The finance department is getting a major makeover, and CFOs are the ones holding the blueprint. A new survey from Wolters Kluwer reveals that these financial leaders are stepping into unexpected territory as champions of digital transformation. Half of North American CFOs now list digital transformation as their top priority for 2026, defying the old stereotype of finance chiefs as cautious number-crunchers who resist change.
The numbers tell a compelling story. About 87% of CFOs expect AI to play a pivotal role in finance operations next year, while 54% plan to bring AI agents into their departments. This enthusiasm comes with practical goals too. Nearly half of CFOs want to automate routine processes so their teams can focus on more valuable work, and 88% rank staff productivity as a top-three priority.
AI adoption in finance has doubled since 2023, with 56% of finance leaders now using these tools. However, the journey isn’t always smooth. About 45% of finance teams remain stuck in limited pilot programs, and only 17% have integrated AI into their core workflows. The hesitation makes sense when 68% of CFOs admit they don’t know where to start, and 92% express concerns about adoption.
Despite these worries, investment is flowing. Nearly 60% of CFOs plan to increase AI spending by at least 10% this year. They’re shifting budgets toward technology rather than hiring more people, which explains why HR budgets are growing at just 0.7%.
The CFO role itself is transforming. These leaders are moving from financial gatekeepers to strategic architects who enable real-time decision-making. AI handles the routine stuff like quarterly closes, forecasting, and compliance, freeing CFOs to focus on judgment and partnership.
Looking ahead, Gartner predicts that AI-driven tools will replace 60% of finance’s custom analysis by 2029. By 2030, CFOs will spend 30% of their time ensuring data integrity. The message is clear: finance departments face more digital disruption than even IT or marketing, and CFOs are leading the charge rather than resisting it. Modern AI systems often rely on machine learning and neural networks to identify patterns and improve forecasting.
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