 16.7%.
Let us now consider another example, that is, a case in which it is not possible to identify all the possible occurrences of a situation, which for this reason can be defined as uncertain. Let’s consider the case of a Chinese investor who 10 years ago considered investing in an import-export activity with Western countries and, in particular, the United States. Very few at the time would have considered the possibility that the new US president could have been Donald Trump and that he would have reintroduced, after decades from their abolition, restrictions on international trade that would have triggered a trade war between China and the United States. At the time, the Trump election was not even hypothesized and therefore was not an event for which the risk had been assessed.
Another example that is useful to consider is Brexit. Ten years ago, the United Kingdom was considered a key member of the European Union: historically, it had an active and inspiring role, and had been a member of the Union since 1973. In light of the role played by the United Kingdom and the importance acquired over time by the London financial district, a foreign bank wishing to localize itself in the London financial district would have considered investing in the United Kingdom and in particular transferring the core of its operations to London. After decades of history during which the United Kingdom was among the protagonists of the European Union, no one could have imagined that years later the then British Prime Minister David Cameron would have called a referendum to ask the British people if they preferred to remain in the European Union or leave it. A bank that had considered placing the center of its operations in London in all likelihood would have not even considered the possibility that the United Kingdom could one day consider leaving the European Union and, least of all, that there was risk that one day it would have decided to do so.
The last two are examples of uncertainty, that is, of situations in which it is not possible to identify all the possibilities according to which the reference context could evolve. When it is impossible to identify all the possibilities the context may develop into, it makes no sense to talk about risk and about the probability that any of such possibilities may occur.
It is key to understand the difference between risk and uncertain situations: in fact, different are the analytical tools that can be used to inform decision-making. In the case of risky situations associated to SPEC contexts, statistical quantitative tools can be used, which allow to produce forecasts and measure confidence intervals, that is, the range of values the estimates have a certain probability to fall into. The risk is quantifiable and decisions can be taken based on the risk measured by the quantitative tool.
In the case of uncertain situations like those in VUCA contexts, on the other hand, statistical tools cannot be used, at least not in the same way and with the same objective they are used in the SPEC contexts. Since it is not possible to identify all the possible events, the leader will have to resort to a very different quantitative approach: first, he will have to identify some relevant scenarios, according to which his organization might be able to evolve; then, he will have to build a model to represent the identified scenarios and simulate the impact of his choices in each scenario. The philosophy of this approach is not the quantification of risk and the forecast of the evolution of the context, but rather the validation of the scenarios, as affected by specific choices by the leader.
The model that is created to represent the scenarios should reflect the interpretations that the leader makes of the context and the narrative chosen by the leader to navigate these scenarios. The model is therefore a way to roughly reduce (without, however, eliminating it) the ambiguity of the VUCA context.
Furthermore, the simulation of a model lends itself to effectively inform the choices of the leader adopting an infinite game perspective, whose objective is to preserve the game and the leader himself in the game, rather than understanding how to prevail on other players by ending the game itself. Through specific functionalities and visualizations, the results of the model will allow us to assess what is the impact of the choices made by the leader (for example, if the impact determines an increase or decrease of specific model’s variables), if the impact it is transitory (that is, if it is temporary and, if so, how quickly it fades away) or if it is permanent (that is, if once occurred, if it is irreversible).
When the future is unpredictable, we must create our own future and, in creating it, we want to forge it in our image and likeness.