Boosting Financial Returns with AI-Powered Accounts Payable Automation

Driving ROI with Agentic AI in Finance

Finance leaders are leveraging agentic AI to enhance ROI through accounts payable automation, transforming manual tasks into autonomous workflows. While general AI projects showed a 67 percent return on investment last year, autonomous agents delivered an average ROI of 80 percent by managing complex processes without human intervention. This performance disparity necessitates a shift in how CIOs allocate automation budgets.

Agentic AI systems are propelling enterprises from theoretical concepts to tangible returns. Unlike generative tools that summarize data or draft text, these agents execute workflows based on strict rules and approval thresholds.

Boardroom pressure is a driving force behind this shift. A report by Basware and FT Longitude reveals that nearly half of CFOs are under pressure from leadership to implement AI throughout their operations. However, 61 percent of finance leaders acknowledge that their organizations introduced custom-developed AI agents mainly as experiments to test capabilities rather than to solve business challenges.

These experiments often fall short. Traditional AI models generate insights or predictions that require human interpretation. Agentic systems bridge the gap between insight and action by embedding decisions directly into the workflow.

Jason Kurtz, CEO of Basware, emphasizes that there is a growing impatience for unstructured experimentation. He states, “We’ve reached a tipping point where boards and CEOs are done with AI experiments and expecting real results. AI for AI’s sake is a waste.”

Accounts Payable as the Key Area for Agentic AI in Finance

Finance departments are directing these agents towards high-volume, rules-based environments, with accounts payable (AP) being the primary use case. 72 percent of finance leaders identify AP as the ideal starting point for agentic deployment. This process is suitable for agentic AI as it involves structured data: invoices are received, undergo cleaning and compliance checks, and culminate in payment bookings.

Teams utilize agents to automate invoice capture and data entry, a task performed daily by 20 percent of leaders. Other applications include detecting duplicate invoices, flagging fraud, and minimizing overpayments. These are practical applications where an algorithm can operate with high autonomy within correct parameters.

Success in this sector hinges on data quality. Basware trains its systems on a dataset of over two billion processed invoices to provide context-aware predictions. This structured data enables the system to differentiate between legitimate anomalies and errors without human oversight.

Kevin Kamau, Director of Product Management for Data and AI at Basware, describes AP as a “proving ground” due to its combination of scale, control, and accountability, which few other finance processes offer.

The Decision Matrix: Build vs. Buy

Technology leaders must determine how to acquire these capabilities. The term “agent” encompasses everything from basic workflow scripts to sophisticated autonomous systems, complicating procurement.

Approaches differ by function. In accounts payable, 32 percent of finance leaders prefer agentic AI integrated into existing software, while 20 percent opt for in-house development. For financial planning and analysis (FP&A), 35 percent favor self-built solutions compared to 29 percent for embedded systems.

This divergence suggests a pragmatic rule for the C-suite. If the AI enhances a process shared across multiple organizations, like AP, embedding it through a vendor solution is logical. If the AI provides a competitive edge unique to the business, in-house development is the preferred route. Leaders should buy to streamline standard processes and build to differentiate.

Governance as a Driver of Efficiency

Concerns about autonomous errors hinder adoption. Nearly half of finance leaders (46%) are reluctant to deploy an agent without clear governance. This caution is justified, as autonomous systems necessitate stringent guardrails to function safely in regulated environments.

However, the most successful organizations do not let governance impede deployment; instead, they leverage it to scale. These leaders are significantly more likely to use agents for intricate tasks like compliance checks (50%) compared to their less confident counterparts (6%).

Anssi Ruokonen, Head of Data and AI at Basware, recommends treating AI agents like junior colleagues. Trust is essential, but significant decisions should not be delegated immediately. Thorough testing and gradual autonomy introduction, with human oversight for accountability, are crucial.

Concerns about job displacement due to digital workers are prevalent. One-third of finance leaders believe job displacement is underway. Proponents argue that agents reshape work dynamics rather than eliminate jobs.

Automating manual tasks such as extracting information from PDFs allows staff to focus on higher-value activities. The goal is to transition from task efficiency to operational leverage, enabling finance teams to expedite closures and make informed liquidity decisions without expanding headcount.

Organizations extensively utilizing agentic AI report superior returns. Leaders who deploy agentic AI tools daily, such as for accounts payable tasks, achieve better outcomes than those who limit usage to experimental purposes. Confidence grows through controlled exposure; successful small-scale implementations lead to broader operational trust and increased ROI.

Executives must move beyond aimless experimentation to replicate the accomplishments of early adopters. Data indicates that 71 percent of finance teams with weak returns acted without clear guidance under pressure, in contrast to only 13 percent of teams that achieved substantial ROI.

Success hinges on embedding AI directly into workflows and managing agents with the same discipline applied to human employees. Kurtz concludes, “Agentic AI can deliver transformative results, but only when deployed purposefully and systematically.”

See also: AI deployment in financial services reaches a turning point as Singapore leads the shift to production

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