Finance Process Excellence – how to structure, automate and apply AI in IT tool

We spoke with Dr. Markus Brenner, Senior Expert at Frankfurt School of Finance & Management gGmbH, about how organizations can structure finance processes, automate workflows, and effectively apply AI within IT tools​. At the FINANCE DAYS on May 7, 2026, he will present on Finance Process Excellence. In the interview, he shares practical insights, real-world examples, and key perspectives on how finance teams can leverage automation and AI to drive efficiency and transformation.

You have spent more than 25 years helping organizations design and embed end-to-end process management. Looking back at this journey, how has the role of the CFO function changed most fundamentally with the rise of digitalization and AI, and what aspects have remained surprisingly constant?
Over the past 25 years, the CFO function has evolved from primarily a reporting and control-oriented role toward a far more strategic and data-driven function. Digitalization and, more recently, AI have dramatically accelerated the speed and depth of financial insights. Tasks that once required extensive manual effort - such as consolidation, reconciliation, or variance analysis - can now be automated or supported by intelligent systems. This has shifted the focus of finance teams from producing information to interpreting it and supporting better decision-making across the organization.
Another major shift is the increasing integration of finance with operational processes. Modern finance functions are no longer confined to the back office; they are embedded in end-to-end business processes and play a key role in ensuring transparency and performance management across the enterprise.
What has remained surprisingly constant, however, is the fundamental responsibility of the CFO function: ensuring financial integrity, transparency, and trust. Regardless of how advanced the technology becomes, the finance function remains the guardian of reliable data and sound governance. In many ways, technology has not replaced this responsibility - it has made it even more critical.
 

Successful digitalization does not start with technology, but with processes. From your experience as Head of Process and Enterprise Automation, what are the most common process-related mistakes companies make when they try to introduce AI into finance, and how can they avoid them?
One of the most common mistakes is attempting to apply AI to processes that are not well understood, not yet optimized or standardized. If a process is fragmented, inconsistent across business units, or heavily dependent on manual workarounds, introducing AI will only automate complexity rather than solve it. Another frequent issue is focusing too narrowly on isolated use cases instead of looking at the end-to-end process. Finance processes such as order-to-cash, procure-to-pay, or record-to-report span multiple systems and organizational boundaries. If automation or AI is implemented only in individual steps without addressing the broader process design, the overall impact remains limited.
Companies can avoid these pitfalls by first investing in process transparency. Techniques such as process mining and data-driven process analysis are extremely valuable in identifying inefficiencies and variations. Once processes are standardized and clearly defined, AI can be applied much more effectively.
In short, AI should be the accelerator of a well-designed process landscape—not the starting point.

Data-driven decision-making is frequently described as a cultural challenge rather than a technical one. What mindset shifts do you believe finance leaders and controllers need to make to fully trust and leverage advanced analytics and AI?
One important mindset shift is moving from a primary focus on controlling and validating data toward actively using data to generate insights. Traditionally, finance functions have concentrated on ensuring accuracy, consistency, and governance of financial information. While this responsibility remains essential, the real value increasingly lies in translating data into forward-looking insights that support better decisions.
A second shift concerns how finance professionals deal with uncertainty. Advanced analytics and AI often provide predictions, scenarios, and probabilities rather than definitive answers. Controllers and finance leaders therefore need to become comfortable working with probabilistic insights and combining them with business judgment.
Equally important is fostering a more exploratory mindset toward data. Instead of relying only on predefined reports, finance professionals should actively investigate patterns, test hypotheses, and collaborate closely with business functions. In this role, finance increasingly acts as a bridge between data, technology, and business strategy.
Trust in analytics and AI ultimately develops through transparency and experience. When models are understandable, their assumptions clear, and their results consistently validated against reality, finance teams begin to see them not as a black box but as a powerful extension of their analytical capabilities.

Automation and AI inevitably change roles within finance teams. Based on your observations, which skills will become more critical for finance professionals - and which activities are likely to lose relevance?
Routine and transaction-oriented activities will continue to decline in importance. Tasks such as manual reconciliations, basic data preparation, or standard reporting are increasingly automated and will gradually disappear from the core workload of finance professionals.
At the same time, several new skill sets are becoming more important. Analytical thinking and the ability to interpret complex data will be critical. Finance professionals must be able to translate numbers into business insights and communicate them effectively to management.
Process understanding is another key capability. As finance becomes more integrated with operational processes, professionals need to understand how financial outcomes are created across the value chain. In addition, digital literacy will play a major role. Finance professionals do not need to become data scientists, but they should understand how analytics, automation, and AI tools work and how they can be applied responsibly. In essence, the finance function is shifting from “processing numbers” toward “explaining the business.”

With AI becoming increasingly embedded in finance, how do you see the CFO function evolving over the next few years in balancing efficiency, transparency, and strategic insight. What new challenges or trade-offs will leaders need to manage?
Over the next few years, the CFO function will likely become even more central to strategic decision-making. As automation increases efficiency in transactional processes, finance leaders will have more capacity to focus on forecasting, scenario modelling, and supporting strategic initiatives.
However, this evolution also creates new challenges. One key challenge will be balancing automation with governance. As AI systems generate insights and sometimes even recommendations, CFOs must ensure that transparency, traceability, and compliance remain intact.
Another challenge is managing the balance between speed and reliability. Digital technologies enable real-time analytics and faster decision cycles, but finance functions must still maintain rigorous standards for data quality and control. Finally, the CFO will increasingly act as a bridge between technology, business strategy, and risk management. This requires not only financial expertise but also a deep understanding of digital capabilities and their implications for the organization. In many ways, the CFO of the future will be both a steward of trust and a driver of transformation.

 Više o temi "Finance Process Excellence – how to structure, automate and apply AI in IT tool" na:  FINANCE DAYS: "AI u financijama", 07.05.2026.