Maximizing Financial ROI: How Agentic AI Transforms Accounts Payable Automation

Finance leaders are increasingly leveraging agentic AI to transform their accounts payable processes, seamlessly transitioning from cumbersome manual tasks to sophisticated, autonomous workflows. In a world where efficiency is paramount, understanding how to harness such technologies can significantly elevate a finance team’s productivity and bottom line.

Recent findings reveal that while traditional AI projects achieved a 67% return on investment (ROI), autonomous agents soared to an average ROI of 80%, executing complex tasks without the need for human intervention. This advantage highlights a crucial need for Chief Information Officers (CIOs) to rethink their automation strategies and budget allocations.

The Shift Towards Autonomous AI

Agentic AI is not simply a theoretical concept; it’s a strategic tool generating real business results. Unlike generative AI that primarily summarizes information or creates content, agentic AI operates according to established rules and processes, executing workflows with precision.

The urgency for this shift is palpable. A joint report by Basware and FT Longitude uncovers that nearly half of CFOs are under pressure from leadership to integrate AI into their operations. Yet, a staggering 61% of finance leaders acknowledge that their organizations have primarily deployed custom AI agents as experimental initiatives rather than practical solutions.

This experimental approach often leads to unfulfilled potential. Traditional AI models provide insights that still require human interpretation. In contrast, agentic systems bridge the gap between insight and action, embedding decision-making directly into workflows.

Jason Kurtz, CEO of Basware, aptly notes that patience for trial-and-error approaches is waning. “We’ve reached a tipping point where boards and CEOs are done with AI experiments and expecting real results,” he states. “AI for AI’s sake is a waste.”

Accounts Payable: The Ideal Testing Ground for Agentic AI

Finance departments are directing their focus towards high-volume, structured processes, with accounts payable (AP) emerging as the primary application for agentic AI. An impressive 72% of finance leaders believe AP is the best area to start automation, thanks to its structured data requirements.

Here are some transformative applications of agentic AI in accounts payable:

Automating invoice capture and data entry, a repetitive task for 20% of finance managers
Identifying duplicate invoices
Detecting fraud
Mitigating overpayments

These tasks are not just theoretical; they represent areas where algorithms can operate with significant autonomy when guided properly.

Achieving success hinges on the quality of the data involved. Basware’s systems are trained on a dataset comprising over two billion processed invoices, which empowers them to deliver context-aware predictions. This ensures that the technology can distinguish between genuine anomalies and errors autonomously.

Kevin Kamau, Director of Product Management for Data and AI at Basware, characterizes AP as a "proving ground" for these technologies, noting its blend of scale, control, and accountability—attributes that few finance processes can match.

Navigating the Build versus Buy Dilemma

In the quest to procure the right technologies, finance leaders must carefully consider whether to build their own solutions or purchase from vendors. The term "agent" spans a wide range, from simple workflow scripts to intricate autonomous systems, making procurement decisions challenging.

Approaches often differ by function. For instance, in accounts payable:

32% prefer agentic AI integrated into existing software solutions
Only 20% opt for in-house development

In contrast, for financial planning and analysis (FP&A), 35% lean towards custom-built solutions versus 29% opting for embedded options.

This divergence offers a useful guideline for C-suite executives: when an AI enhancement serves a common process like AP, embedding a vendor solution is typically the way to go. Conversely, when the AI can deliver unique competitive advantages, building in-house proves more beneficial. Leaders should aim to buy for efficiency and build for differentiation.

Governance: Accelerating Adoption While Maintaining Control

The fear of autonomous errors stands as a significant barrier to wider adoption. Almost half of finance leaders (46%) hesitate to deploy agents without robust governance frameworks. This caution is justified, as autonomous systems require rigorous safeguards, particularly in regulated environments.

However, the most pioneering organizations have found ways to embrace governance as a facilitator for scaling. These forward-thinking leaders are twice as likely to utilize agents for complex operations, such as compliance checks.

Anssi Ruokonen, Head of Data and AI at Basware, suggests treating AI agents like junior team members. The system requires trust but should not be expected to make significant decisions out of the gate. His recommendation? Test thoroughly and introduce autonomy gradually, with a human always involved to oversee critical decisions.

Concerns about job displacement also linger in financial circles. Approximately one-third of finance leaders believe that job losses are already underway. Supporters of AI argue that these systems reshape the nature of work, allowing human professionals to engage in higher-value tasks rather than mundane data entry.

By eliminating repetitive tasks like extracting information from PDFs, teams can focus on strategic functions. The ultimate goal is to enhance operational leverage—enabling finance departments to close books faster and make informed liquidity decisions without expanding headcount.

Organizations that are extensively leveraging agentic AI report superior returns. Leaders utilizing these tools daily for tasks like accounts payable achieve more favorable outcomes compared to those who restrict their use to pilot testing. Gradual exposure builds confidence; successful small-scale deployments naturally extend into larger operational trust and enhanced ROI.

To truly succeed, executives must move beyond unstructured experimentation, aiming instead to replicate the achievements of early adopters. Data reveals that 71% of finance teams with weak returns acted impulsively without clear guidance, whereas only 13% of teams enjoy robust ROI.

In conclusion, achieving success in finance requires embedding AI directly into daily workflows and employing governance structures as rigorously as one would for human teams. “Agentic AI can deliver transformational results, but only when it is deployed with purpose and discipline,” Kurtz emphasizes.

As you explore possibilities with agentic AI, consider how your organization can optimize its operations, empower your team, and unlock the full potential of automation. Embrace the journey toward AI integration, and witness the remarkable transformation it can inspire.