Generative AI systems such as ChatGPT and Copilot have sparked a revolution that is rapidly changing all areas of our working world, and finance departments are no exception. All of a sudden, machines are now able to accomplish tasks which were previously the preserve of humans – be it writing texts, creating images, or interpreting and compiling complex content.
The new generation of dialogue-based generative AI can provide plausible answers to almost any question, create Excel formulas and code, or even explain financial reports. Although the development and regulation of generative AI is highly dynamic, it has long been clear that generative AI has the potential to create a quantum leap in finance transformation. The hype around AI is more justified than that around any other technology in the past ten years.
Key points
- Generative AI is revolutionizing the world of business. Its universal nature means that it can be used in a variety of ways in almost every sector.
- In finance, there are numerous use cases for generative AI systems – from more flexible reporting, to greater automation in accounting, to simulated scenarios for improved decision-making.
- Statutory regulations for the new generation of AI are currently being debated. Alongside these regulations, there are also a range of company data protection requirements and ethical standards that need to be considered.
The new generation of AI: a master of many trades
Generative AI is a new class of AI that builds on established methods such as machine learning and deep learning. Its disruptive potential for the business world is already being felt far and wide. It is still in the early stages of development, but it is evolving at a phenomenal pace.
These new AI systems are based on so-called foundation models, which are trained using very large quantities of data. Proceeding from these models, the AI systems are then able to create content. Users interact with these systems via an input screen, also known as a prompt, in a similar way to traditional chatbots.
In contrast to traditional machine learning and deep learning applications, generative AI systems are true generalists, rather than being trained for one specific use case. This means that they are able to speed up recurring tasks, create new content and assist humans in numerous work tasks – whether it’s writing emails and program code or analyzing reports. In order to provide these systems with specialized knowledge, foundation models can also be trained with company-specific data, and thus optimized to related use cases.
Game-changer for finance transformation
Although generative AI is still a very new technology, it is likely to play a key role in modernizing finance functions. It can be lucratively employed in planning, accounting, reporting and decision-making in many areas of responsibility for finance departments. For example, it can automate reporting and many accounting operations, generate scenarios, and assist with data integration.
When used correctly, generative AI is not only capable of automating simple and repetitive tasks, but also more challenging tasks such as data analysis and preparing reports. It can also act as a virtual assistant and sparring partner, assist with strategic planning and create new baselines for decision-making. It therefore helps with transforming the finance function from an administrative department to a strategic partner.
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Use cases for the finance function
There are many promising use cases for generative AI across the entire spectrum of typical tasks within a finance department. These use cases can help to significantly reduce costs, speed up processes and increase productivity at the same time.
New legal provisions are being debated
Despite the myriad possibilities offered by generative AI, there are also risks, regulatory frameworks and compliance issues that need to be taken into account. To date, over 170 standards relating to AI have been published and many more laws are in the works. At the European level, this includes, in particular, the EU AI Act. It classifies some applications as high-risk AI and imposes a number of transparency requirements.
In order to use AI responsibly, companies should be guided by a number of core principles. First and foremost is the primacy of human action. And when it comes to technical implementation, data protection must be taken into account alongside robustness and security, and a quality management system must be implemented. It is essential that there is transparency with regard to how AI systems arrive at their results and what these results are based on in order to prevent discrimination and ensure fairness.
Developing prototypes and starting pilot projects
Generative AI creates decisive benefits for business by boosting efficiency and productivity. In order to remain competitive, companies should evaluate the use of systems such as ChatGPT and gather practical experience as soon as possible. Firstly, suitable use cases must be identified and tested. The right framework must also be created in order to ensure safe use of AI. We can support you from initial brainstorming right through to reliable implementation.
As a starting point, we recommend use case ideation workshops, in which we will work with you to analyze challenges from a user perspective and iteratively develop solutions. Once a number of possible use cases have been identified, we will assess and prioritize them, and then carry out a joint cost-benefit analysis. This will help you to identify promising generative AI use cases that address specific problems and deliver clear added value, even during a pilot project.
“Generative AI offers significant potential for modernizing the finance function in a wide variety of use cases. The key to its successful implementation lies in establishing a framework that facilitates the integration of these innovative solutions within enterprises.”
Prof. Frauke Schleer-van Gellecom,Partner, PwC GermanyRelated content
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