KPMG Survey Finds 75% of Finance Functions Using AI for Governance

Why It Matters

The rapid adoption of AI across three‑quarters of finance functions signals a fundamental shift in how companies manage risk, forecasting and compliance. Firms that can couple AI speed with auditability are poised to capture efficiency gains while meeting tightening regulatory expectations. Conversely, organisations that ignore governance and data quality may face compliance penalties, reputational damage, or costly model failures. For CFOs and risk managers, the survey provides a benchmark: achieving assurance readiness is no longer optional but a competitive imperative. The findings also highlight sector‑specific challenges, urging healthcare leaders to prioritize data integration if they wish to reap AI benefits comparable to banking peers.

Key Takeaways

• 75% of finance functions now actively use AI for governance, up from 30% in 2024.
• 71% of surveyed leaders say AI meets or exceeds ROI expectations.
• Banking shows the strongest forecast‑accuracy gains at 71%; healthcare lags at 44%.
• Assurance‑ready firms cut errors by 33% versus 6% for non‑ready peers.
• Only 42% of organisations are fully assurance‑ready; 36% cite data quality as the top barrier.

Pulse Analysis

The KPMG survey underscores a tipping point where AI moves from experimental pilots to core finance operations. Historically, finance departments have been cautious adopters of new technology, but the 75% penetration rate suggests that AI is now viewed as a necessity for staying competitive. The clear performance gap between assurance‑ready and non‑ready firms mirrors earlier waves of digital transformation, where early adopters who invested in governance frameworks reaped disproportionate benefits.

Regulators are catching up fast. In Europe, the European Banking Authority has issued draft guidelines on AI explainability, while the U.S. SEC is probing the use of algorithmic models in financial reporting. Companies that can demonstrate auditable AI outcomes will likely enjoy smoother audit cycles and lower compliance costs. This creates a strategic advantage for firms that have already built internal audit AI capabilities or partnered with external specialists.

From a talent perspective, the dual approach of upskilling existing staff and hiring new AI talent reflects a broader industry trend toward hybrid skill sets. Finance professionals are expected to speak both the language of numbers and the nuances of machine learning. Organizations that fail to develop this talent pipeline risk falling behind not only in efficiency but also in the ability to interpret and govern AI outputs. As AI models become more sophisticated, the line between data scientist and finance analyst will blur, reshaping the career landscape for the next generation of finance leaders.