We are increasingly living in a digital world, where companies attempt to adapt to a new context of Industry 4.0. The authors believe that artificial intelligence and the use of logarithms will alter the game of competition. Digitization is moving our economy away from “financial capitalism” to “data capitalism,” and companies and their boards need to adopt the way they operate and steer the organization to new ecosystems where personalized service becomes part of the new digital strategy. Basically, it is not a battle of AI versus humans, but rather finding a way to enhance the collaboration of AI and humans in organizations. Despite the enormous potential benefits of AI, boards should not ignore the darker side of AI, namely the potential biasedness and sometimes unfairness of algorithms and privacy concerns and the ubiquitous cyberthreats. This is why proper data governance at the board level is needed. The authors suggest that this becomes a critical success factor to be addressed at boards, either as part of the risk management or strategic committee or as a separated digitization committee.
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Today, a majority of people are communicating via social media leaving digital traces which provides the “new oil” - or data – used by organizations that claim to facilitate the quality of our daily life. Indeed, our world is being dramatically influenced and driven by big data. In 2000, about 25% of all data were digitized, about 18 years later, 97% of all data are digitized in one form or another. In the future, data-rich markets will offer individual choices without the constraints of inescapable cognitive limitations.
A majority of people in the developed but increasingly also in emerging markets now communicate via the prevailing channels of social media – be it Facebook, WhatsApp, Instagram, Snapchat, Twitter, LinkedIn to name a few, which are all powered by digital data and algorithms. In the future, data-rich markets will offer individual choices without the constraints of inescapable cognitive limitations. Indeed, data are fast combining the new oil. We could easily argue that we are moving from a finance capitalist system to a form of data capitalism, facilitated by the growing internet traffic or network effect, massive data sets, and the enhanced processing data capacity or analytical power of computer. One of the consequence of this data capitalism lies in the curious shift from causation – as scientists have looked for through appropriate statistical methodology – to correlation where data “speak for themselves”, without necessarily understanding the why behind the correlation. Current (narrow) AI applications are not able to generate causal relationships, as UCLA computer scientist and mathematician Judea Pearl argues (2019). AI today remains an algorithmitization of supervised data, allowing to recognize (sometimes complex and ambiguous) patterns in a speedy and efficient manner, whereby this non-causal analysis and mere correlations are often fast and reasonable cheap. But this narrow artificial intelligence cannot answer the why behind any mathematical equation, and AI cannot reflect yet how humans think in terms of general purpose. The human brain observes and continuously searches for causal links from limited data, based on a certain assumed model – which is quite different of how AI algorithms operate today.