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A KPMG survey of 1,013 senior finance leaders across 20 countries finds that AI’s most meaningful contribution to finance is improving decision quality and speed, not cutting costs or accelerating close cycles. Active AI usage across finance functions jumped from 30% in 2024 to 75% today, with 70% of respondents reporting improved decision-making quality and 71% citing faster decisions. Only 23% said AI exceeded expectations, and just 29% formally track where AI adoption fails, suggesting widespread adoption that organizations haven’t yet learned to stress-test.
What this means for your business
The efficiency framing for AI in finance has always been the safe pitch, the one that gets past procurement and satisfies a board deck. What this data challenges is the assumption that efficiency is where the compounding returns actually live. Finance leaders who are measuring AI against close-cycle speed are measuring the wrong thing. The 68% ROI improvement rate among organizations that formally track AI KPIs versus 58% among those that don’t isn’t a small gap; it’s the difference between deploying a tool and running a program.
KPMG’s framing here is worth examining carefully, because KPMG sells AI advisory and assurance services to the same finance functions it surveyed, which tilts the research toward conclusions that make AI audits and governance engagements sound urgent. The assurance readiness finding, specifically that 60% of agentic AI leaders are strongly audit-ready compared to 42% of all companies, conveniently positions third-party assurance as a prerequisite for leadership rather than a lag indicator of maturity. That doesn’t make the finding wrong, but it does mean the implied prescription deserves scrutiny. The underlying dynamic is real: organizations that can’t explain why their AI conclusions fail are flying blind on their most consequential financial decisions.
The 29% failure-tracking figure is the number that should unsettle any CFO who thinks their AI deployment is going well. Knowing what AI delivers without knowing where it breaks is the finance equivalent of reporting revenue without recognizing churn. The organizations that will separate from this cohort aren’t the ones spending more on AI; they’re the ones building the internal infrastructure to audit their own confidence in AI-generated conclusions before a regulator or a bad quarter does it for them. If your AI governance program can’t answer “where did this forecast go wrong and why,” your judgment improvement story is still theoretical.
Concept deep-dive: Assurance readiness
Assurance readiness means an organization’s data, controls, and processes are structured well enough to survive a formal third-party audit of their AI-enabled outputs. Think of it as the difference between a finance team that can show its work and one that just trusts the output. In AI-driven finance, it matters because automated forecasts and AI-assisted decisions can fail silently, and regulators, auditors, and boards increasingly want traceable justification for conclusions that used to come from a human analyst’s documented reasoning.
Based on reporting from AI’s top value for finance now lies in improving judgment, not efficiency, originally published 2026-07-16 08:51:00.
