AI is now finance's decision engine, but data quality is key: Report
Artificial intelligence is increasingly driving finance decisions and improving performance. Active AI use in finance has more than doubled, reaching seventy-five percent by 2026. Organizations report significant gains in forecasting and decision-making quality. However, data quality and assurance readiness remain critical challenges for value extraction. Effective AI operationalization and…
Artificial intelligence is now emerging as the primary decision-making engine in finance, rather than just a cost-cutting tool, according to a KPMG report. A survey of 1,013 senior finance leaders from 20 countries and 13 sectors reveals that active AI usage in finance has more than doubled over the past two years, increasing from 30% in 2024 to 75% in 2026.
Over three-quarters of organizations are using AI for financial planning, reporting, and commercial analysis, and 71% report that AI is meeting or exceeding expected return-on-investment. The report highlights that AI's biggest impact is in judgment-heavy tasks, with 70% of organizations reporting improved decision-making quality, 71% seeing faster decision-making, and 64% achieving better forecasting accuracy.
AI deployments at advanced stages show even stronger performance advantages, outperforming early-planning organizations by 32 percentage points on average and by nearly 40 points in forecast accuracy and ROI.
However, the report warns that AI adoption alone does not guarantee value. Organizations that are assurance-ready and have robust governance, controls, and measurement in place report three to six times the rate of significant improvement compared to those that are not. Data quality remains a major constraint, with 36% of organizations identifying improving data quality, integration, and system interoperability as their greatest opportunity to extract more value from AI.
Additionally, 38% of organizations are upskilling existing finance teams, while only 28% are hiring for new skill sets.
Finance leaders are advised to prioritize AI investment in planning, forecasting, risk assessment, and commercial analysis, while also embedding governance, measurement, and human oversight into deployment. The report concludes that the competitive advantage will increasingly lie not in whether organizations use AI, but in how effectively they operationalize it to improve decision-making and performance.
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