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“Disruption! Robots take over! Uncertainty rules the realm!” How to keep your cool in this doomsday hype? The answer is an analytical approach to uncertainty

The phenomenon of “fake news” has obvious parallels to the world of digital transformation. Strong beliefs with little evidence and scaremongering with dystopia scenarios. Unlike in politics and news, the populist message in digital transformation is that absolutely everything is going to be changed by digital technology and old rules don’t apply in this new digital world. Following tweets, LinkedIn posts, and “inspiring keynotes” conjures a feeling that artificial intelligence, robotics, big data, lean startups, design thinking, agile responsiveness etc. are disrupting the foundations of everything and only the few enlightened ones will survive the forthcoming “disruptmageddon”.

Unlike in the fake news in public media, there is surprisingly little criticism to balance out the digital disruption prophets and snake oil salesmen on their soap boxes. At least in to counter the “fake news” in media there are things like fact checking websites and a collective pursuit for truth. Where are the objective voices and fact checking institutions in the digital transformation hype? 

One of the popular tools for disruption populists is ‘uncertainty’ and how the business environment is full of it. Uncertainty is used to undermine traditional management principles and any historical evidence. The message is typically that due to ubiquitous uncertainty executives are supposed to throw out the baby and the bathwater, and preferably themselves as well, and start a new agile, lean, responsive, design thinking, autonomous platform business. And because we live in uncertain times like never before, there is no alternative. Otherwise robots, startups, GDPR, blockchain, and GAFA take over. Sounds like a religious cult, doesn’t it?

Nothing uncertain under the sun

Fortunately the concept of uncertainty has been studied, tackled, and addressed for decades in business studies and literature. This is most definitely not the first time businesses face uncertainty. And in many ways the disruptive times we live in are nothing new in the history of business and technology. Yes, many things are changing rapidly as we speak. However, different things are changing at a different pace, and some things are not changing at all.

For example, we can all agree that the consumer photography industry has been disrupted and overhauled. Some things have changed fundamentally, such as the dominant business models in B2C business, and the underlying recording technology. However, some things have not, such as the dominant camera manufacturers or the reasons why people take photographs in the first place (i.e., togetherness, social identity and memories). Also, it is good to bear in mind that the disruption was already the third in the history of consumer photography. The previous two were in the 1840s and 1890s.

The fact is that disruptions and major overhauls in industries and society come and go. There is always something new about them, but there is also loads of similarities in the ways that markets, organisations and industries analyse and respond to business uncertainty. 

Three types of uncertainty

The first step in taking control of uncertainty is to analyse and assess the actual uncertainty at hand. Fortunately this is nothing new in business literature. Frances J Milliken wrote, already back in 1987, a good analysis of three types of perceived uncertainty for an organisation.

3 types of uncertainty
Adapted from Milliken, 1987

First, there is state uncertainty, which means uncertainty about the state of the environment in which the organisation operates in. In other words, this is uncertainty outside the organisation: the market, the industry, the customer base, the world.  

In the current digital transformation world this means uncertainty in regulation, value chains and ecosystems, competition, new emerging technologies, shifting customer needs and expectations, as well as radical changes in channels, processes, and tools. This is, of course, the bread and butter of the “disruption hype” discussed above, and we will get back to that.

Second, Milliken defines effect uncertainty. This is the inability to predict the effects of outside changes on the organisation itself. In digital transformation terms this is about effects on decision making processes, organisational silos, innovation and development speed, legacy systems and structures, and outdated competences and capabilities. This is the world of organisational transformation initiatives and culture transformation projects, where the goal is to adapt and adjust the organisation to outside changes and strategic objectives. Which brings me to the last type of uncertainty. 

Milliken’s third type of uncertainty is response uncertainty. This is uncertainty in understanding what alternatives and options there are for the organisation to take and what is the value and impact of each alternative. In all simplicity, this is the uncertainty in creating a strategy. 

To dig a little deeper into this type of uncertainty, let’s have a look at another great academic paper: Courtney, Kirkland, and Viguerie (1997), “Strategy under Uncertainty”, Harvard Business Review, November-December 1997. 

How uncertain is it? Really? Seriously?

Courtney et al. ask the burning question of what makes a good strategy in highly uncertain business environments? And to answer that questions they provide an approach to understanding the actual levels of uncertainty at hand. This is the perfect sober remedy to tackle the hype and buzz around digitalisation: to stay calm and analyse where are the boundaries of uncertainty in the particular case of your organisation and your business environment. 

Courtney et al. divide the levels of response uncertainty into four*.

Level 1 Uncertainty: A clear-enough future
Adapted from Courtney et al., 1997, and Milliken, 1987

The first level of uncertainty is very low. The future is clear enough to point to a single most probable scenario. This is the traditional case in which there is little state uncertainty and the effects of the market on the organisation itself are predictable. 

Level 2 Uncertainty: Limited alternate futures
Adapted from Courtney et al., 1997, and Milliken, 1987

The second level of response uncertainty is slightly more complex but still limited to few discrete scenarios. Typically, in a level two uncertainty, there are clear alternatives or options that can’t be known in advance, but the impact of their outcomes is predictable. In a level 2 uncertainty the strategy has a limited number of alternatives built in, and once the optional futures are realised the strategy adapts to the outcomes. 

Level 3 Uncertainty: A range of futures
Adapted from Courtney et al., 1997, and Milliken, 1987

The third level of response uncertainty is a range of futures. Unlike in level two, there is so much uncertainty that listing all the potential futures is too much work or simply impossible. However, in level 3, there are clear boundaries for the uncertainty. In other words, not every future is possible, only a limited range.

In practice this means that the strategy has to take into account two things. First, it has to identify the boundaries of the range. The boundaries can be, for example, a specific customer group or a carved-in-stone market regulation. Second, the strategy has to address uncertainty within those boundaries, and therefore, there has to be built-in mechanisms to update and iterate parts of the strategy as the future becomes more certain. 

Level 4 Uncertainty: True ambiguity
Adapted from Courtney et al., 1997, and Milliken, 1987

The fourth level of uncertainty is true ambiguity. This is the doomsday prophet scenario in which nothing is certain and predicting anything is very difficult. If an organisation is truly in this level of uncertainty, then the strategy has to be designed to be a continuously updated process that is adjusted based on continuous gathering of data and knowledge about the environment. 

Certainties in your uncertainty

Courtney et al. write that in their experience half of strategies fall into levels 2 and 3, and most of the rest are level 1. They point out that level 4 uncertainty is very rare, and even when there is true ambiguity it will gradually change into a level 2 or 3. In my opinion and experience, this holds true of many current situations as well. There is hype and a buzz that paints a picture of level 4 true ambiguity, however, a calm and sober analysis shows that typically the uncertainty is level 2 or 3. 

For example, lets think about the banking business, which according to many is in the middle of a disruption and uncertainty. Sure, there are uncertainties about the competitors, technologies, and customer behaviour. Nevertheless, there are also certain boundaries inside which limit the uncertainties. For example, as much as PSD2 is hyped as a disrupting factor it also gives predictability and structure into how APIs and other interfaces are going to work. Also, on the one hand, platform business models are considered a radical new approach. However, on the other hand, there are only a very limited number of scenarios how a platform business model works. By no means is the banking sector in a level 4 uncertainty situation. Depending on the actual organisation at hand, it is most probably a level 2 or 3, just like discussed above.

Summary

Uncertainty is too often used to pull the rug under traditional business planning and good old strategy work. The concept of uncertainty is seldom analysed and grounded to the actual organisation at hand. This populistic hype and buzz is pure sales talk, and should be treated as such. However, uncertainty should not be overlooked. There is true uncertainty in the markets and organisations, and this must be taken into account in strategy work. 

Courtney at al. already in 1997 note that sometimes executives treat uncertainty as a binary: either there is none or everything is uncertain. In the first case, the solution is traditional strategy work, and in the latter case the old textbooks and experiences are thrown out of the window. Like in any human activity, the world is not black or white. Also in business uncertainty, there are different types of uncertainty (Milliken’s three broad categories) and there are different levels of uncertainty (Courtney et al.). Already these two models combined help us to address and tackle uncertainty by chopping it into pieces and analysing the boundaries of these pieces. That is the very first step in harnessing uncertainty and turning it into a feature of a strategy rather than the sales pitch for snake oil treatments. 

Author

Risto Sarvas, service designer who has a long career working with software, design and strategy. He also works as an Adjunct Professor at Aalto University
Sami Loikala, Head of Customer Experience & Design  

Note

This text relies heavily on Tuomo Laine’s work and research on portfolio management and uncertainty in business environments. See his forthcoming thesis for a deep and rich analysis of these topics. Thanks, Tuomo!

* Combining Milliken and Courtney et al. is a connection I make. Courtney et al. do not reference Milliken’s model. 

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