The News
A new Yooz survey of 500 finance professionals found that 67% of teams are already using or piloting AI, but only 10% have embedded it into core financial workflows. The findings point to a clear shift: finance teams are moving beyond experimentation and starting to apply AI directly to processes like reporting, forecasting, and fraud detection, though gaps in trust, training, and leadership alignment remain.
Analysis
AI in Finance Is No Longer Experimental, But It’s Not Fully Operational Either
The takeaway from Yooz’s survey is that AI is already in the building, but it hasn’t taken over the job yet. Two-thirds of finance teams are using or testing AI, which lines up with what we’re seeing across the broader application development landscape. In Efficiently Connected’s AppDev research, 74.3% of organizations say AI/ML is their top spending priority, so it’s not surprising finance teams are moving quickly.
But the interesting part is the gap. Only 10% have AI embedded into core workflows. That’s the real story. As Principal Analyst Paul Nashawaty has noted, organizations are moving from AI curiosity to AI accountability. The question is no longer ‘can we use AI?’ but ‘can we operationalize it at scale?’
Finance is right in the middle of that shift. Teams are testing AI, getting comfortable with it, but still figuring out how to make it part of everyday operations without breaking controls or introducing risk.
Confidence Is Rising, but Trust Still Needs Work
The survey shows that 53% of finance professionals feel more confident using AI than they did a year ago, which is a strong signal that adoption is sticking.
But confidence doesn’t automatically equal trust. One in four respondents still say trust in AI outputs is a top barrier, and another 26% point to lack of training.
That combination matters. Finance isn’t like marketing or product teams where you can experiment more freely. These teams are responsible for compliance, financial accuracy, auditability, and fraud prevention. If AI outputs aren’t explainable or predictable, they simply won’t get used at scale.
This ties back to a broader trend in AppDev: governance is becoming just as important as capability. It’s not enough for AI to work; it has to be trusted, traceable, and aligned with business rules.
The Real Opportunity Is in Workflow Integration
Right now, most finance teams are using AI in reporting (43%) and forecasting (27%). That makes sense as these are lower-risk, high-value entry points. But the bigger opportunity is what’s not happening yet. Only 19% are using AI for fraud detection, risk, and compliance, which are arguably the highest-impact areas.
Why the gap? Because those use cases require deeper integration into core systems and workflows. Teams that are seeing value are using AI to remove friction from existing processes instead of adding new layers of complexity.
The shift ahead is from AI as a feature to AI as part of the process.
Efficiency Gains Are Real But Not Consistent Yet
There are early wins. About 32% of teams report time savings on manual tasks, which is often the first measurable benefit of AI in finance. But at the same time, 33% say they haven’t seen clear benefits yet. That’s a big split. This tells us something important: AI value in finance is still uneven.
Some teams are seeing real efficiency gains, while others are stuck in pilot mode without clear ROI. This is a common pattern across the AppDev landscape where early adopters see gains, but scaling those gains across the organization is harder.
From a development and platform perspective, this points to a need for:
- Standardized workflows
- Better integration with existing systems (ERP, P2P, etc.)
- Clear metrics for success
Without that, AI stays fragmented.
Leadership and Ownership Are the Missing Pieces
One of the more overlooked findings is around ownership. Only 13% say finance leadership (CFO/VP) is driving AI adoption, while 24% point to IT, and 22% say no one is driving it at all. AI in finance can’t just be an IT initiative. It has to be owned by the business, especially when it impacts core financial processes.
This aligns with what we’re seeing across AppDev and platform engineering: IT can enable AI, but business teams have to operationalize it. Until finance leadership takes a more active role, adoption will likely stay fragmented.
Why This Matters for Developers
This isn’t just a finance story; it’s an application development story too. Finance systems are becoming AI-driven applications, which means developers and platform teams are now responsible for:
- Embedding AI into workflows (not just adding features)
- Ensuring explainability and auditability
- Integrating AI with systems like ERP, payments, and reporting tools
It also reinforces a bigger shift: enterprise applications are moving from transactional systems to decision systems. Finance isn’t just recording what happened anymore; it’s using AI to decide what should happen next.
Looking Ahead
The next phase of AI in finance is pretty clear: from pilots to production workflows.
Adoption is already strong, and confidence is growing. The focus now shifts to:
- Embedding AI into core processes
- Building trust through governance and transparency
- Scaling results across teams
We’ll likely see more emphasis on end-to-end workflow automation, where AI handles repetitive tasks, flags risks, and supports decision-making in real time.
The organizations that figure this out won’t just move faster; they’ll operate differently. Less manual work, fewer exceptions, and more consistent outcomes. Or, more simply: AI in finance is no longer about experimenting. It’s about making the work actually better.
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