However, despite positive examples from experience, a realistic expectation is appropriate with regard to the forecast accuracy of machine planning and forecasting as there are limits to the ascertainability and planning capability of AI in a VUCA environment ( Caglio, 2003 Warner and Wäger, 2019). The differences between human and machine forecasting can be plausibly explained by the complementarity of human and machine information processing ( Harris and Wang, 2019 Hofmann and Rothenberg, 2019). The few field reports from predominantly large corporations seem to confirm the possibility of predictability through AI and the superiority of machine forecasts. This revived the belief in the predictability of the future (see Figure 1), at least until the outbreak of the corona crisis. Access to new data sources (big data), almost unlimited computing power and AI systems has quickly led to keywords such as predictive analytics and the first applications of AI-based machine forecasts ( Batistič and der Laken, 2019 Brands and Holtzblatt, 2015 Earley, 2015 Mikalef et al., 2019 Qasim and Kharbat, 2019). With the advent of digitisation, however, a paradigm shift seems to have begun. In response to the then “new normal”, concepts such as modern budgeting, scenario planning, bandwidth planning and rolling forecasts were presented, which in various ways propagated the abandonment of detailed, precise planning and forecasting ( Lepori and Montauti, 2020). In the course of the 2008 financial crisis, the term VUCA, which stands for volatility, uncertainty, complexity and ambiguity, became established as a synonym for the problem of the predictability of future developments ( Bennett and Lemoine, 2014). At the beginning of the 2000s, the Beyond Budgeting Round Table (BBRT) loudly called for an end to classical planning. The complaints about an uncertain and difficult to plan environment, the premature “being outdated” of planning and the budgetary “power games” have a long history. This article examines both the limits of the forecasting capabilities and the possible applications of the automated forecasts and provides a derived research agenda for our field. At the same time, there are great expectations from the AI systems used in controlling ( Seufert and Treitz, 2019). The percentage of companies using AI in controlling is therefore negligible. According to a study by the German Federal Ministry of Economics, only 5% of German companies currently use AI in one of their divisions ( Feser, 2020). While the automation of routine activities, particularly in large companies, is progressing successfully, the support of analytical activities seems to be considerably more difficult. On the one hand is the automation of repetitive routine activities (robotic process automation) and on the other hand is the support or automation of demanding analytical activities (such as machine forecasts and artificial intelligence ).
The influence of digitisation is directed at two very different areas of management accounting and monitoring (summarised as controlling henceforth). Introduction: a paradigm shift in planning, budgeting and forecasting?
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