Artificial intelligence is quickly becoming a core part of the finance function, and a new KPMG survey shows just how rapidly the shift is happening. Finance leaders are moving beyond small scale experiments and beginning to integrate AI into critical workflows, decision making and financial operations.

KPMG’s 2026 Global AI in Finance research surveyed 1,013 senior finance leaders across 20 countries and 13 sectors. The findings show that active AI use across finance has more than doubled since 2024, rising from 30 percent to 75 percent. At the same time, 71 percent of organizations report that AI is meeting or exceeding their return on investment expectations.

The findings provide important Technology insights for CFOs who are evaluating where AI can create measurable business value. More importantly, they show that successful adoption is becoming less about experimentation and more about disciplined execution.

AI Is Becoming a Decision Engine for Finance

Traditionally, finance teams have used technology to automate repetitive processes and improve reporting efficiency. Now, AI is moving into areas where judgment and speed have a much greater influence on business performance.

According to KPMG, organizations report improvements in decision making quality, decision making speed and forecasting accuracy. The research found improvements of 70 percent in decision making quality, 71 percent in decision making speed and 64 percent in forecast accuracy.

This transformation is particularly significant for CFOs. Faster forecasting can help organizations respond to changing market conditions, while stronger analytical capabilities can give executives greater confidence when allocating capital, managing costs and identifying growth opportunities.

These developments also connect with broader Finance industry updates, where finance departments are increasingly expected to become strategic partners rather than simply reporting functions.

The Race to Scale Is Creating New Challenges

Although adoption is accelerating, KPMG’s findings suggest that simply deploying more AI does not guarantee better results. The organizations gaining the strongest advantages are building governance, controls and measurement into their AI strategies.

Data quality remains one of the biggest challenges. KPMG reports that 36 percent of organizations identify data quality as both a major barrier and an important opportunity. Without reliable data, even sophisticated AI systems can produce unreliable insights.

Consequently, finance leaders need to think beyond technology selection. Data architecture, internal controls, cybersecurity and accountability are becoming equally important components of AI investment.

This trend also reflects wider IT industry news, where organizations are discovering that AI scalability depends heavily on the infrastructure and governance supporting it.

Trust Is Becoming a Competitive Advantage

As AI becomes more deeply involved in financial processes, trust is moving to the center of the discussion. Finance leaders must be confident that AI generated information is accurate, explainable and secure before allowing it to influence important decisions.

KPMG’s US research found that 50 percent of finance leaders identify cyber and AI related security threats as a major concern, while 48 percent are concerned about the accuracy of AI generated financial outputs.

Therefore, independent assurance and strong AI controls are becoming strategic assets rather than simply compliance requirements. KPMG reports that assurance ready organizations demonstrate substantially stronger improvements in error reduction and greater confidence in scaling AI.

For CFOs, this means responsible AI should be treated as part of the investment strategy itself. When governance is designed into AI systems from the beginning, organizations can move faster without sacrificing financial integrity.

Finance Teams Need New AI Skills

Technology alone cannot deliver the transformation. People remain central to the success of AI adoption, particularly as finance professionals increasingly work alongside intelligent systems.

KPMG’s research highlights data fluency as an important capability. Finance professionals need to understand data quality, evaluate AI generated outputs and communicate insights in ways that support business decisions.

This shift creates a strong connection between Finance industry updates and HR trends and insights. Hiring strategies, employee development and workforce planning are becoming closely connected to AI transformation.

Rather than replacing financial expertise, AI is creating an opportunity to elevate it. Routine analysis can increasingly be handled by technology, allowing finance professionals to spend more time on interpretation, strategic planning and judgment.

The CFO Role Is Expanding

The acceleration of AI is also changing what businesses expect from CFOs. Financial leaders are increasingly involved in technology investment, data strategy, governance and enterprise transformation.

The most effective approach is not necessarily to adopt AI everywhere. Instead, organizations should identify areas where AI can improve decision quality, accelerate forecasting or strengthen operational performance.

This perspective is relevant across other business functions as well. Sales strategies and research increasingly depend on predictive analytics, while Marketing trends analysis is being shaped by automated insights and intelligent customer data platforms.

The finance function can therefore become a central driver of enterprise wide AI adoption.

What Finance Leaders Should Focus on Next

The KPMG research points toward a practical lesson for organizations entering the next stage of AI adoption. Scaling should begin with clearly defined business outcomes rather than technology for its own sake.

Finance leaders should establish measurable performance indicators before expanding AI across additional workflows. At the same time, they should strengthen data governance, cybersecurity and human oversight. Workforce development should happen alongside technology deployment rather than after it.

Equally important, organizations should create a culture where finance professionals can experiment with AI while understanding its limitations. Practical training, relevant use cases and access to controlled environments can make adoption more effective.

KPMG’s research shows that the organizations pulling ahead are not simply those using the most AI. They are the organizations connecting AI with trusted data, measurable outcomes, strong governance and capable people.

Actionable Insights for CFOs

The AI race in finance is entering a more mature phase. CFOs should evaluate AI investments according to the quality of decisions they improve, the measurable value they generate and the level of trust they can establish.

For finance teams, the immediate opportunity is to combine automation with human judgment. Build reliable data foundations, develop AI capable talent and establish governance before scaling complex systems. Meanwhile, leaders should continuously measure whether AI is improving forecasting, decision making and financial performance.

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