The financial leadership suites of major corporations are increasingly becoming the front lines for artificial intelligence strategy, a significant evolution from earlier, more experimental phases. This shift places CFOs at the heart of enterprise AI adoption, moving beyond mere budgetary oversight to active strategic partnership. Insights from finance chiefs at companies like Dataminr, Adobe, and Huntington Bancshares reveal a multifaceted approach to integrating AI, balancing innovation with risk and efficiency.
At Dataminr, AI is not merely an initiative; it’s a foundational element of annual planning and a constant consideration in budget allocation, as noted by CFO Tiffany Buchanan. She emphasizes AI adoption as a non-negotiable across all operational functions, leveraging native capabilities within modern SaaS platforms and accessible AI tools. This perspective reframes the CFO’s role, positioning them as a strategic partner to the CEO, directly influencing capital deployment towards growth and efficiency-driven AI endeavors. The company’s approach underscores a belief that AI is now a ubiquitous enabler, not a specialized project.
Dan Durn, CFO and EVP of finance, technology, security, and operations at Adobe, articulates his company’s AI strategy through the lens of enhancing “organizational velocity.” He describes this as compressing the timeline from initial insight to actionable outcomes within Adobe’s data-rich environment. Durn highlights that integrating AI throughout operations allows teams to identify crucial signals more rapidly and respond with greater effectiveness. However, he cautions that technological integration alone is insufficient for success. A supportive culture, a commitment to continuous learning, and leaders who prioritize intellectual curiosity over rigid playbooks are equally vital components.
For Zachary Wasserman, CFO at Huntington Bancshares, the landscape of AI adoption within a highly regulated banking environment presents a distinct set of challenges. He points out that the rapid advancement of model capabilities means even a slight delay in implementation can result in a significant competitive disadvantage. To navigate this, Huntington has developed a generative AI risk framework, systematically prioritizing use cases based on their risk level and mandating human oversight for applications with higher impact. This structured approach allows the bank to pursue AI innovation while adhering to stringent regulatory requirements and managing potential pitfalls.
These executive viewpoints collectively illustrate a broader trend: finance organizations are transitioning from tentative AI experimentation to deliberate execution. Sommer Frazier, managing director of finance transformation at KPMG US, observes that while many companies have adopted some form of AI, a significant number struggle to move beyond pilot projects to scaled deployment. Common obstacles identified include issues with data quality, inadequate governance structures, infrastructure deficiencies, talent shortages, and cybersecurity concerns. Frazier underscores the critical need for finance leaders to champion data stewardship, establishing robust standards and governance frameworks. While business teams maintain day-to-day accountability for their data, finance plays a crucial role in setting the overarching strategy and ensuring data integrity.
Generative AI, once a niche technology, has rapidly become embedded in everyday enterprise tools, from productivity software to core financial systems. Finance teams are now leveraging it for practical applications such as summarizing meetings, drafting variance commentary, analyzing contracts at scale, and identifying patterns in pricing and payment terms to enhance financial performance. Looking ahead, Frazier predicts that by 2026, the industry will see a notable shift toward scaled AI agent orchestration. This future state envisions employees managing networks of AI agents, rather than discrete processes, freeing up finance talent for more complex analysis and strategic decision-making. This evolution suggests a profound reallocation of human capital towards higher-value tasks, fundamentally reshaping the finance function.
