AI adoption in finance doubles, but assurance readiness determines who wins, KPMG finds

Key findings

• Active AI use across the finance function has more than doubled since 2024 (30 percent to 75 percent), with nearly three-quarters of leaders (71 percent) reporting AI is meeting or exceeding ROI expectations.
• Organizations report significant improvements in decision-making quality (70 percent), speed (71 percent), and forecast accuracy (64 percent).
• Assurance-ready organizations report three to six times higher rates of error reduction (33 percent vs. 6 percent) and greater confidence in scaling AI (42 percent vs. 14 percent).
• More than a third of organizations (36 percent) cite data quality as both their top barrier and opportunity.

AI is pushing finance beyond basic process automation and into areas where judgement matters most such as forecasting, planning, and risk assessment, where finance has traditionally struggled. For example, 70 percent of organizations report improved decision-making quality, faster decision-making (71 percent), and stronger forecast accuracy over the past year (64 percent).

However, performance varies sharply by sector. In banking, 71 percent of leaders report moderate or significant improvements in forecast accuracy, compared with just 44 percent in healthcare—a 27-point gap driven by differences in data foundations. Banking benefits from structured data that gives AI a foundation. By contrast, sectors such as healthcare face fragmented data sources that limit what AI can do. Ultimately, stronger data foundations are what enable judgment-driven AI use cases to scale and perform.

As AI becomes more embedded in finance processes, organizations with strong controls, governance, and auditability are seeing a clear performance advantage. The report suggests assurance-ready organizations, those that are able to produce audit evidence and explain it, are delivering stronger outcomes, reporting three to six times higher rates of improvement in error reduction than their peers (33 percent versus 6 percent) and are more confident in their ability to scale AI (42 percent versus 14 percent). When finance teams can clearly demonstrate how AI decisions are generated, trust shifts from assumption to proof, enabling more consistent performance and more confident adoption at scale.

At the same time, the report suggests many organizations are still building this capacity. Fewer than half of organizations (42 percent) are fully assurance-ready for AI-enabled finance processes, and only 29 percent track where AI adoption fails, creating partial visibility that shows what AI delivers but not where it breaks down, such as how outcomes are produced or where risks like errors or bias may emerge. As regulators and external auditors increasingly require proof that AI-enabled finance processes are controlled and explainable, assurance-readiness becomes even more essential for organizations to sustain performance and maintain trust with stakeholders.

The biggest barrier to AI success isn’t technology, it’s data. The survey shows that 36 percent of organizations cite data quality as both their biggest barrier and opportunity, pointing to improvements in data integration and system interoperability as key to unlocking more value from AI in finance. In response, most organizations are focused on upskilling their existing finance and internal audit teams (38 percent), while fewer are choosing to hire for new skills (28 percent). The organizations pulling ahead to close this data quality gap are choosing to do both: upskilling existing teams while also bringing in people who can assess data quality and interpret outputs.

Marie-Helene De-Messou, Senior Associate, Global Corporate Communications, KPMG International

T: +1 202 569 6824
E: mdemessou@kpmg.com

The Global AI in Finance survey was conducted among 1,013 C-suite and senior finance leaders working in organizations with annual revenues of at least US$250 million (US$500 million in the United States). Participants completed the survey online in March 2026 across 20 countries, territories and jurisdictions in the Americas, EMEA and Asia-Pacific.

Respondents included C-suite executives and direct reports in finance, risk and audit, technology and general management roles, all with direct knowledge of AI usage within their organization's finance function.

Sectors: 13 sectors including Technology and Telecommunications, Financial Services, Industrial Manufacturing, Healthcare, Energy and Natural Resources, Consumer and Retail, and Government and Public Sector. Technology and Financial Services together account for 58 percent of the sample (36 percent and 22 percent respectively).

Geographies: Americas, Asia-Pacific and EMEA.

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