a KPMG survey of 1,013 finance leaders<\/a> across 20 countries. The gains are concentrated in forecasting, decision-making speed, and commercial analysis, not back-office cost reduction. Organizations running multi-agent AI, where multiple AI systems coordinate tasks autonomously, outperform early-stage peers by 32 percentage points overall and by nearly 40 points on forecast accuracy and ROI.<\/p>\nWhat this means for your business<\/h2>\n
The story here isn’t adoption rates, it’s the gap opening between organizations that have made AI governable and those that haven’t. KPMG finds that companies able to produce AI-related audit evidence efficiently see three to six times the rate of significant improvement compared with those that can’t. Only 42 percent of respondents qualify as strongly assurance-ready. If your finance function is in the majority, the returns you’re seeing from AI are likely the floor, not the ceiling, and the ceiling is moving fast for competitors who already cleared the governance bar.<\/p>\n
The 32-percentage-point performance gap tied to agentic AI maturity deserves particular attention. Agentic AI means systems that don’t just answer questions but execute multi-step processes, like building a rolling forecast, flagging anomalies, and routing decisions, with minimal human intervention at each step. That gap isn’t a vendor claim; it’s a structural outcome of compounding. Organizations further along the orchestration curve get better data feedback, which trains better models, which produce better forecasts. The compounding works the same way in reverse: staying at the planning stage isn’t neutral, it’s a position that gets harder to exit the longer you hold it.<\/p>\n
Data quality is the constraint that finance leaders can actually control right now. Thirty-six percent named it as their single greatest opportunity, and that number is almost certainly understated, because poor data quality also degrades the governance outcomes that drive the performance multipliers above. The workforce picture adds a wrinkle worth watching: 38 percent of organizations are retraining existing finance staff while only 28 percent are hiring for new skill sets. That ratio tells you something about where the real friction lives. The question isn’t whether to invest in AI for finance; it’s whether the data infrastructure and assurance frameworks are solid enough for the investment to compound rather than leak.<\/p>\n
Concept deep-dive: Assurance readiness<\/h2>\n
Assurance readiness means an organization can produce documented, auditable evidence of how its AI systems reached a given output, what data they used, what controls were applied, and who reviewed the result. It matters in finance because regulators and auditors increasingly treat AI-generated forecasts and reports the same way they treat human-signed ones. Think of it as the difference between a financial statement with a clear audit trail and one reconstructed from memory after the fact. Without it, AI adoption creates compliance exposure proportional to how much the AI is actually being used.<\/p>\n
Based on reporting from KPMG Report Highlights AI’s Transformative Role in Finance: Governance and Data Quality Critical for Success, ETEnterpriseai<\/a>, originally published 2026-08-06 00:30:00.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"Share with your CFO AI adoption across finance functions has more than doubled in two years, with 75 percent of senior finance leaders now reporting active use, up from 30 percent in 2024, according to a KPMG survey of 1,013 finance leaders across 20 countries. The gains are concentrated in forecasting, decision-making speed, and commercial […]<\/p>\n","protected":false},"author":1,"featured_media":7931,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[146],"tags":[190],"tmauthors":[],"class_list":["post-7930","post","type-post","status-publish","format-standard","has-post-thumbnail","category-ai-data","tag-cfo"],"_links":{"self":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/7930","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/comments?post=7930"}],"version-history":[{"count":0,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/posts\/7930\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media\/7931"}],"wp:attachment":[{"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/media?parent=7930"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/categories?post=7930"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tags?post=7930"},{"taxonomy":"tmauthors","embeddable":true,"href":"https:\/\/workai.tv\/news\/wp-json\/wp\/v2\/tmauthors?post=7930"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}