The story of AI in finance is no longer about adoption – it is about operating discipline. Active use has more than doubled in two years, but the share of organisations reporting AI is exceeding expectations sits at just 23% – a narrower group than the broader satisfaction figure suggests. The leaders are not adopting more AI. They are directing it at the work where judgment matters, governing it for trust, measuring it for evidence, and resourcing it with a workforce equipped to act. That cycle is the Decision Advantage.
In this environment, AI is becoming a decision-engine for finance, and trust – operationalised through AI governance and AI controls – is the defining advantage
AI in finance: Adoption broadens, performance narrows
AI adoption in finance is broad. More than three-quarters of organisations are leveraging AI in financial planning, reporting and commercial analysis, and 71% report it is meeting or exceeding ROI expectations.
But adoption breadth and exceptional performance are not the same thing. The share of organisations reporting AI is exceeding expectations sits at 23% – a narrower group than the broader satisfaction figure suggests. Adoption is moving faster than the operating capability to translate it into enterprise-wide performance at scale.
Active AI use in the finance function has more than doubled in two years.
AI in finance: From cost lever to decision-engine
AI in finance is producing the strongest gains in judgment-heavy work, not transactional automation. This is where finance has historically been weakest, and where AI has the most leverage. Decision-making quality (70%), decision-making speed (71%) and forecasting accuracy (64%) lead the gains, and organisations deploying agentic AI for finance separate from the rest by 32 percentage points on average, growing to nearly 40% on forecast accuracy and ROI. The leaders are directing AI at the decisions where judgment matters most.
Trust and AI governance: The operating advantage
Governance is often framed as a brake on AI adoption. The data shows the opposite. Organisations that can produce AI audit evidence efficiently report three to six times the rate of significant improvement compared to those that cannot – 33% versus 6% on error reduction, 42% versus 14% on confidence in scaling. Assurance readiness is a stronger predictor of performance than KPI tracking alone.
As AI moves to scale, trust – earned through AI governance, AI risk management and human oversight – separates the organisations capturing value from the rest.
AI literacy and workforce transformation: The next constraint on AI performance
Data quality is among the most cited barrier and the most cited opportunity in this study. Thirty-six percent of organisations identify improving data quality, integration and system interoperability as their greatest opportunity to extract more value from AI in finance – and as one of the most frequently named vulnerabilities. The constraint is not the technology. It is the condition of the data AI depends on.
Most organisations are training the team in place, not rethinking who belongs on it:
- 38% are upskilling existing finance teams
- only 28% are hiring for different skillsets.
Workforce capability is a distinct constraint from data quality, requiring its own response.
Data fluency is the most critical capability need – the ability to assess data quality, interpret outputs and communicate findings the business can act on. It is a professional skill at the intersection of finance expertise and AI literacy. The leaders are doing both: upskilling teams while hiring for a different orientation to data.
Four AI in finance priorities for finance leaders in 2026
Our research points to four priorities for finance leaders looking to translate AI adoption into durable performance.
These four priorities are a reinforcing cycle, not a checklist. Decision-oriented AI compounds with governance; governance scales with measurement; measurement translates into action only with the right workforce. Built together, they create the Decision Advantage.
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The 2026 Global AI in Finance report is based on a survey of 1,013 senior finance leaders across 20 countries and 13 sectors, with annual revenues of US$250 million or more, fielded in March 2026.
Why choose KPMG for AI in finance?
AI is part of our mindset – it has become integral to how we tackle challenges and create technology solutions that empower and enable clients. As the first in the world to be ISO certified for AI by BSI, we are global leaders in AI excellence and deeply committed to taking a Trusted approach to AI use. We collaborate widely through strategic alliances and have developed a range of AI support tools such as the KPMG Clara audit workflow, Tax Digital Gateway platform and our Trusted AI framework.
We provide comprehensive AI governance, assurance, and advisory services, empowering clients with innovative AI solutions across finance and other key business areas, promoting responsible and ethical AI deployment.
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Meet the team leading the way in AI and finance
Nikki McAllen
Partner in Charge – Corporates, Consulting | Global Head of Finance Advisory
KPMG Australia
David P Sofrà
National Leader, Workforce & Innovation | Chief Technology Officer, Tax & Legal
KPMG Australia
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FAQs
AI in financial reporting is increasingly being used to enhance activities such as data analysis, risk mitigation and identification, fraud detection, predictive analysis, speeding up the auditing process, real-time auditing, document gathering, trend analysis, and improving responsiveness.
Embedding AI in financial reporting offers faster, more efficient processes, granular data analysis, improved accuracy, and better predictive power, allowing finance staff to focus on value-adding tasks. Among leading adopters, 57% report that AI exceeds ROI expectations.
The future of AI in finance is more than promising, with 95% of leaders expecting to use generative AI in financial reporting within three years. AI adoption is also expanding globally, widening it to areas like treasury, risk, and tax management. Although emerging economies are slightly behind major markets, the gap is closing, demonstrating AI’s global impact on finance.
The KPMG AI maturity framework assesses AI progress in financial reporting and finance through three survey questions, based on an organisation’s recent and future use of AI and generative AI, as well as AI application in various financial areas.
In the results, you will be categorised as a beginner, implementer, or leader. You will discover how your organisation compares to your industry peers and priority areas for action. Take the quiz
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