AI in Finance Has Moved Beyond the Experiment Stage. Now What?

For years, artificial intelligence felt like something finance teams were watching from the sidelines.

Today, that's no longer the case.

According to the Yooz 2026 AI in Finance Report, AI adoption has reached a tipping point inside finance organizations. Nearly two-thirds (67%) of finance teams report they are either using or piloting AI technologies, signaling that AI is no longer a future initiative—it's becoming part of day-to-day operations.

But while adoption is growing rapidly, the research reveals something even more interesting: Most organizations are still in the early stages of turning AI into measurable business value.

AI Is Everywhere But Not Yet Embedded

The report found that while 67% of finance teams are using or piloting AI, only 10% say AI is fully embedded into their core financial processes.

That gap highlights one of the biggest opportunities facing finance leaders today.

Many organizations have experimented with AI through reporting tools, analytics platforms, forecasting applications, or workflow automation. However, relatively few have integrated AI deeply enough into their processes to create repeatable, scalable outcomes.

While AI is being tested, the next challenge is operationalizing it.

Organizations that successfully embed AI into standardized workflows can reduce manual effort, improve consistency, strengthen controls, and increase visibility across financial operations.

Confidence Is Growing

One of the more notable findings from the report is that finance professionals are becoming increasingly comfortable using AI in their work.

More than half (53%) of respondents say they are more confident using AI today than they were a year ago, while 40% report their confidence has remained unchanged.

That familiarity is reflected in how finance professionals describe their approach to AI. The report found that 42% consider themselves "curious but cautious," while 26% say they are excited and confident.

The findings suggest that finance teams are becoming more comfortable using AI for tasks such as reporting, planning, and analysis. However, with only 10% of organizations reporting that AI is fully embedded in core financial processes, many are still determining how to apply AI more broadly across finance operations. Closing that gap will require clear use cases and leadership guidance on where AI can deliver the greatest impact.

Reporting Is Leading the Way

When finance teams first begin using AI, reporting and analytics often become the natural starting point.

According to the report, 43% of organizations currently use AI for reporting or analytics, making it the most common use case today. Forecasting and financial planning follow at 27%.

These areas provide a relatively low-risk entry point because outputs can be reviewed, validated, and refined before decisions are made.

As confidence grows, however, organizations are beginning to explore how AI can support more complex and transaction-heavy processes.

The Next Big Opportunity: Fraud Prevention and Controls

One of the most surprising findings in the report is where AI adoption remains relatively low.

Only 19% of finance teams currently use AI for audit, risk management, compliance, fraud detection, or fraud prevention.

This represents a significant opportunity.

While many discussions around AI focus on productivity gains, some of the greatest long-term value may come from strengthening financial controls.

AI-powered systems can identify anomalies, detect unusual vendor activity, flag duplicate invoices, and surface potential risks that might otherwise go unnoticed. These capabilities help finance teams improve oversight while simultaneously reducing manual review work.

As organizations continue scaling automation efforts, AI-driven controls will likely become an increasingly important part of the finance technology stack.

What's Slowing Adoption?

Despite the momentum, barriers remain. The biggest obstacles are not budget limitations or regulatory concerns.

Instead, the report found that:

• 26% cite lack of training and education as the primary barrier.
• 25% cite lack of trust in AI outputs.
• Only 12% point to regulatory or compliance concerns.
• Just 10% identify budget constraints as the biggest challenge.

These findings suggest that successful AI adoption is less about purchasing technology and more about helping teams understand how to use it effectively.

Organizations that invest in education, governance, workflow integration, and clear review processes will likely see stronger adoption and more consistent outcomes.

Moving Toward Lean Financial Operations

Perhaps the most important takeaway from the report is that AI should not be viewed as a standalone tool.

The greatest value emerges when AI is embedded directly into the workflows finance teams already use every day.

Whether it's invoice processing, approvals, reporting, forecasting, or fraud detection, AI works best when it helps eliminate repetitive tasks, reduce exceptions, and strengthen controls.

The Road Ahead

The message from finance leaders is clear: AI is no longer an emerging technology. It is becoming an operational necessity.

The organizations that gain the most value won't necessarily be those experimenting with the most AI tools. They'll be the ones that successfully integrate AI into repeatable processes, establish trust and governance, and empower finance teams to focus on higher-value work.

The next chapter of AI in finance isn't about the technology itself. It's about the business outcomes it enables. As organizations move beyond experimentation, success will depend on applying AI in core areas where it can improve productivity, strengthen controls, and support better decisions.

Download the Yooz 2026 AI in Finance Report

How are finance teams actually using AI today?

Get insights from 500 finance professionals on adoption trends, reporting and analytics, fraud prevention, finance automation, confidence levels, and the challenges organizations face embedding AI into core processes.

Download the report and explore the data behind the findings.