2026 Global AI in Finance Report

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 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 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.

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.

Data quality is among the most cited barrier and the most cited opportunity in this study. Thirty-six percent of organizations identify improving data quality, integration and system interoperability as their greatest opportunity to extract more value from AI in finance — and as one of the most frequently named vulnerabilities. The constraint is not the technology. It is the condition of the data AI depends on.

Most organizations are training the team in place, not rethinking who belongs on it. Thirty-eight percent are upskilling existing finance teams; only 28 percent are hiring for different skillsets. Workforce capability is a distinct constraint from data quality, requiring its own response.

Data fluency is the most critical capability need — the ability to assess data quality, interpret outputs and communicate findings the business can act on. It is a professional skill at the intersection of finance expertise and AI literacy. The leaders are doing both: upskilling teams while hiring for a different orientation to data.