Corporate management and planning in artificial intelligence
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AI in controlling

Corporate management and planning in artificial intelligence

Many organizations currently face the challenge of digitally transforming their finance and controlling departments. Organizations need to invest in improving and digitizing business processes using artificial intelligence (AI) in management. This investment includes modern data processing software for business intelligence (BI) and analytics to harness the power of AI.

Why are more organizations than ever investing in AI? Because planning in artificial intelligence boosts the efficiency of financial processes, organizations can spend more time and resources on value-adding activities. The increasing strength of AI tools means a higher quality of automation, which amplifies these benefits .

Corporate management and artificial intelligence

The amount of available data is increasing rapidly, not least due to the digitalization of processes. With it, the desire of companies to make better use of this data for decision-making and planning is also on the rise. This use requires well-founded data analyses and the derivation of decisions from them. Data quality is becoming a central focus, especially when basing decisions on data and analysis you receive via artificial intelligence.

AI management is one of the most critical topics in this context, and many organizations are currently grappling with it. Planning in artificial intelligence deals with the imitation of human-like decision structures via computers and algorithms using mathematical models. One of the goals here is to make better forecasts based on a high-quality database and develop ever more accurate artificial intelligence business plans that are scalable and even automated. In terms of efficiency and automation capacity (for example, of forecasting processes), many rightly see great potential in using AI for planning and corporate management.

Planning and corporate management in the digital age

Planning and forecasting processes are quickly gaining importance in relation to the digital transformation of finance and controlling. Many organizations realize 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. It must also take 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 today’s technologies. This is precisely where AI management offers completely new possibilities. For example:

  • Analyzing non-trivial relationships in data and derive insights about patterns, developments and forecasts
  • Identifying unknown driver dependencies and cause-and-effect relationships
  • Including more extensive data and value drivers in forecasts than a human planner could ever do
  • Validating data entry based on identified rules, taking into account historical data.

Using AI for planning is especially relevant in the area of intra-year forecasting. This is becoming 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. Planning in artificial intelligence can be a helpful relief when used correctly.

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 management provides support for companies when extracting non-trivial insights from past data and using them to make "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 their digital transformation.

However, AI management 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 management can take your planning, corporate management, and artificial intelligence business plans to the next level.

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