Ask most people outside of a finance department how much they think finance teams use artificial intelligence, and they’ll likely underestimate it. That’s one of the findings revealed inside the 2026 AI in Finance Report, where about a third of respondents say their team uses AI more than outsiders realize.
It makes sense when you consider how AI actually shows up in finance work today. It’s not always visible. It runs in the background of invoice processing systems, surfaces anomalies in payment data, and powers the analytics tools that produce the reports finance teams present to leadership every week. It doesn’t always announce itself. But it’s there, and it’s more widespread than the industry conversation typically acknowledges.
Two-thirds of finance teams say they’re currently using or piloting AI. Confidence is rising, and the appetite for more is real. The report commissioned by Yooz examines results from a January 2026 survey of 500 finance professionals. The findings make it clear that finance teams have already gotten their foot in the door with AI, and they’re ready to take the next step.
Where Finance Teams Are Using AI Right Now
The most common home for AI in finance today is reporting and analytics, cited by 43% of respondents. It’s not hard to see why. Reporting is a high-volume, high-frequency activity where AI can generate outputs quickly and where results are relatively easy to validate. Finance professionals who use AI to build dashboards, generate narratives, or surface trends in spend data get immediate, tangible feedback on whether the tool is working. That makes it a natural entry point and a confidence builder for teams that are still finding their footing with the technology.
Forecasting and financial planning come next, with 27% of teams reporting AI use in that area. This is a more demanding application. Forecasting requires AI to work with more complex, interconnected data sets, and the stakes of getting it wrong are higher. The fact that more than a quarter of finance teams are already using AI here signals that adoption is maturing faster than many expect.
Accounts payable and receivable, along with expense management, each come in at 18%. Vendor and invoice management sits at 14%. These figures are lower than reporting, but they represent areas where AI has already demonstrated clear, measurable value in organizations that have deployed it well.
Using AI to automate accounts payable, for example, reduces manual processing time and improves accuracy. AI can automate the entire invoice lifecycle, from capture and data extraction to approvals and fraud detection. It allows teams to catch errors before they clear. The result is faster processing, lower costs, and stronger vendor relationships, while finance teams gain more time to focus on strategic priorities.
Confidence Is Growing
One of the most important findings in the report isn’t about what finance teams are doing with AI, but how they feel about it. More than half of respondents (53%) say they’re more confident using AI than they were a year ago. Only 7% say they feel less confident. The dominant mindset across the field is “curious but cautious,” cited by 42%, with another 26% describing themselves as excited and confident.
That combination of growing confidence and disciplined optimism is exactly the right orientation for a function that manages financial controls and fiduciary responsibility. Finance teams are building familiarity carefully and systematically, which is precisely what creates the conditions for durable, trustworthy integration.
Confidence also has a compounding effect. As finance professionals become more fluent with AI in lower-stakes applications like reporting, they build the literacy and the trust in outputs that make it easier to extend AI into more complex and consequential workflows. The groundwork being laid today in analytics and forecasting is what will enable the next phase of adoption.
The High-Potential Areas Where AI Is Still Underdeployed
The most striking finding in the data is in the areas where AI arguably has the most to offer. Only 19% of finance teams say they use AI for audit, risk, compliance, or fraud detection and prevention. At a time when payment fraud attempts are hitting the vast majority of organizations annually and growing in sophistication as fraudsters increasingly use AI themselves, that figure represents a significant and urgent opportunity.
Fraud detection is precisely the kind of application where AI can do things that humans simply can’t do at scale. AI can handle high-volume pattern-recognition tasks like screening every invoice for signs of document manipulation, flagging unusual vendor behavior, catching duplicate payment attempts, and identifying out-of-policy transactions before they’re approved with a consistency and speed no manual review process can match.
The same logic applies to compliance and audit preparation. AI that’s integrated into financial workflows can maintain a continuous, real-time audit trail. It can highlight exceptions, document approvals, and flag anomalies as they happen rather than discovering them weeks later during a review cycle. For finance teams under pressure to do more with less, that kind of embedded oversight is a force multiplier.
Finance Leaders Need To Play a Stronger Role in AI Adoption
Understanding where AI is being used is only half the picture. Understanding who’s driving those decisions matters just as much.
Twenty-four percent of respondents say IT or technology teams are the primary driver of AI adoption in finance. Only 13% point to the chief financial officer or vice president of finance. Another 22% say no one in particular is leading the effort.
This explains why so many organizations are stuck in the middle. AI is present but not fully operational, and active in some areas but not embedded across the workflows that matter most. When finance leaders aren’t visibly driving the AI agenda, teams end up with access to capabilities that aren’t integrated into their actual workflows and aren’t supported by the training and governance infrastructure that would make them usable at scale.
The opportunity in front of finance leaders right now is enormous. The curiosity is there. The confidence is building. What’s missing is direction and ownership.
Finance leaders who step into that void will find that the conditions for rapid progress already exist inside their organizations. As finance leaders take ownership of building the process foundations that support AI, it moves from experimentation to embedded capability. And with that shift comes something more valuable than efficiency: a finance function with the speed, visibility, and control to be a true strategic force inside the business.
This story was produced by Yooz and reviewed and distributed by Stacker.
Copyright 2026 Stacker Media, LLC
This story was originally published April 21, 2026 at 4:00 AM.