In recent years, artificial intelligence (AI) technology has rapidly transformed industries, and the finance sector is no exception. More and more companies are integrating AI into their financial management practices to boost efficiency, reduce costs, and improve decision-making quality. However, despite the widespread adoption of AI, many companies still struggle to see tangible results.
According to a recent study conducted by Vlerick Business School’s Centre for Financial Leadership and Digital Transformation, while AI technology is increasingly utilized, its actual impact in the finance function often falls short. This gap is not due to the wrong choice of tools or platforms but rather stems from how finance teams are structured and supported.
Take, for instance, Zane Rowe, CFO of Workday, who noted that building trust in AI and managing the growing complexity of data are significant challenges in AI adoption. Similarly, Mike Santomassimo, CFO of Wells Fargo, pointed out that while AI may enhance efficiency, it must “complement,” not “overwhelm,” human decision-making processes. These insights highlight a crucial reality: technology is not the main hurdle. The true challenge lies in how effectively finance teams can integrate AI into their existing structures to unlock its full value.
The latest research underscores that AI adoption should not only focus on technological tools but must also consider how organizational management, team collaboration, and company culture can support AI integration. Specifically, in the digital transformation of finance teams, the use of AI is not just a technical challenge, but a leadership and organizational challenge. Financial leaders need to align both the human and technological sides of transformation to ensure the successful application of AI.
One of the key challenges finance teams face in this transformation is the tension between innovation and collaboration. AI requires teams to experiment with new tools, adjust KPIs, and perform forward-looking analysis.
This experimentation requires speed, autonomy, and room for iteration. On the other hand, AI’s successful integration demands collaboration across departments, which requires time, trust, and effective coordination.
Finance teams must work closely with other business units, such as sales, supply chain, and operations, to ensure AI delivers real value across the organization. Striking the balance between these two aspects—innovation and collaboration—is where friction often arises.
In practice, many finance teams are not in a position to focus solely on either innovation or cross-functional collaboration. As AI applications expand across functions, from supply-chain forecasting to pricing strategies, finance teams are increasingly expected to innovate and collaborate simultaneously. However, these activities require overlapping resources—time, attention, and organizational bandwidth.
Experimentation demands autonomy and rapid decision-making, while collaboration requires coordination and sustained engagement. When finance teams lack sufficient support and try to juggle both tasks at once, progress can stall, leading to frustration and gridlock.
This tension between innovation and collaboration is not insurmountable. Research reveals that successful finance teams don’t try to tackle everything at once. Instead, they prioritize one area—whether that’s experimentation with new AI tools or building trust and cooperation across functions—and gradually expand their capabilities. This step-by-step approach is particularly important for lean finance teams and organizations still in the early stages of AI transformation.
Two crucial enablers have emerged from the research that help finance teams strike the right balance between innovation and integration: employee retention and financial slack. Both factors play an essential role in helping teams move from incremental progress to more integrated and sustainable AI transformations.
Employee retention is critical for successful innovation under pressure. High employee retention reduces the tradeoff between innovation and collaboration by ensuring that teams have deeper relationships, accumulated institutional knowledge, and a quicker ramp-up when switching tasks or collaborating with other functions. This stability fosters smoother coordination and enables more effective experimentation. In practice, CFOs are increasingly recognizing that retaining the right finance talent is key to driving AI transformation.
For example, Doug Chambers, CFO of UScellular, has placed a strong emphasis on developing and retaining cross-functional finance talent. By regularly rotating team members into new business-facing roles, Chambers' team enhances its adaptability and broadens its perspective, which is vital for adopting and applying AI technologies effectively.
In addition to employee retention, financial slack is another critical factor. Financial slack refers to the flexibility in the budget or funding that gives teams the space to experiment without the constant pressure of day-to-day operations.
With this financial buffer, finance teams can test new AI tools, support cross-functional pilots, and absorb inevitable missteps without being forced to show immediate results. Without it, every initiative must justify itself upfront, which stifles creativity and collaboration. Amy Hood, CFO of Microsoft, understands this principle well.
She manages Microsoft’s $64 billion AI budget with strict guardrails to ensure that experimental investments do not come at the cost of the company’s core performance. By ensuring sufficient financial slack, Microsoft can continue its AI transformation without compromising its foundational operations.
Despite these challenges, AI’s potential to enhance the finance function remains undeniable. When properly applied, AI can help finance teams increase efficiency, reduce errors, and provide more accurate forecasting and decision-making.
Finance teams that can successfully navigate the tension between innovation and collaboration will become key strategic partners in their organizations, driving digital transformation and contributing significantly to business growth.
However, as many experts agree, AI’s success is not achieved overnight. Finance teams must continuously experiment, adjust, and optimize strategies to ensure that AI aligns with their organizational needs and priorities.
Successful AI adoption requires flexibility, adaptability, and a deep understanding of the company’s unique circumstances. Only when finance teams are equipped with the right organizational structures, resources, and support can they fully harness the power of AI and emerge as leaders in the digital transformation journey.