Adoption of artificial intelligence among finance leaders has surged to 75% this year, up from less than one-third in 2024, according to KPMG's Q3 AI Finance report released last week. The firm surveyed more than 1,000 senior finance executives across 20 countries. Despite the rapid uptake, the report reveals a widespread challenge: in many organizations, AI implementation is outpacing the ability to translate its use into performance gains across the enterprise, a pattern the report notes persists across industries.
At least 71% of finance leaders say the technology is delivering returns that meet expectations, yet only 23% report that AI is surpassing what they anticipated. The technology is being applied to financial planning, reporting, and commercial analysis. Organizations deploying agentic AI reported stronger performance in forecast accuracy and return on investment, while those with robust governance frameworks saw outcomes significantly better than peers—in some instances, three to six times superior. The report found AI performs best when applied to judgment-intensive work such as decision-making and forecast accuracy, and less effectively in areas focused on cost reduction.
According to Nikki McAllen, global head of finance advisory at KPMG Australia, data and talent remain the top obstacles to AI in financial services two years after they were first identified. "Functions that will likely pull ahead are the ones that recognize these as distinct problems requiring distinct responses," McAllen said. "Data quality is not a technology fix. Workforce capability is not a training plan." The report emphasizes that skills gaps, rather than the technology itself, typically present the barrier to success. Finance leaders identified four top priorities in the latest survey: reframing AI around value rather than tasks, prioritizing AI governance, building measurement of AI into execution, and shaping their workforce beyond just training.
The report explains that businesses are reshaping financial operations around AI to pursue better decision-making and more accurate forecasts, placing pressure on technology leaders to ensure people and workflows are ready to leverage the technology or risk forfeiting its advantages. Data security, privacy, and risk were the top factors affecting AI strategy for nearly all finance leaders earlier this year, KPMG data from the spring showed. A PwC report from April found that while technical skills are critical, CIOs must cultivate human-centric capabilities to advance AI in their organizations, including coaching, agility, judgment, and empathy.
Successful financial teams build the conditions AI requires, such as governance, measurement, and workforce abilities, the report concludes. McAllen noted that "trust, embedded in how performance gets built, is what separates the organizations capturing value from the rest." The takeaway is clear: finance functions that treat data quality and workforce capability as distinct challenges requiring tailored solutions will outpace those that rely on generic technology fixes or standard training programs. The gap between AI adoption and AI performance will likely widen unless organizations address governance and talent as foundational requirements, not afterthoughts.