Basware research has found that finance leaders are preparing to increase investment in artificial intelligence, but many have not yet built the operating model needed to govern AI at scale.
The Forrester Consulting Opportunity Snapshot, commissioned by Basware, surveyed 231 enterprise finance leaders and finance professionals responsible for finance and accounts payable across the UK, US, France, and Germany. It found that 76% plan to increase AI investment over the next 12 to 24 months, while 68% require demonstrable return on investment before committing more funds.
AI is already being used in accounts payable. The study found that 67% of finance teams are running AI in AP, but only 39% have built centres of excellence at scale. Less than half, 46%, said they had struck an effective balance between governance and innovation.
The findings show that the finance AI debate is becoming more disciplined. Early automation use cases focused on efficiency, data extraction, matching, exception handling, and workflow support. The next phase involves AI acting within live finance processes, where errors, weak controls, or unclear accountability can create audit, compliance, supplier, and cash flow consequences.
“Finance is a strong place to start with AI because the value can be measured,” said Donna Wilczek, chief product and technology officer at Basware. “The challenge is getting from ambition to execution in a way the business can trust. Once outcomes are proven, the remit can grow.”
The study found that finance leaders are also taking a more realistic view of payback. Only 7% expect AP AI investments to pay back in under six months, while 20% expect payback within six to 12 months and 35% expect value to take 13 to 24 months to materialise.
That reflects the complexity of accounts payable. AP is often seen as a back-office process, but it sits at the centre of supplier relationships, cash management, fraud prevention, procurement control, compliance, and audit trails. Automation in this environment has to handle exceptions, approvals, payment timing, data quality, and regulatory requirements.
Financial employers are already preparing staff for wider AI adoption through AI retraining commitments, while governed agents are entering core operations. Basware’s research brings the same question into finance execution: adoption is advancing, but trust depends on control, auditability, and clear authority limits.
Wilczek said: “Governed AI is no longer aspirational, it’s a board-level requirement. Every AI decision in accounts payable needs to be logged, traceable, and auditable from the moment it’s made, not reconstructed after the fact.”
That requirement becomes more important as finance systems give AI greater authority. An AI tool that suggests an action carries less risk than one that resolves an exception, recommends approval, or triggers a workflow. Finance teams need to define when AI advises, when it collaborates, and when it is allowed to operate within set guardrails.
Basware’s Governed Autonomy framework sets three levels of AI authority, from Advisor to Collaborator to Operator. The structure is intended to let finance teams expand AI authority only as outcomes are proven, while keeping human review inside processes where judgement remains necessary.
Regulation is another driver. The study found that 65% of respondents said major or urgent improvement is needed to adjust to new financial regulations. Finance automation that cannot produce traceable decisions may create as many problems as it solves, particularly in organisations subject to audit, payment rules, sanctions controls, tax requirements, and internal policy obligations.
The findings also reveal a procurement shift. Finance leaders are not chasing AI features in isolation. Sixty-four per cent prioritise stability and compliance over raw innovation when choosing AI. Vendors are likely to be judged increasingly on governance architecture, explainability, logging, and integration with existing controls.
“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,” Wilczek added.
The research points to a more mature phase of finance technology investment. AI budgets may rise, but tolerance for vague productivity claims is falling. Finance leaders are looking for measurable value, controlled execution, and audit evidence strong enough to survive scrutiny after the invoice has been paid.
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