The Future of Controlling: New Skills for the Finance Team

The month-end closing follows a familiar rhythm. Data is exported from the ERP system. It is transferred to Excel spreadsheets, checked, and prepared for reporting. These manual tasks are time-consuming and can be prone to errors. At the same time, new technologies are changing the tools available to us. The automation of routine tasks is not a threat to the finance department. It is an evolution. The discussion about the future of controlling and the necessary controlling skills future is therefore central. The role of the finance team is shifting away from pure data collection towards analysis and strategic advice.

From Data Entry Clerk to Data Analyst

Traditional work in controlling is often characterised by a long chain of manual steps. One employee exports raw data, another cleans it, and a third creates the monthly reports from it. These manual steps carry a risk of error. Correcting these errors costs additional time. These resources are then lacking for the actual task of controlling — interpreting the figures and supporting management in decision-making.

Automation breaks this chain. Recurring tasks such as data export, transformation, and the creation of standard reports can be taken over by systems. This not only reduces the error rate but, above all, creates free capacity in the finance team. Digital transformation has been shown to improve the quality of accounting information. It leads to more transparency and can reduce information asymmetries (Accounting, Finance and Computational Intelligence 2024). Better data is the foundation for better analyses.

The time freed up can and should be used for more demanding activities. The skill set is shifting from data creation to data analysis. Controllers are becoming analysts who identify trends, investigate deviations, and understand the underlying causes. Their job is to tell the story behind the numbers. They no longer just answer the question 'What happened?' but primarily 'Why did it happen?' and 'What does this mean for the future?'.

New Tools Require New Skills

The tools themselves are also evolving. New generations of software are integrating artificial intelligence directly into workflows. One example is Microsoft 365 Copilot. Such systems make it possible to synthesise insights and generate content using conversational AI (Microsoft 2026). A controller can ask the system questions in natural language instead of writing complex queries or formulas. The goal is to reduce the fragmentation of workflows and accelerate decision-making (Microsoft 2026).

Using these tools requires new abilities. The required skills are changing. Detailed mastery of a specific software is becoming less important. The ability to ask the right questions is becoming more crucial. An AI system can quickly search large amounts of data and suggest patterns. However, the human must provide the context, critically review the suggestions, and interpret the results. The responsibility for the accuracy of the final statement remains with the finance expert.

A system can show a correlation. The controller must assess whether it is a case of causality or coincidence. They must understand the business logic behind the data. The ability to maintain a critical distance from the results of an automated system is becoming a core skill. Trust in technology must be accompanied by professional scepticism.

The Role of Monitoring and Control

Automated processes are not flawless. They execute what they have been instructed to do. Errors in master data, incorrect configurations, or unusual business transactions can lead to incorrect results. Automation therefore does not relieve the finance team of its control function. On the contrary, this function is becoming more demanding.

The new skill is the monitoring of the systems themselves. Instead of manually checking individual entries, the controller monitors the automated processes. They analyse the logs and check the plausibility of the results. Modern systems support this with anomaly detection algorithms. They can point out unusual patterns or transactions. A study shows that machine learning-based techniques can be significantly superior to traditional models in fraud detection. An Autoencoder Neural Network achieved an accuracy of 0.93 in this regard (Journal of Management and Business Solutions 2024).

When the system reports an anomaly, the controller's real work begins. They must investigate the cause. Is it a data error, a process error, or an actual fraud attempt? The ability to validate a system's reports and draw the right conclusions is crucial. The controller evolves from an executor to a monitor and auditor of automated financial processes.

Strategic Advice as a Core Skill

When routine tasks are automated and data quality is high, the finance team can concentrate on a central task — providing strategic advice to management. Controllers become internal business partners. They use the insights gained to improve the company's financial management and to substantiate strategic decisions.

This role requires more than just financial knowledge. Communication skills are needed to explain complex financial relationships in an understandable way. A deep understanding of the business model is necessary to put the figures into the right context. Analyses can achieve a new depth. For example, the identification of fraudulent financial reports can be improved by combining the analysis of the manager's tone in annual reports with complexity indicators and anomaly detection algorithms (Journal of Management and Business Solutions 2024).

The skill lies in deriving actionable recommendations from data. The finance team no longer just supplies historical data but actively shapes the future. It evaluates investment projects, creates scenario analyses, and helps to identify risks at an early stage. This strategic partnership is a significant added value that a modern finance team can offer the company.

What You Can Do This Month

Developing new skills is a gradual process. It begins with an honest assessment of current working methods. The following steps can help you identify initial potential in your team:

• Identify a recurring, manual process in your month-end closing. Note down every single step from the ERP export to the finished report.
• Check which of these steps are purely data transfers or simple calculations. These tasks are the first candidates for automation.
• Discuss with your team what analytical questions you would ask if you had more time for analysis. What additional information would management need to make better decisions?
• Evaluate your existing tools. Which features of Excel, Power BI, or your ERP system are you not yet using for analysis or automation?

It all starts with a precise understanding of the existing process. This documentation creates a foundation for future improvements. It is the basis for developing the skills within your finance team.

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