The most important facts in brief

Artificial intelligence controlling represents the next step in the development of corporate management: away from reactive number analysis and towards forward-looking decision support. AI technologies such as machine learning, automated data analysis or generative tools such as ChatGPT make it possible to accelerate and optimize complex controlling processes and provide them with new strategic depth.

This is not just about software, but about a profound change in the controlling organization: role profiles are changing, requirements are increasing, data quality and IT infrastructure are becoming the focus of attention. Whether reporting, planning, cost analysis or scenario modeling – wherever large amounts of data are processed, new potentials arise, but also risks.

This article focuses on opportunities, challenges and specific fields of application – and shows how companies can make their controlling fit for the future with a clear focus and targeted use of AI.

Why AI is no longer optional in controlling

Controlling is facing a new era: the growing complexity of global markets, increasing demands for speed and precision as well as the explosive growth in available data volumes are making traditional controlling methods increasingly inadequate. At the same time, new technologies, above all artificial intelligence (AI), are opening up completely new possibilities for data-based decisions, automated analyses and strategic corporate management.

In this area of tension, controlling is evolving – from retrospective reporting to a forward-looking, dynamic area. AI-supported systems, machine learning algorithms and intelligent tools such as ChatGPT or specialized AI tools promise enormous potential: faster decisions, more precise processes, personalized reports and new task distributions within the controlling organization.

However, these opportunities are also accompanied by new challenges: ethical questions, security concerns, uncertainties in application and changing role models in the controlling team. In this article, we shed light on how artificial intelligence and controlling interact today, what opportunities and risks exist – and how companies can usefully develop specific fields of application.

What does artificial intelligence mean in controlling?

Definition and differentiation

Artificial intelligence in controlling refers to the targeted use of AI-based technologies for analysis, forecasting and decision support in finance-related processes. In contrast to traditional automation – which maps recurring tasks based on rules – AI is characterized by its ability to learn, pattern recognition and independent decision-making logic. It can react to complex data volumes, recognize correlations and suggest alternative courses of action.

In controlling, artificial intelligence is primarily used where traditional systems reach their limits – for example when combining heterogeneous data sources, in real-time analyses or when simulating scenarios with many variables. This is increasingly being supplemented by generative systems such as ChatGPT, which are able to formulate reports, interpret evaluations or support processes.

Relevance for corporate management

In an increasingly dynamic economy, data availability is becoming a prerequisite for informed decisions. AI offers far more than just analytical depth – it is changing the way companies are managed. Forecasts, variance analyses and risk assessments are not only becoming more precise, but also faster. Decisions can be made on the basis of automated suggestions, which represents a real competitive advantage, especially in highly volatile markets.

AI is not a replacement for controlling, but a tool for optimization. The controller remains responsible for evaluation, context and communication – but AI provides a new form of support that frees up time for more strategic tasks.

Opportunities and potential of using AI in controlling

More efficiency, more analysis, more room for maneuver

The use of artificial intelligence in controlling opens up far-reaching possibilities for improving workflows, information quality and decision-making. Processes such as forecasting, budgeting and variance analyses can not only be automated, but their informative value can also be significantly increased. AI recognizes correlations in complex data structures, suggests alternative courses of action and provides a basis for decision-making in real time.

AI becomes particularly valuable when it takes on repetitive tasks: Data consolidation, plausibility checks, visualization or the formulation of reports with the help of generative AI (e.g. ChatGPT) create freedom for controlling teams. The focus is shifting: away from operational activities and towards strategic evaluation and advice for management.

New perspectives for controllers

The use of AI is not only changing processes, but also the way controlling sees itself. Specialists are developing more in the direction of analyst, strategist and initiator. They interpret AI results, communicate their relevance and anchor data-based management logic in the organization. AI is becoming an assistant – not a replacement.

Concrete fields of application – where AI is already working today

From scenario planning to ChatGPT: examples from controlling practice

The use of AI in controlling is no longer a vision of the future. More and more companies are using AI-based tools for automation, data processing and strategic decision support – especially in the area of finance.

Here are some current fields of application in which AI already offers real added value:

🔍 Typical AI applications in controlling

• Forecasting & simulation: AI models recognize patterns and enable simulation-based planning based on past data
• Automated reporting: tools such as ChatGPT generate annotated reports at the touch of a button – customizable in terms of language for management or specialist departments
• Deviation analyses: algorithms identify outliers or anomalies in real time
• Cost simulation & optimization: Dynamic models help to better understand cost structures and develop alternative courses of action
• Risk assessment: AI-based scenario analyses visualize the financial impact of market changes or strategic decisions
• Data quality check: AI recognizes incorrect or inconsistent entries at an early stage – a real advantage in complex processes

Practical insight: How AI is changing work in controlling

In many companies, AI-based tools are already noticeably changing the role of controlling experts. Instead of spending hours on manual data preparation and report creation, they analyze the results of automated evaluations, interpret trends and advise management – faster, more precisely and more strategically.

This development is particularly evident in the area of finance: forecasts are becoming more data-driven, reconciliations more efficient and work in controlling more versatile. This shows that the real progress is not in the tool itself, but in the expertise with which people use the potential – and actively translate the topic of artificial intelligence into business practice.

Challenges and risks when using AI

Between skepticism, responsibility and system limits

As great as the potential of artificial intelligence in controlling is, the path to productive use is not free of hurdles. This is because technological innovations not only bring progress, but also uncertainty. Many controllers express fear of losing control, of their expertise becoming less relevant – or simply of the speed of change – during training courses or in specialist surveys.

In addition to cultural barriers, technical challenges also play a role. The integration of AI tools into existing IT infrastructures is often complex and requires coordinated strategies and clear processes for data protection, security and responsibilities.

Possible risks at a glance

• Poor data quality can lead to incorrect results and wrong decisions
• Black box problem: AI models are not always comprehensible – this reduces trust
• Costs and resources: Good AI projects initially require time, money and expertise
• Lack of specialists: There is still a lack of trainers, developers or data analysts with a controlling background in many organizations
• Unclear role models: Job advertisements often do not make it clear which skills are really required in “AI controlling”
• Liability and responsibility: Who is liable if the AI makes a wrong decision?

Requirements for the successful use of AI in controlling

What organizations need to make AI usable

The path to the successful use of artificial intelligence in controlling does not start with the tool, but with a clear idea of which problems are to be solved and which expectations are to be met. After all, AI is not an end in itself – it must harmonize with the company’s strategy, processes and culture.

Technical requirements such as stable data architecture, secure interfaces and standardized data formats are essential. However, the organizational environment is just as important: Who uses AI? How is it integrated? Who is responsible for the results?

Strategies for implementation in the controlling organization

From pilot project to intelligent control

The introduction of artificial intelligence in controlling is not an isolated IT project, but a company-wide transformation process. If you want to successfully implement AI, you need more than just a new tool – you need clear strategies, suitable solutions and a controlling team that is prepared to actively develop its work further.

A step-by-step approach is recommended: First, analyze which processes are suitable for AI. This is followed by a pilot project – small enough for manageable risks, large enough to generate measurable benefits. The solution is then iteratively improved and rolled out.

Orientation through visualization and communication

Communication also plays a key role: with clear answers to questions such as “What will change?“, “What are the benefits?” and “How will tasks be redistributed?”, fears can be reduced and expectations managed realistically. Images, infographics and use case demonstrations help to make the abstract topic of AI tangible – especially in organizations where digitalization has previously been treated in a rather abstract way.

---

Future outlook – How AI is changing the job profile of controllers

The increasing integration of artificial intelligence is fundamentally changing the self-image and task profiles in controlling. The traditional job profile of the controller, characterized by manual evaluations and number-oriented reviews, is evolving into a role-flexible, strategically embedded data analyst with management proximity.

With the use of AI – for example through predictive analyses, automated data evaluations or generative tools such as ChatGPT – the skills requirements are shifting. In addition to technical accuracy and process understanding, technological skills, analytical thinking, data interpretation and a keen sense of the corporate context will come to the fore in the future.

The financial sector is also subject to this change: not only operational experts are in demand, but also financial experts who can responsibly manage and interpret AI-supported systems and use them as a basis for decision-making. It is not only experience in dealing with new technologies that is gaining in value – the ability to critically reflect on AI results is also becoming essential.

Editors and specialist portals are picking up on these developments in more and more articles – both in the context of traditional financial publications and in the context of job profile analyses and training and further education topics. They help to provide orientation and realistically calibrate expectations.

AI is changing controlling – but it is not replacing it. It challenges it, complements it and opens up new ways of generating added business value from data.