Artificial Intelligence in Controlling – an Outlook

The term artificial intelligence (AI) is on everyone's lips. The technology also has a significant impact on controlling. Whether in forecasting or risk management – thanks to the volumes of data available today, AI can increase the competitiveness of companies in many areas. But how can AI be used successfully? And what role do people play in this? 

Artificial intelligence (AI) was first mentioned at a conference at Dartmouth College in 1956. While expectations were limited in the years that followed and many research projects were even discontinued altogether as part of so-called AI winters, the topic has gained enormous momentum in the recent past and has become a megatrend. For some time now, numerous applications have been playing an important role in the lives of many people. For example, search engines, social media feeds and spam filters in email services are based on AI. Active use has also become relatively simple and dedicated training is often not necessary to achieve useful results. Keyword: ChatGPT. AI can already improve and accelerate business processes today and numerous companies are using the technology accordingly. According to a study by the ifo Institute, the proportion of these companies in Germany is currently 13.3 percent. 

While the underlying processes for the way AI works are not new, there are some developments that have paved the way for progress in recent years. On the one hand, companies now have access to large amounts of data (big data). Secondly, there are optimized AI algorithms that can be used to make better use of this data. In addition, the computing power and capacity of computers has increased significantly in recent years. In line with Moore's Law, the number of transistors installed on a chip has doubled approximately every two years since 1965. 

What is AI?  

But what exactly is AI? The term describes self-learning computer systems that use large amounts of data and algorithms to provide human-like intelligence services such as problem-solving or learning, support humans in decision-making and ultimately replace them. There are different levels of development, from algorithms that perform repetitive tasks to big data analysis and self-learning systems. 

The purely mathematical processing of data sets with basic arithmetic operations is also considered AI in some definitions. However, such methods are more likely to be found in the field of data analysis and statistics than in AI and fall less within the specific scope of AI functionalities. 

The way an AI works can be described in the following steps. 1. the acquisition of structured and unstructured information and data similar to the process of human sensory perception, 2. the analysis and meaningful processing of the acquired information and data and 3. the implementation of actions based on the acquired information. A further, 4th step is independent learning through training and feedback based on the previously collected data. The last step in particular is important for the existence of an AI and distinguishes it from other applications, some of which are already complex. 

Areas of application for AI  

The main areas of application for AI in companies that are already relevant today can be divided into different categories.  

On the one hand, there are human-to-machine dialog processes. These offer the possibility of communicating with the machine or computer in natural language in order to avoid complex screen or keyboard interactions. This can take place in verbal or written form. One example of such dialog processes is the voice control of navigation systems or virtual voice assistants such as Amazon Alexa. 

On the other hand, there are machine-to-machine processes that are based on networking technical devices with each other and with a central logic. The data exchange made possible by this is essential for applications in the Internet of Things (IoT), for example. One specific area of application is the prediction of upcoming maintenance work using machine learning processes that utilize sensor data. This can be done, for example, with air conditioning systems as part of building control. 

Intelligent automation (IA) describes the combination of process optimization with AI. It helps companies to improve their internal processes. One of the many areas of application is the automotive industry. Here, IA can be used to predict and adapt production more effectively in order to respond better to supply and demand. Furthermore, robots and automated systems can take over tasks such as assembly, welding, painting and quality control, minimizing employee injuries and providing higher quality products at a lower cost. 

The final category, intelligent decision support, describes the analysis of data using AI algorithms to make more effective decisions. Fields of application include assistance systems in medicine, for example, where AI-based diagnostics can support people. High-quality data is required so that AI can effectively support human users in their decision-making. 

How widespread is AI in controlling today? 

The use of AI in controlling can take different forms, although not all of them are already a reality in companies today. The forms can be divided into the four stages below. 

The first stage is semi-intelligent data analysis, which is used in many companies to supplement human intelligence. Specific AI techniques used at this level of AI deployment include automated data cleansing and preparation, pattern recognition and forecasting, and automated financial reporting and analysis. 

The next higher level involves proactive AI support based on an even more comprehensive data basis. A corresponding intelligent assistance function can support controllers in performing their tasks in various work situations. This can be done, for example, in the context of interpreting complex financial data, generating real-time reports or assisting with budget preparation. 

The content-enhanced use of AI represents the third stage of the forms of AI use in controlling. Here, AI can not only interpret data, but also provide context-related recommendations for controlling measures based on company-specific goals and targets. In this form, the AI has extended decision-making autonomy and can perform certain repetitive tasks independently or make smaller decisions based on recurring patterns in the data. 

The fourth stage involves the comprehensive strategic use of AI. Here, the AI is able to analyze data autonomously and act strategically based on the results or propose strategic measures on its own. All approaches from the previous stages are combined here. The most important source of learning for the AI at this stage is the controller's behavioral patterns, which are observed by the AI system. It can thus observe all relevant cause-and-effect relationships in the controlling context of the respective company and learn from them. 

Use cases from the first two stages can already be found in businesses today. However, AI has not yet reached the stage where it can independently identify optimization opportunities and name the most efficient approach. This means that the technology is still a long way from the fourth stage mentioned above. 

Examples of AI in controlling that are already relevant today  

At the first two levels mentioned above, there are various fields of application for AI in controlling with different levels of benefit for stakeholders.  

One of these areas of application is forecasting. By evaluating past data, for example on supply, demand, sales figures or production costs, AI tools can integrate various historical data streams. If this data is modeled forward in time, reliable forecasts can be created for numerous areas of the company. 

In addition to forecasting, the planning process can also be made significantly more efficient with AI systems. They can be used to find patterns and abnormalities in transactions, financial data and business reports and identify drivers, for example for sales planning. In the planning process in particular, it makes sense to also use external data such as economic indicators in order to better assess developments in the market, competition or risks, for example.  

AI can also help with risk management in controlling. By processing data from various sources and recognizing patterns, AI algorithms can identify risks at an early stage. Modern tools are for example able to check contracts and ensure that applicable regulations are complied with. 

AI can also support data evaluation. Thanks to AI, it can be largely automated without the need for large numbers of staff to provide information. Data from various functional areas such as sales, production or logistics can be quickly evaluated and immediately presented in graphical form with the help of AI. 

What should be considered when using AI in controlling  

For the embedding of AI in a controlling department to succeed, certain basic requirements must be met in the company. If the company has problems generating, processing and completing data or is struggling to set up a seamless digital tool landscape, the introduction of AI will be difficult. An implementation should therefore only be considered if a comprehensive database and relevant tools are available and the processes in the company are prepared for the change. The use of an AI-based system in controlling only makes sense if, for example, standardized processes and a complete database have been created by using an ERP system. 

It should also be noted that when working with AI, there is a human component that formulates data models and selects algorithms. This is because, at least at the current stage of development, input from a human is necessary so that the data queries result logically from everyday business and correlations are not incorrectly interpreted as causalities. At the same time, humans can ensure that the data is comparable and consistent. The symbiotic interaction between AI and humans therefore currently offers the greatest opportunities to increase effectiveness in the company and achieve competitive advantages. 

A clear objective should also be defined before AI is used in controlling. The technology can only be successfully integrated into the various stages of the value chain if it has been precisely formulated what the use of AI in controlling should achieve. Possible goals could include reducing the probability of errors, improving the quality of decisions or providing information more quickly.  

In order to achieve such goals, a roadmap should also be drawn up. Questions that controlling managers should ask themselves are: How can I prepare my data well for working with AI? How can I extract information from the data? How can I derive conclusions from the data? And how can I use these conclusions and external data to create models? 

Conclusion 

The use of AI is changing processes in companies in the long term. This also applies to controlling. AI offers numerous potential applications for this area, some of which are already in use today. It not only makes day-to-day work easier, but also provides new insights. In many cases, neither a data scientist nor an AI specialist is required for this. For most controlling departments, the question of whether to use AI is consequently not one of if, but when. Managers should therefore start to engage with the technology early on, identify useful areas of application in their company and build up relevant skills in the team. 

The comprehensive influence of AI will also have an impact on the requirements profile for employees in controlling. Qualification characteristics such as a high affinity for numbers, experience with spreadsheets in Excel and industry knowledge will be less relevant in the future, while expertise in the use of BI tools and an understanding of IT systems will become increasingly important.  

Despite the major upheaval which AI entails, it will not make controllers superfluous in the foreseeable future. Rather, they will be freed up for activities that are critical to value creation in the company, as they will have to spend less time collecting and processing data. In addition, AI will enable them to create more in-depth data analyses than before. As a result, new areas of responsibility will develop and there will be a shift in the work areas of controllers and machines.