Artificial intelligence is pushing the finance function to rethink not just the tools it uses, but the very way it operates. According to a study by BearingPoint, finance departments across the world are experimenting with AI assistants, automated accounting tasks, and new forecasting methods. While these innovations offer benefits, they also raise a deeper question: if we were to design a finance department from scratch with AI capabilities, would it look the same as today's? Probably not. AI has the potential to change how financial information is created, analyzed, and shared, yet many departments are still trying to fit it into old processes. In 2023, Eurostat reported that 55% of large European companies were already using at least one AI technology, and nearly a quarter were using it to automate workflows or assist in decision-making. However, a BearingPoint study titled CFO in the age of AI found that while 75% of finance directors expect a high or very high impact of AI by 2030, 74% still see their current AI adoption as minimal or basic. This suggests that the real transformation may not be about simply adding more use cases, but rethinking how financial work is organized overall. A study by Stanford University provides a glimpse of this change. Researchers found that among 79 small and medium-sized enterprises using AI-integrated accounting software, the monthly closing deadline was reduced by 7.5 days, and accountants spent about 8.5% less time on routine data entry. However, these results do not predict the future of large finance departments, but they highlight an important point: when technology takes over part of the work, the focus shifts from time saved to what the company does with the newly available capacity. Instead of asking, "Can we automate this task?" the question should be, "How can we redesign the entire process?" New ways of working are emerging as AI changes the distribution of tasks within finance. Traditionally, finance has followed a linear sequence: executing, controlling, reconciling, consolidating, and analyzing. With AI, systems can collect information, perform controls, identify inconsistencies, and even prepare initial analysis, requesting human input only when necessary. This allows accountants to focus more on exceptions, judgment, and data quality, while controllers shift from assembling information to understanding discrepancies and challenging hypotheses. Planning and forecasting teams, which are seen by 77% of CFOs as the most affected by AI, can move from simply producing forecasts to evaluating multiple scenarios and their consequences. This transformation may also change the pace of finance. Currently, financial operations are largely organized around calendar events, such as monthly closes and annual budgets, because gathering and analyzing data takes time. As AI makes some of these processes continuous, deviations or shifts in assumptions can be detected earlier, allowing for more responsive financial management. This shift could lead to a more continuous approach, where human input focuses on interpretation, arbitration, and decision-making. As these changes unfold, questions about roles and skills are becoming more pressing. The CFO in the age of AI study suggests that 80% of CFOs expect moderate or significant changes in roles within five years. While the goal is not to turn finance professionals into IT specialists, it is to help them understand the systems they use, validate their results, and apply judgment where it matters most. Skills such as economic understanding, risk assessment, and dialogue with business functions are likely to become more valuable. Productivity in finance must also be redefined. Time saved through automation does not automatically translate into cost savings, and faster tasks do not always lead to immediate financial benefits. When AI is applied to entire processes, the focus shifts from the sum of hours saved to the overall capacity gained—such as closing faster, processing more operations, or making better decisions. The CFO's role is to organize this new distribution of work, determining where automation is appropriate, when human intervention is needed, and where responsibility must remain with people. The finance function of 2030 is unlikely to be a version of today’s finance simply enhanced with more AI, nor will it be an organization where human expertise is sidelined. Instead, it may be less focused on routine operations and more on analysis and decision-making, using information in a more continuous way. The key challenge for finance leaders is not just where to apply AI, but how to restructure their departments when AI becomes a core part of their operations. Only then can finance move beyond individual use cases to achieve a more fundamental transformation.