Christian Gralingen
Artificial intelligence was supposed to dramatically change the corporate finance function. Forecasts would become more accurate and more frequent. Closing cycles would shorten. Risks would be identified earlier. Scenario planning would evolve from an occasional exercise into a continuous capability. On the basis of those optimistic predictions, many finance leaders have invested heavily in the technology.
However, when CFOs speak in private, a different story emerges. There are proofs of concept that never leave their sandboxes. Models that looked promising in pilot sit unused when the pressure of the quarter hits. Dashboards are produced and refreshed but rarely shape the decisions that matter most. Finance is undeniably busier and more automated but not obviously more forward-leaning in how it helps the organization adapt.
It is tempting to blame the shortfall on technology issues: The data quality is not there yet; the tools are not sufficiently integrated; the models are not trusted; the vendors overpromised. All of those factors matter, but they do not explain why similar AI technologies, introduced under broadly comparable conditions, lead to very different outcomes in finance than in other corporate functions.
After several years of working closely with CFOs and their teams as they tried to apply AI in practice, another explanation became hard to avoid: In many organizations, the technology is moving faster than the way leadership actually works inside the finance function.
When new tools arrive, people tend to talk, decide, and behave much as they did before. Attention gravitates toward getting the close done, explaining variances, defending a single forecast number, and treating deviations as errors to be corrected rather than signals to be explored. AI is introduced into that environment and expected to transform it. Most of the time, it does not.
To understand why, and what might be done differently, it helps to look less at technology adoption and more at leadership practice. (See “The Research.”) In studying the question of AI adoption in finance, we took a simple but demanding view of leadership: Leadership is not a job title or an individual trait; it is the work through which people help their organization adapt under uncertain and changing conditions. In finance, that work shows up every time someone reframes a question, tries a different way of seeing the numbers, surfaces an uncomfortable signal, or helps colleagues adopt a better routine.
Viewed this way, leadership does not sit only with the CFO or a small circle of direct reports. It can be exercised by an analyst who notices something unusual and asks, “What might this mean?”; by a controller who proposes a trial of a different forecasting driver; or by a planning manager who brings several futures into the conversation instead of converging on one.
Leadership becomes visible in the way practices are introduced, tested, and shared. It also becomes visible in who feels empowered to initiate and sustain that work. This view aligns with broader work on digital transformation that frames leadership less as top-down control and more as the orchestration of attention, accountability, and learning across the organization.1
This way of looking at leadership has a sobering implication for AI in finance. If leadership is understood primarily as the CFO’s personal competence or as the formal hierarchy’s right to decide, then the function’s capacity to experiment, learn, and embed new ways of working will always be limited. If leadership is instead understood as shared work in practice, then AI becomes an opportunity to reshape how that work is done. However, the very nature of finance work itself can raise challenges for AI adoption.
When Finance Is Pulled Into a Paradox It Did Not Choose
Finance has always lived with a tension between control and change. Its core mandate is to ensure reliability: accurate reporting, regulatory compliance, and disciplined stewardship of capital. Over time, the function has built processes, controls, and habits designed to reduce surprise. A great deal of finance’s professional ethos is shaped by the imperative of not being caught out.
AI introduces a different dynamic into this environment. It allows finance teams to see more, and earlier. It makes it possible to scan wider sets of signals, test alternative assumptions at low cost, and explore uncertainty in ways that were previously impractical.
The result is that finance is pulled more deeply into a paradox it did not choose. It remains responsible for being the organization’s safe pair of hands while at the same time being asked to become more curious, experimental, imaginative, and adaptive. Finance must protect what is working, even as it helps reinvent what may soon no longer work.
Some finance functions have learned to live with this paradox. They develop ways of working that keep discipline and exploration in constructive tension. Others fall to one side or the other: They either protect the familiar and treat AI as an efficiency add-on, or they embrace every new tool and struggle to make anything stick.
What makes the difference is not simply the tools they buy but the pattern of leadership work that emerges inside the function.
Across many engagements, we saw four recurring activities that particularly mattered for finance teams learning to work with AI: staying alert to what is changing, experimenting in practice, thinking differently about the future, and embedding what proves useful. These are not stages in a process. They are different ways that leadership shows up in everyday finance work. Here, we will present four vignettes, drawn from our research, that show how leadership work around AI takes shape in everyday finance practice. Details have been anonymized and, where necessary, combined to protect confidentiality, but each vignette reflects patterns we observed repeatedly across multiple organizations, rather than isolated or exceptional cases.
When Vigilance Becomes Shared Work
At a European manufacturing company, the central finance team had invested in a sophisticated data platform that provided access to a wide range of external market and supply chain indicators. Over time, the volume of available information increased, but much of it remained in the background. The data was technically accessible, yet it was rarely featured in the conversations that shaped plans or decisions.
That began to change when a financial planning and analysis manager proposed a small adjustment to how the team worked. Each Monday morning, two analysts were asked to bring one external signal th
