AI in Finance Executive Report

AI in finance is accelerating faster than anyone expected, transforming how CFOs, FP&A teams, and accountants analyze data, automate workflows, and support decision-making. According to new data from the 2025 Datarails AI in Finance Executive Report, AI is no longer an experimental tool on the sidelines, it’s becoming a core operational layer for analysis, automation, reporting, compliance, and decision support.

As organizations adopt AI technology across analysis, reporting, forecasting, compliance, and automation, the study highlights how AI is reshaping financial operations, improving productivity, and redefining the future of finance. 

This report highlights the most important findings from the study — including adoption trends, leading AI use cases, organizational shifts, technology preferences, and the expected future impact of AI on productivity and headcount.

AI Adoption in Finance Is Rising at Record Speed

The Datarails study shows a dramatic rise in AI usage across finance teams. The trend isn’t gradual but exponential.

Most Finance Leaders Are Already Using AI

A striking 84% of finance leaders personally use generative AI, with 77% of them doing so within a company-approved environment. Only 1% say their company prohibits AI use. This signals two things:

• AI comfort levels are high among finance executives.

• Organizations are formalizing AI programs faster than expected.

AI Adoption Began Mostly in the Last 12 Months

The report shows a compressed adoption timeline:

• 19% of finance teams started using AI in the last 6 months.

• 44% adopted AI 7–12 months ago.

• Only 11% have been using AI for more than 24 months.

This means the finance profession is still early in its AI maturity curve—yet adoption is widespread.

AI in Finance Is Driven From the Top (CEO > CFO)

One of the clearest findings from the 2025 report is that AI adoption inside finance organizations is overwhelmingly being led from the top of the company, not from individual contributors or grassroots experimentation.

According to the study, 44% of companies say the CEO is the primary driver of AI initiatives within finance, making the chief executive the most influential champion of AI transformation. This aligns with broader industry research showing CEOs increasingly viewing AI as a strategic enterprise priority rather than a departmental experiment.

By comparison, 28% of respondents said the CFO is leading the push, while another 26% credited finance department heads, such as leaders within FP&A or Accounting, with driving implementation.

What Finance Teams Are Actually Doing with AI

The study breaks down AI applications across FP&A, Accounting, FinOps, Treasury, Audit, Tax, and Investor Relations—revealing consistent themes.

Data Analysis Is the Leading AI Use Case Across Finance

Across FP&A, Accounting, Treasury, and Audit, AI-driven analysis comes out on top:

• 88% of FP&A teams use AI for data analysis.

• 71% of Accounting teams do the same.

This aligns with finance’s long-standing pain point: too much data, not enough time.

Why Analysis Leads the Way

AI eliminates hours of manual work by:

• Identifying trends and correlations

• Detecting anomalies

• Summarizing large data sets

• Creating insights that humans often miss

It’s the most accessible starting point—and the most universally valuable.

AI is transforming analysis, reporting and planning since FP&A teams are using AI for:

• Analysis – 88%

• Process automation – 70%

• Reporting & narratives – 66%

• Planning & modeling – 63%

• Forecasting – 53%

This shift signals that FP&A is moving from spreadsheet-driven processes to AI-assisted decision-making.

AI Is Modernizing High-Volume Processes in Accounting

Accounting teams are applying AI to the most repetitive tasks:

• AR automation – 66%

• AP automation – 63%

• Process automation – 66%

• GL processes – 33%

Because ERP systems were early adopters of AI features, Accounting is now one of the most AI-enabled finance functions.

AI Strengthens Planning and Process Efficiency in FinOps

Top use cases include:

• Financial planning – 80%

• Process automation – 55%

• Compliance & risk – 49%

FinOps is using AI to reduce inefficiencies and optimize spend management.

AI Becomes a Risk and Compliance Force Multiplier in Treasury & Audit

AI is becoming indispensable in functions where accuracy, compliance, and fraud detection are mission-critical.

Treasury:

• Risk management – 80%

• Cash management – 78%

• Investment management – 67%

Audit:

• Risk & anomaly detection – 70%

• Continuous auditing – 67%

Benefits of AI in Finance

AI Strongly Improves Individual Productivity

According to the study, nearly the entire finance profession is experiencing meaningful productivity gains from AI. An overwhelming 95% of finance professionals reported that AI has improved their personal productivity, and a full 60% described that impact as “strong,” not just incremental.

This reflects how quickly AI has become a practical, everyday tool for individuals across FP&A, accounting, and financial operations. Instead of spending hours cleaning data, building formulas, or drafting reports, professionals can now complete those tasks in minutes with AI’s assistance. The result is more time for higher-value work such as analysis, strategic thinking, and oversight rather than getting buried in manual, repetitive processes.

Finance Teams Are Seeing Even Higher Department-Level Gains

The benefits scale beyond individuals:

• 68% of finance departments report a strong productivity impact.

• Only 3% report no improvement.

This is a decisive signal: AI is transforming workflows, not just individual productivity.

Top Benefits of AI Adoption

Participants ranked the following as the biggest benefits:

• Better insights & decision support – 35%

• Increased productivity – 27%

• Cost reduction – 18%

Insight quality, not automation, is the top perceived value of AI.

What Tools Finance Teams Prefer

The study shows a clear hierarchy in the AI technologies finance teams rely on today, with OpenAI emerging as the dominant model across the profession. An impressive 89% of respondents reported using OpenAI tools in their workflows, underscoring how deeply embedded the technology has become in daily financial operations.

However, Google Gemini is quickly gaining traction, with 70% of organizations adopting it as well. Much of Gemini’s momentum is tied to its seamless integration with Google Workspace, making it an easy, natural extension of tools employees already use for communication, documentation, and collaboration. Together, these two platforms now form the backbone of AI activity inside most finance organizations.

Beyond which models they use, the study also sheds light on the type of AI solutions companies prefer to deploy. A significant 74% of finance teams say they favor building their own internal AI tools on top of third-party large language models rather than relying solely on off-the-shelf products. 

While 54% still value AI-native finance tools, fewer organizations — only 31% — prefer to depend on AI features embedded within their existing vendor platforms. Taken together, these preferences reveal an industry moving toward hybrid models that balance flexibility with specialized capabilities.

What makes this trend even more notable is that it’s not just aspirational—many companies are already deep into execution. Among organizations that prefer building internal tools, 66% already have at least one AI solution in production, meaning internal development is not merely a future strategy but an active reality.

Why Some Finance Teams Still Haven’t Adopted AI

Even with overwhelming interest, some barriers remain. Here are the top reasons companies haven’t adopted AI:

• 76% say they need more time to understand how to use AI.

• 50% lack expertise.

• 36% worry about output quality.

• 34% fear data privacy risks.

Larger companies are especially concerned about data privacy (100%).

The Future Impact of AI in Finance

AI Could Reduce Finance Costs by 11%–50%

The study highlights substantial ROI potential:

• A company investing $10M in finance could save $1.1M–$5M per year through productivity gains.

This reinforces why CFOs are increasingly linking AI to financial outcomes.

Industries Expect Strong Productivity Gains

Predicted productivity increase:

• Manufacturing, retail, and healthcare expect 11%–25% gains

• Financial services expect >25% gains (39%)

AI maturity is advancing faster in highly regulated sectors than expected.

However, AI won’t reduce headcount as much as people think. Even though 92% expect productivity gains, only 43% expect headcount reductions >11%.

This implies AI will:

• Enable finance teams to do more with the same staff.

• Support company growth without linear headcount expansion.

From Experimentation to Real Transformation

The 2025 Datarails AI in Finance Executive Report makes one message clear: AI is no longer a futuristic add-on, but it’s becoming the backbone of modern finance.

Finance teams are using AI for analysis, automation, continuous monitoring, forecasting, and better decision support. Adoption is accelerating, productivity is rising, and financial leaders are preparing for a future where AI is integral to every workflow.

The companies that thrive from 2025–2030 will be those that:

• Build AI fluency inside finance

• Use AI tools responsibly with strong data governance

• Systematically measure productivity and ROI

• Integrate AI into strategic and operational processes

AI in finance transforms how finance teams think, work, and deliver value.