Finance leaders plan to increase spending on artificial intelligence in accounts payable, but most are holding back from scaling the technology until they see clear returns, according to commissioned research from Basware. The study found that only 39% of respondents say they are ready to scale AI, even as investment expectations rise.
The survey of 231 finance and accounts payable technology decision-makers in the UK, US, France and Germany shows a gap between adoption of AI for Finance and operational discipline. While 67% said their teams already use AI in targeted accounts payable tasks, 68% said they need demonstrable return on investment before approving further finance technology spending. The findings add weight to the broader debate over whether AI tools deliver measurable value inside core business systems rather than through stand-alone assistants.
Investment plans
Three-quarters of respondents expect AI investment to rise over the next 12 to 24 months. But timelines for payback remain restrained. Only 7% expect AI investments in accounts payable to pay back in less than six months. Another 20% expect returns within six to 12 months, while 35% see payback taking 13 to 24 months. Finance departments are treating AI as a longer-term operational project, not a quick cost-saving lever.
Spending is shifting toward areas where outcomes can be measured against existing controls and workflow data. In accounts payable, that includes exception handling, approval recommendations and tasks where software actions can be compared with established processes. The study also found that 64% of respondents prioritise stability and compliance over raw innovation when selecting AI tools, while only 46% said they had achieved an effective balance between governance and innovation.
Control concerns
The data suggests many finance teams lack the structures to manage AI at scale. Although 67% reported some use of AI in accounts payable, only 39% operate AI centres of excellence at scale. That shortfall matters because accounts payable sits close to the controls that govern cash movement, supplier payments and fraud prevention. Any AI system that recommends or completes actions in that environment faces greater scrutiny from finance leaders than systems used for lower-risk administrative work.
Regulatory pressure is also shaping decisions. Some 65% of respondents said major or urgent improvement is needed to adapt to new financial regulations, and 63% cited rising demand for data-backed decision-making.
Donna Wilczek, Chief Product and Technology Officer at Basware, said finance offers one of the clearest tests of whether AI produces measurable business value. "Finance is a strong place to start with AI because the value can be measured," she said. "The challenge is getting from ambition to execution in a way the business can trust. Once outcomes are proven, the remit can grow."
Audit trail
For finance leaders, the issue extends beyond whether a model can automate a task. They also need records showing what the system did, why it acted and when human intervention was required. Wilczek said those requirements are becoming mainstream governance expectations. "Governed AI is no longer aspirational, it's a board-level requirement," she said. "Every AI decision in accounts payable needs to be logged, traceable, and auditable from the moment it's made, not reconstructed after the fact."
Basware is using the research to frame its approach to governed autonomy, under which finance teams define how much authority AI tools have over a process. Human review remains part of workflows where judgment is needed, and broader autonomy follows only as outcomes are verified. The framework sets out three levels of AI authority, moving from advisory to collaborative to more autonomous operation, but always bounded by internal rules and audit requirements.
The results come amid wider concern about how autonomous systems behave in live environments. Recent scrutiny of AI agents acting with limited oversight has sharpened questions for large companies about access controls, monitoring and accountability when software is allowed to take action rather than simply generate suggestions.
Why this matters for finance professionals
The shift toward governed AI means finance teams must prioritise auditability and compliance over speed of adoption. Success will depend on building control structures that allow AI to act within clear boundaries, with human review retained for judgment calls. Professionals should demand that vendors provide traceable decision logs and focus on measurable ROI, not just feature promises. The survey indicates that departments are not rejecting AI, but placing stricter conditions on its use. As Wilczek put it, "Success in the next phase of AP won't be achieved by the teams using the most AI, but by the teams that govern it best."
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