Planning and corporate management with artificial intelligence
Many organizations are currently facing the challenge of the digital transformation of their finance and controlling departments. To this end, significant investment is needed for the improvement and digitalization of business processes as well as investment in data processing and modern software applications for business intelligence (BI) and analytics. The goals pursued are to increase the efficiency – as well as the quality and automation – of financial processes in order to gain time and resources for value-adding activities.
Artificial intelligence for planning and corporate management
Not least due to the digitalization of processes, the amount of available data is increasing rapidly and with it the desire of companies to make better use of this data for decision-making and planning. This use requires well-founded data analyses and the derivation of decisions from them. Data quality is therefore becoming a central focus, especially if decisions are to be based on data, for example on the basis of artificial intelligence (AI). AI is one of the most important topics in this context, and many organizations are currently grappling with it. It deals in the imitation of human-like decision structures via computers and algorithms using mathematical models. One of the goals here is to make forecasts based on a high-quality database and AI more accurate, scalable and even automated. In terms of efficiency and automation capacity (for example, of forecasting processes), many rightly see great potential in the use of AI for planning and corporate management.
Planning and corporate management in the digital age
Planning and forecasting processes are quickly gaining in importance in relation to the digital transformation of finance and controlling. Many organizations are realizing that a purely reactive analytical view of the past is no longer enough. New challenges such as speed, agility and foresight require that planning and forecasting in the future must be carried out on a short-term, automated and, if necessary, rolling basis, taking into account driver-based cause-and-effect relationships. The aim is to reverse the planning and corporate management paradigm towards a proactive forecasting approach that makes the best possible use of the options and technologies available today. This is exactly where AI offers completely new possibilities. For example:
- to analyze non-trivial relationships in data and derive insights about patterns, developments and forecasts;
- to identify unknown driver dependencies and cause-and-effect relationships;
- to be able to include more extensive data and value drivers in forecasts than a human planner could ever do; and
- to validate data entry on the basis of identified rules, taking into account historical data.
Especially in the area of intra-year forecasting – something that many companies remain rather reserved about doing, but where the time pressure for short-term forecasts based on rapidly changing data has increased immensely – AI can be a helpful relief for planners.
Summary and challenges
Making "intelligent decisions" based on data is a core requirement in our fast-moving digital world. It is becoming ever more difficult to take all relevant influencing factors into account and to do this in the short term and on a sound basis. AI can provide helpful support for companies when it comes to extracting non-trivial insights from past data and using them for "intelligent decisions". The optimal use and analysis of available data for better decisions and forecasts of the future is one of the central challenges for organizations in the context of their own digital transformation.
However, the use of AI also brings challenges and requires certain framework conditions that companies need to create. First and foremost, these include a high-quality database from which patterns, correlations and developments of the past can be learned. Only the right data in the right quality, the required granularity and sufficient history allow for solid forecasts. A deep understanding of causal relationships is essential (cause-effect relationships). Patterns, correlations and developments learned from past data must also be valid for the future so that they can be learned and used for high-quality forecasts or data validation. If these basic conditions are in place, AI is able to take planning and corporate management to the next level.