AI adoption in finance has doubled in two years, with most leaders seeing returns
Active use of AI across finance functions jumped from 30 percent in 2024 to 75 percent in 2026, according to a KPMG survey of 1,013 senior finance leaders across 20 countries. Nearly three-quarters of respondents said AI is meeting or exceeding their return-on-investment expectations.
The shift marks a move from pilot projects to operational deployment. Organizations report concrete gains: 70 percent improved decision-making quality, 71 percent faster decisions, and 64 percent better forecast accuracy over the past year.
Sector performance varies sharply
AI's benefits aren't uniform across industries. In banking, 71 percent of leaders reported moderate or significant improvements in forecast accuracy. In healthcare, that figure dropped to 44 percent-a 27-point gap driven primarily by data fragmentation.
Banking's structured data gives AI a stable foundation. Healthcare and other sectors with scattered data sources struggle to extract the same value. The constraint isn't the technology itself; it's the quality of data feeding it.
Organizations with strong controls outperform peers
Finance teams that can explain and audit their AI decisions-what KPMG calls "assurance-ready"-are seeing outsized results. These organizations report three to six times higher rates of error reduction (33 percent versus 6 percent) and greater confidence in scaling AI (42 percent versus 14 percent).
Yet fewer than half of organizations (42 percent) are fully assurance-ready. Only 29 percent track where AI adoption fails, leaving blind spots around bias, errors, and decision-making breakdowns. As regulators tighten oversight, this gap will widen the performance divide between prepared and unprepared organizations.
Data quality remains the main bottleneck
Thirty-six percent of organizations cite data quality as both their biggest barrier and their greatest opportunity. Better data integration and system interoperability would unlock more value from AI investments.
In response, 38 percent of finance teams are upskilling existing staff. Only 28 percent are hiring for new roles. Organizations pulling ahead are doing both-building capability internally while bringing in specialists who can assess data quality and interpret AI outputs.
Read more: AI for Finance or explore the AI Learning Path for CFOs to understand governance and implementation at the leadership level.
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