AI in Finance: What’s Hype and What’s Actually Working

By Rob Israch, President, Tipalti

Every finance leader has sat through a version of the same pitch by now: AI will transform your function, automate away the drudgery, and free your team for higher-value work. Some of that is true. A lot is hype. The more useful question isn’t whether AI belongs in finance; that debate is over. It’s which parts are actually earning their keep, and which are still just a pitch.

Finance Is Optimistic, but Not Naive

Finance teams aren’t the AI skeptics some assume them to be. Tipalti’s global research on AI in finance found that 55% of finance professionals describe themselves as extremely optimistic about AI’s potential. That’s a clear majority already sold on the upside.

What’s more telling is what’s driving that optimism, and what isn’t. Time savings (64%) and quality or accuracy improvements (62%) rank as the top reasons finance teams adopt AI, ahead of enhanced decision-making at 58%. Cost reduction, often the headline in vendor marketing, doesn’t top the list. Finance leaders aren’t adopting AI because it’s cheap. They’re adopting it because it does the job better and faster. That’s a meaningful difference, and a useful filter for separating real value from hype.

Where the Hype Breaks Down

What separates AI’s promise from practical reality is trust, and trust turns out to be a much more specific, structural problem than “people are cautious about new tech.”

The same research found the top barriers to AI adoption in finance are integration with legacy systems (61%), inconsistent or poor data quality (57%), and a lack of in-house AI expertise (41%). Notice what’s absent from that list: fear of job loss, resistance to change, and general distrust of automation. The obstacles finance leaders report are operational and structural, not cultural. The fix isn’t a change management exercise. It’s an infrastructure one.

One line from the research captures the underlying issue well: if you can’t trace how an answer was made, you can’t trust it. That’s not a philosophical objection to AI; it’s a specific, addressable requirement. Finance runs on auditability. A tool that can’t show its work doesn’t meet the bar, no matter how impressive its output looks in a demo.

What’s Moving the Needle Right Now

Strip away the hype and look at where finance teams report real, current AI deployment, and a consistent pattern emerges. Financial analysis and benchmarking lead at 63.5% adoption, followed closely by report generation and insights at 62%, and cash flow forecasting at 58%.

These have one thing in common: they’re all tasks where AI accelerates a well-defined analytical process a human would otherwise do manually, not tasks where AI is making autonomous judgment calls with real consequences. That’s not a limitation to apologise for. It’s exactly why these use cases are working. They deliver measurable time savings and quality improvements without asking finance teams to hand over decisions they’re not ready to delegate.

By contrast, strategic planning and predictive finance, the more ambitious, judgment-heavy applications often featured in AI marketing, remain largely untapped. That’s not because the technology can’t theoretically do it. It’s because trust hasn’t caught up to capability in those areas yet, and finance teams are, rightly, not forcing the issue.

Trust Is Built in Layers, Not Handed Over Wholesale

The clearest signal in the data is what finance teams say they need before extending AI further. Visibility into AI’s actions and customisable automation configurations both rank as “extremely important” at 55%, and advisory recommendations paired with human oversight follow closely at 54%. Fully autonomous AI operation, asking the system to just handle it, ranks lowest of all these priorities at 46%.

That ranking is telling. Finance teams don’t want AI to remove all of the work; they want AI to take on the highly manual aspects of the job while they retain the ability to see what it’s doing, adjust it, and step in when it matters. That’s a fundamentally different ask than the “set it and forget it” framing that shows up in a lot of AI marketing, and it’s a smarter one.

What This Means for Finance Leaders

The practical takeaway isn’t to wait for the fully autonomous AI that the hype promises before adopting it. It’s to be precise about where you deploy it today. Apply it to manual, high-volume, analytically clear tasks where the value is measurable and the oversight is straightforward: invoice coding, matching, reconciliation, benchmarking, and report generation. Hold off on treating it as a replacement for judgment calls that carry real financial or strategic consequence, at least until the visibility and governance infrastructure exists to support that level of delegation.

This is the approach that advanced accounts payable software is built around: automating the well-defined, repeatable parts of the AP process end-to-end, while maintaining the controls, audit trails, and human checkpoints that let finance teams trust what’s happening. That’s a much less exciting pitch than “AI will run your finance function.” It’s also the version that’s actually working.