KPMG finds AI adoption in finance doubles, assurance readiness matters
KPMG says finance AI is past the pilot phase, with more than three-quarters of firms using it and assurance readiness now separating winners from laggards.
More than three-quarters of organizations now use AI in financial planning, reporting and related processes in KPMG International’s 2026 Global AI in Finance survey. The real test is whether teams can prove those systems are governed, auditable and ready for assurance.
From adoption to advantage
KPMG International launched the 2026 Global AI in Finance: The Decision Advantage survey in London on 11 May 2026. KPMG frames AI in finance as a shift from adoption to advantage and from experimentation to practical value creation.
That shift is visible in the report title itself, The Decision Advantage: How AI is producing value across the finance function. The contents move from an executive summary into chapters on AI as decision-engine, not cost lever, governance and controls build confidence, the assurance readiness gap, data quality and the workforce gap, and from adoption to performance.
On 3 December 2024, KPMG put the share of organizations using AI in their finance operations at 71 percent. Finance teams are no longer merely trying tools anymore. The conversation has moved to which workflows can be redesigned around AI and which controls have to be rebuilt around them.
Where the work changes first
The highest-stakes use cases are the ones that touch the finance calendar and the board pack. Forecasting, close acceleration, narrative reporting, variance analysis and insights generation are the places where AI can change how finance teams work day to day, not just how they demo new software. Those are also the areas where a bad assumption, a weak data set or an opaque output can spread fast across management reporting.
For KPMG consultants and advisory professionals, that means client delivery will increasingly blend finance transformation with data, tax and audit expertise. A team helping a client modernize the close is no longer just shaving time off reconciliations. It is redesigning how numbers are gathered, how exceptions are escalated, and how narrative commentary is produced so leaders can make decisions faster without losing control of the underlying evidence.
For auditors, the practical question is even sharper. If AI is drafting variance explanations or proposing forecast adjustments, who signs off on the logic, how are the assumptions documented, and what is the audit trail when management asks why a number changed? Governance and controls sit beside the assurance readiness gap in KPMG’s 2026 report. The value case now depends on whether the control environment can keep up with the model.
The use cases that matter most in practice
- Forecasting: AI can speed up scenario work and refresh estimates more often, but only if the underlying drivers are reliable and the assumptions are visible.
- Close acceleration: Automation can reduce manual consolidation work, yet finance teams still need review points for exceptions, journal entries and sign-off.
- Narrative reporting: Drafting the words around the numbers can save time, but the message still has to match the data and the controls behind it.
- Variance analysis: AI can flag anomalies quickly, but finance leaders need clear thresholds for what gets investigated and what gets explained.
- Insights generation: This is where AI starts to influence decisions, not just reports, which raises the bar for validation and governance.
Controls, auditability and assurance readiness
The firms that win will not be the ones that adopt AI first, but the ones that can show assurance readiness. For finance leaders, the control question is no longer secondary to the productivity question. If AI is helping produce planning outputs or reporting commentary, companies need to know how models are trained, how outputs are validated, and how evidence is retained.
The report’s focus on governance and controls build confidence and data quality and the workforce gap signals where the pressure lands. Clean data is still the base layer, but the human layer matters just as much. Finance staff need to know how to question a machine-generated answer, test whether the output makes sense, and escalate when a model drifts from business reality. That is a different skill set from simply preparing a spreadsheet or assembling a deck.
KPMG’s Global AI Pulse Q1 2026 found that 95 percent of organizations surveyed reported having an AI strategy, while only 8 percent reported established return on investment. Strategy is now common. Measurable value is not. That gap is exactly where assurance, controls and operating model redesign become the deciding factors.
What this means inside KPMG
It changes how KPMG itself thinks about finance functions, resource planning and management reporting. If AI can reduce manual consolidation and repetitive analysis, internal teams can spend more time on margin management, scenario planning and strategic decision support. That is the same operating model KPMG is asking clients to consider, which makes internal execution part of the firm’s credibility.
It also has implications for career paths. In a Big 4 firm, promotion cycles reward people who can combine technical competence with judgment under pressure. As AI moves deeper into finance and assurance work, the people who stand out will be the ones who can bridge control design, data quality, model oversight and client communication. In busy season, that may mean less time on mechanical tasks and more time on review, challenge and explanation.
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