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Digitisation has radically changed business conditions for executive managers. The Internet has permeated all aspects of human life, transforming societies, economies and cultures in fundamental ways – not least through ever more pervasive networking. This means that connectivity is no longer just a technology-driven process; it is, above all, a social one.
But networking isn’t just confined to human beings – things are also becoming more interconnected. Indeed, the Internet of Things (IoT) is ushering in a fourth industrial revolution, Industry 4.0, by way of machine-to-machine communication and increasingly “smart” manufacturing facilities. As more and more business processes are digitised and networked, so more and more interfaces are springing up between a hugely diverse range of players. The collection and cross-referencing of information from so many different sources are creating vast data sets that play an increasingly important role in business management.
This trend is driving complexity, as shown by this formula:
Multiple participants (nodes)
+ growing density of links (connectivity)
+ high levels of spontaneous activity (activation)
= makes the emergence of non-linear effects (hypes) more likely
Social networks can now interconnect most of humanity. People act as nodes in these networks, able to spontaneously share information without much effort at all. Usually, all it takes is a “Like”, a click, or a share purchase. If certain items of information start resonating across the network, they may start to trend, which can eventually result in a “hype”. The spread of such a hype is not linear – it is more often exponential.
In the financial sector, hypes are something you see on stock exchanges. For example, the key performance indicators for Tesla do not – by any stretch of the imagination – justify the automaker’s current market value. To invest in Tesla is to bet on the future. In terms of communication, this is closely bound up with Elon Musk’s statements on social media; his pronouncements regularly result in strong share-price fluctuations. Certain items of information can generate huge effects within a network, whereas others have little or no influence. Which items of information will result in a hype is, however, difficult or even impossible to predict.
As people and things steadily become less linear and more interconnected, complexity also increases. At the same time, events become less predictable, making planning much harder and long-term forecasts almost impossible. How many senior managers would still honestly dare to predict what their current business activity will look like in 10-15 years’ time? Or which technologies will become dominant in their target markets? Regardless of industry – automotive or pharmaceutical, engineering or services – it is no longer possible to predict how technology will actually develop in any business sector.
Planning is a core managerial mandate, especially in finance. You can only make sensible, long-term investments if you have some idea of how sales markets, production costs and underlying technologies will all develop in the future.
For financial accounting and controlling departments, this is a major headache. They must work with steadily growing data sets and increasingly complex models. At the same time, the many unknown factors involved are causing a rapid decline in the predictive power of their in-house forecasts.
Effectively, complexity is becoming one of the primary challenges facing senior managers. In discussions with corporate decision-makers and executives, we regularly hear how they are “navigating by feel”. But once senior managers are reduced to navigating by feel, one of their key responsibilities goes up in smoke: long-term strategic planning. To lead a company and its workforce in the right direction, an executive must know what developments to expect in the surrounding business environment.
This is where artificial intelligence could become an important part of the solution. In the financial sector in particular, AI is helping to analyse and make sense of the vast quantities of data generated by networking. Autonomous, AI-based forecasting models use auto-adaptive algorithms for continuous self-improvement. Far from finding large data sets a problem, these AI-based models positively thrive on them, because they make complexity easier to analyse. Executives in the finance industry must engage with artificial intelligence if they want to make best use of it. This will also help to clear up some of the confusion and doubt surrounding the future of corporate development.
Co-author: Professor Mike Schulze is Vice Dean and Professor of Controlling, Accounting & Financial Management at CBS International Business School; he also lectures at FS.
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