Generic Strategies in a VUCA World

The term VUCA – volatile, uncertain, complex and ambiguous – is currently very popular in business and strategy analysis but it is fast becoming an excuse for inaction! What can be done when everything is VUCA? As Bennet and Lemoine say in “What VUCA Really Means for You” (HBR, February 2014), VUCA is easy to use “as a crutch, a way to throw off the hard work of strategy and planning…” To help address this type of reaction, the authors therefore propose some generic strategies and methods for dealing separately and in turn with volatility, uncertainty, complexity and ambiguity.

 

Here’s how to deal with a VUCA world followed by further implications (“et alors”):

 

Generic Strategies in a VUCA World

 

The authors have classified each sub-term according to 1/ “How well can you predict the results of your actions [“Predictability”]” and 2/ “How much do you know about the situation [Knowledge]?”  Putting the two together yields the following:

 

Volatility

 

The challenge is “unexpected” or “unstable” but it’s not necessarily hard to understand and further knowledge is often at hand.

 

It is high predictability in the context of high knowledge.

 

The generic approach is therefore to “build in slack” and “devote resources for preparedness” (for example building stock to cover variations in demand).

 

Uncertainty

 

Whilst uncertain, the event’s “basic cause and effect are known.” Change is possible but not a given.

 

Knowledge of the situation is high but predictability is low.

 

The generic approach is to therefore use the current “knowledge” to increase predictability (for example running scenario planning).

 

Complexity

 

With many interconnected parts and different variables the volume or the nature of the event (or “situation”) can be overwhelming.

 

Knowledge is actually low because you are missing the key parts, but predictability can be high.

 

The generic response is to hire experts or develop your own specialists to address the gaps in the understanding.

 

Ambiguity

 

Causal relationships are completely unclear – this can be the domain of the “unknown unknowns.”

 

Both knowledge and predictability are low.

 

The generic response is to advance with try-and-see approaches to “test” the ambiguity. Invest in real-time feedback loops.

 

Et alors

 

In a positive sense, these strategies are very simple – for example the approach to volatility might well rank as “common sense”. (However the real challenge with volatility comes from recognising the potential for volatility and planning for it an advance before it happens…) For the other sub-categories, whilst the strategies are sound, the way of dealing with uncertainty and complexity might be subject to cultural preferences. Scenario planning is in some way accepting at least some uncertainty, whereas some cultures which are less accepting of uncertainty might propose heavy planning processes as a strategy to really increase the “knowledge”. (The use of experts however does seem to be a globally generic pan-cultural solution to complexity…) The strategy for ambiguity is, well, ambiguous from a cultural point of view: some cultures would prefer to try-and-see in a practical, pragmatic sense whereas others might try-to-see through rationalising and reasoning…