Artificial Intelligence in Controlling

Artificial Intelligence (AI) is increasingly used in controlling and financial management, transforming how companies manage their financial processes. Although it can at first be easy to get lost in the multitude of possibilities, to effectively assess the opportunities presented by AI professionals need to identify and prioritise the most relevant use cases from the wide array of applications.
 

In this article, we aim to highlight the potential and possible applications of AI in controlling based on our project experience in the finance sector, as well as to shed light on the associated challenges.

AI is revolutionising controlling by enhancing both the efficiency and accuracy of business processes. We've identified the most promising use cases in common IT financial management, as outlined in the following points:

1. Real-time analysis: Real-time analysis powered by AI significantly enhances both the quality and speed of data analysis. This advanced capability allows for quicker reactions to changes in financial performance, enabling more accurate planning, forecasting, monitoring, and reporting. By identifying trends, deviations, and potential risks that might be missed by human observation, AI improves risk management and supports well-informed decision-making. Additionally, AI's ability to rapidly detect suspicious activities through transaction pattern analysis strengthens fraud detection, offering a more robust safeguard against fraudulent activities. 

2. Process automation: AI saves valuable time by automating routine tasks such as document processing and expense categorisation. This frees up time controllers can instead dedicate to strategic analyses and decision-making. 

3. Personalised financial advice: AI-supported chatbots and virtual assistants improve customer experience through instantly providing tailored recommendations that help save time carrying out tasks.

Overall, it is clear that AI is becoming an indispensable tool in modern controlling. By automating routine tasks, enabling precise data analysis, and providing a solid basis for decision-making, AI drives efficiency in companies and enhances competitiveness. Controllers who use AI responsibly can develop their role from pure data management to that of a strategic advisor who proactively creates value for their organisation.

In 2024, the overall AI market is valued at approximately US $184 billion with a projected CAGR of 28.4% from 2024 to 2030. As a result, AI-based solutions have begun emerging throughout a range of business areas, from the automotive industry through to finance and controlling. With this development it is possible to trace a range of AI use-case developments unique to each sector: BMW for example is leveraging AI for financial forecasting, risk assessment, and customer analytics in its Financial Services Digital Ecosystem; the TBM Council is also actively integrating AI into its revolutionary value-management framework (more information found in article here); and SAP and Microsoft are standing out as leaders for integrating AI into a diverse range of their solutions – which we explore in greater detail below. 

SAP's Strategic Shift Towards Comprehensive AI Integration

SAP stands out as a leader in AI integration within enterprise software, utilising artificial intelligence to enhance and innovate its business product offerings. By the end of 2024, SAP aims to have around 100 AI use cases integrated into their solutions with regard to a broad application of AI and machine learning. Some of SAP’s available AI solutions include Analytics Cloud and Business AI. 

Designed to provide integrated analytics capabilities that combines business intelligence, planning and predictive analytics within a single cloud environment, SAP offers the Analytics Cloud for users across finance and controlling functions. This solution has experienced consistent growth driven by both an increasing demand for integrated analytics and planning tools and the shift towards cloud-based solutions. 

With around 300 million global enterprise users working regularly with cloud solutions from SAP and consistent revenue growth throughout 2024 and 2023, SAP is firmly taking advantage of this shift to the cloud with its Business AI solutions. Part of this package is SAP RISE (and GROW for mid-size companies) which launched in early 2021 and offers AI-enabled cloud ERP. RISE consolidates essential cloud migration solutions such as SAP S/4HANA Cloud, business process intelligence, technical migration tools, and infrastructure management into a single subscription. In 2023, SAP also rolled out an embedded AI assistant, Joule, that provides users with intelligent, context-aware responses to queries. Embedded in human resources, finance, ERP, sales, services, marketing, commerce applications, and platform solutions, Joule is enhancing operational efficiency and enabling data-driven decisions. 

Microsoft has laid the foundation for AI tools with its Power platform

Microsoft has established a robust foundation for enabling organisations to streamline their controlling processes through its comprehensive Power Platform, which includes Power BI, Power Automate, and Power Apps. Power Automate, an end-to-end cloud automation platform enables businesses to automate routine tasks and processes efficiently. For instance, Power Automate facilitates the automatic generation of regular financial reports instead of relying on manual data entry. By minimising repetitive tasks, controllers can ensure greater accuracy in their financial reporting and enhance their capacity to respond to real-time insights.

In addition to automation, Microsoft has introduced Microsoft Fabric, an AI-driven central platform designed to enhance data analysis and reporting capabilities. This platform integrates with Power BI, allowing controllers to access and analyse data from various source files more effectively. Microsoft Copilot, a functionality within Fabric, is also useful for assisting users by generating insights and automating data interpretations, enabling controllers to make more informed decisions based on predictive analytics.

In addition to the numerous opportunities to improve forecasting accuracy, reduce the workload of employees and optimise processes, the technical challenges and issues surrounding governance and security must not be overlooked when using AI. These aspects require careful consideration in order to utilise the full potential of the technology without taking any risks. We have summarised the most common challenges for you:

Despite these challenges, the advantages of implementing AI outweigh the difficulties posed – if approached with caution. With proper preparation and support, companies can fully leverage AI's potential in controlling and position themselves for future success.