KPMG – 2026 Global AI in Finance Report

September 7, 2026 • 0 Comment

Active AI use across the finance function has more than doubled in two years. Many organizations now see meaningful business returns, according to KPMG – AI in Finance report 2026. The strongest gains are concentrating in specific places, and a clear gap is opening between organizations seeing performance at scale and those still investing without commensurate impact.

This report, AI in Finance 2026, examines where those gains are coming from and what’s driving them. It builds on KPMG 2024 research on AI in financial reporting, expanding the focus to the full finance function — including governance, controls, and the workforce. The findings are based on a survey of 1,013 senior finance leaders across 13 sectors and 20 countries.

The dynamic in finance parallels what KPMG’s Q1 2026 Global AI Pulse found at the enterprise level: AI maturity is outpacing the operating capability to translate it into performance. The 2026 finance survey reflects this. Technology and Financial Services organizations are over-represented relative to the market, which means headline adoption figures are partly shaped by the sectors furthest along.

Across sectors, finance leaders are navigating a shared set of operational questions: where AI is producing the strongest performance gains, whether to lead or follow on adoption, how to measure what AI is delivering, how to strengthen the controls around it, and how to build the total workforce, human and AI, that can sustain it. These are live conversations in boardrooms and finance functions, not theoretical ones.

One theme stands out. The organizations moving fastest on AI are those that have made trust — governance, controls, human oversight — part of how performance gets built, not a cost of compliance.

AI in finance moves from adoption to advantage

The story of AI in finance is no longer about adoption — it is about operating discipline. Active use has more than doubled in two years, but the share of organizations reporting AI is exceeding expectations sits at just 23 percent — a narrower group than the broader satisfaction figure suggests. The leaders are not adopting more AI. They are directing it at the work where judgment matters, governing it for trust, measuring it for evidence, and resourcing it with a workforce equipped to act. That cycle is the Decision Advantage.In this environment, AI is becoming a decision-engine for finance, and trust — operationalized through AI governance and AI controls — is the defining advantage.

AI in finance: Adoption broadens; performance narrows

AI adoption in finance is broad. More than three-quarters of organizations are leveraging AI in financial planning, reporting and commercial analysis, and 71 percent report it is meeting or exceeding ROI expectations. But adoption breadth and exceptional performance are not the same thing. The share of organizations reporting AI is exceeding expectations sits at 23 percent — a narrower group than the broader satisfaction figure suggests. Adoption is moving faster than the operating capability to translate it into enterprise-wide performance at scale.

AI in finance: From cost lever to decision-engine

AI in finance is producing the strongest gains in judgment-heavy work, not transactional automation. This is where finance has historically been weakest, and where AI has the most leverage. Decision-making quality (70 percent), decision-making speed (71 percent) and forecasting accuracy (64 percent) lead the gains, and organizations deploying agentic AI for finance separate from the rest by 32 percentage points on average, growing to nearly 40 on forecast accuracy and ROI. The leaders are directing AI at the decisions where judgment matters most.

Trust and AI governance: The operating advantage

Governance is often framed as a brake on AI adoption. The data shows the opposite. Organizations that can produce AI audit evidence efficiently report three to six times the rate of significant improvement compared to those that cannot — 33 percent versus 6 percent on error reduction, 42 percent versus 14 percent on confidence in scaling. Assurance readiness is a stronger predictor of performance than KPI tracking alone.As AI moves to scale, trust — earned through AI governance, AI risk management and human oversight — separates the organizations capturing value from the rest.

Four AI in finance priorities for finance leaders

KPMG research points to four priorities for finance leaders looking to translate AI adoption into durable performance:. Reframe AI around value, not tasks
. Treat AI governance as the ticket to play
. Build measurement into execution
. Shape the total workforce, not just training

These four priorities are a reinforcing cycle, not a checklist. Decision-oriented AI compounds with governance; governance scales with measurement; measurement translates into action only with the right workforce. Built together, they create the Decision Advantage.

“The conversation about AI in finance has changed. Two years ago, the question was whether AI could deliver. Today the question is what it should be deployed to do. The organizations getting it right are the ones using AI to sharpen judgment, not just to speed up tasks. That is where the real return is.” – said Sebastian Stöckle, Global Head of Audit Innovation and AI, KPMG International.

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About the AI in Finance research
The 2026 Global AI in Finance report is based on a survey of 1,013 senior finance leaders across 20 countries and 13 sectors, with annual revenues of US$250 million or more, fielded in March 2026.

Download: KPMG Global – AI in finance report 2026