How Finance Teams Can Succeed with AI
Researchers at Vlerick Business School’s Centre for Financial Leadership and Digital Transformation have collaborated with CFOs and finance leaders to explore how finance can lead in the AI era. Their recent study builds on a 2023 digital-maturity diagnostic and asks: What does it take for finance to lead, not lag, in AI adoption?
Over the past seven years, working closely with finance professionals across industries has revealed key insights. The 2023 digital-maturity diagnostic, published in MIT Sloan Management Review, helped finance teams evaluate their readiness for AI. Since then, over 100 CFOs and senior finance leaders, many from multinational companies, have completed this assessment.
In a follow-up study, survey responses were combined with company data such as financial slack, headcount stability, and organizational size. This approach captured not only how finance leaders view their AI readiness but also the structural realities they face. The link between these factors and the finance function’s ability to act as a proactive, strategic business partner sharpened the understanding of what drives success in AI-enabled finance transformations—and where efforts often fall short.
The findings reveal a common pattern. While AI adoption is widespread, significant impact remains limited. As noted by The Wall Street Journal, AI use in companies is "stunningly high," but few have seen major returns. The Financial Times also pointed out that AI boosts efficiency but rarely delivers strategic value without organizational alignment. Finance teams experience this tension keenly, as they juggle accelerating AI initiatives while managing risk, compliance, growth opportunities, and accurate reporting. It’s a tough balance.
A study in Management Accounting Research described how 137 finance professionals feel overwhelmed managing multiple digital initiatives like automation, analytics, and AI alongside daily operations. Senior finance executives echo these concerns. Workday’s CFO highlighted the challenge of building trust in AI and handling complex data, while Wells Fargo’s CFO stressed that AI must complement—not overwhelm—human decision-making.
The takeaway is clear: technology itself isn’t the main barrier. The bigger challenge is whether finance teams have the right setup to absorb and apply AI effectively. The recent data reveals where friction arises and what’s needed to overcome it.
The Trap of AI-First Thinking
AI has the potential to transform finance into a true strategic driver. But this shift requires more than just technology. Two capabilities consistently drive progress: a willingness to experiment with AI and strong cross-functional collaboration.
Teams that actively test and apply new AI tools generate actionable insights, challenge outdated KPIs, and provide forward-looking analysis. Meanwhile, finance teams that collaborate across departments build credibility by aligning with business needs and influencing operational decisions.
Both behaviors independently correlate with stronger strategic impact. However, finance teams rarely have the luxury of focusing on one at a time. As AI use cases expand—from supply-chain forecasting to pricing—finance must experiment and collaborate simultaneously. This is where the real tension emerges.
The Hidden Tradeoff
Survey and company data show that modernization efforts can undercut each other when pursued in parallel. Experimentation with AI adds value, but its benefits weaken or reverse when combined with collaboration efforts lacking proper organizational support.
Why? Both activities demand overlapping resources: time, attention, and organizational bandwidth. Experimentation needs speed, autonomy, and space to iterate. Collaboration requires coordination, trust, and ongoing engagement across functions. When stretched thin, finance teams trying to do both without adequate support face stalled progress.
This dynamic is especially strong in AI transformations. Analysis reveals a consistently negative and statistically significant interaction between AI experimentation and cross-functional collaboration. It’s not that either is bad, but handling both simultaneously without the right conditions strains teams.
This trade-off isn’t unavoidable. With the right organizational setup, finance teams can advance innovation and integration. The most effective teams focus first on where they have the strongest momentum—either experimenting with tools or building trust across departments—and then gradually expand their capacity. This approach suits lean teams and organizations early in AI adoption best.
How to Scale Both Innovation and Integration
Two critical enablers support scaling these efforts: employee retention and financial slack. These help teams move from a step-by-step approach toward a more integrated one without overextending resources.
- Employee retention. Stable teams are essential for innovation under pressure. High retention reduces the tradeoff between AI experimentation and collaboration. Long-tenured employees maintain deeper relationships, institutional knowledge, and adapt faster when shifting tasks or teams. This smooths coordination and lets experimentation proceed without repeatedly rebuilding trust or retraining.
- Many CFOs now see developing and retaining finance talent as central to AI transformation. A Grant Thornton survey found CFOs prioritize culture and career development to attract and keep skilled professionals needed for tech-driven change. For example, UScellular’s CFO rotates finance staff through cross-functional roles to broaden perspectives and boost adaptability—key for adopting AI.
- Financial slack. Flexible budgets give teams room to experiment without risking daily operations. This buffer allows testing new tools, supporting cross-functional pilots, and handling inevitable setbacks without immediate performance pressure. Without slack, initiatives must prove value upfront, which stifles creativity and collaboration.
- Microsoft’s CFO applies this principle at scale, overseeing a $64 billion AI budget with disciplined guardrails that protect core performance while enabling experimentation.
Many finance teams today face an AI implementation gap—not due to poor tool choices but because current systems and structures can’t support their ambitions. This is ultimately a leadership challenge. Embracing these principles positions finance to lead enterprise AI efforts rather than just track costs.
For finance professionals interested in boosting AI skills and applying these insights, exploring targeted training can be a practical next step. Resources like Complete AI Training’s courses for finance roles can help build the right capabilities to succeed.
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