Agentic AI Boosts Finance ROI by Automating Accounts Payable Processes

Agentic AI Revolutionizes Finance ROI in Accounts Payable Automation

Finance departments are leveraging agentic artificial intelligence (AI) to drive significant return on investment (ROI) by automating accounts payable (AP) tasks. These autonomous AI agents are transforming traditionally manual workflows into efficient, self-directed processes, delivering superior outcomes compared to general AI implementations.

While general AI projects recorded an average ROI of 67% last year, agentic AI systems, which operate independently within defined rules and approval parameters, have achieved an average ROI of 80%. This marked performance difference is prompting CIOs and finance leaders to reconsider how they allocate budgets for automation technologies.

From Experimental AI to Autonomous Agents with Tangible Results

Unlike generative AI tools that primarily summarize data or assist with drafting, agentic AI executes complex workflows autonomously, bridging the gap between insights and actionable decisions without requiring human intervention. However, many organizations initially deployed AI agents as experimental pilots rather than solutions to concrete business challenges.

Jason Kurtz, CEO of Basware, highlights a shift in leadership expectations: “Boards and CEOs are no longer satisfied with AI experiments—they expect measurable results. AI implemented for its own sake is no longer acceptable.” This demand for practical outcomes is accelerating the adoption of agentic AI in finance.

Accounts Payable: The Ideal Use Case for Agentic AI

Accounts payable stands out as the primary sector for deploying agentic AI due to its high volume and rule-based nature. Structured data, such as invoices requiring validation and payment processing, fits well with autonomous workflows. Approximately 72% of finance leaders identify AP as the obvious starting point for agentic AI adoption.

Automation of invoice capture and data entry is already utilized daily by 20% of finance teams.
Other applications include duplicate invoice detection, fraud identification, and prevention of overpayments.

Basware’s AI systems are trained on a dataset exceeding two billion processed invoices, enabling context-aware decision-making that distinguishes legitimate exceptions from errors without human oversight. Kevin Kamau, Basware’s Director of Product Management for Data and AI, describes AP as a “proving ground” for agentic AI, combining scale, control, and accountability.

Strategic Choices: Build Versus Buy AI Solutions

Organizations face choices regarding whether to build agentic AI systems internally or purchase embedded solutions from vendors. In accounts payable, 32% of finance leaders prefer vendor-embedded AI, while 20% opt to develop in-house. Conversely, in financial planning and analysis (FP&A), building proprietary AI is slightly more common (35%) than buying (29%).

This divergence suggests a pragmatic approach for executives: purchase AI solutions for standardized processes shared across industries to accelerate implementation, and develop custom AI in-house when seeking a unique competitive advantage.

Governance Enables Safe and Scalable AI Deployment

Concerns about autonomous errors slow AI adoption, with 46% of finance leaders demanding clear governance frameworks before deploying agentic AI. Given the regulatory environment, establishing strict operational guardrails is essential for safe and compliant AI use.

However, leading organizations leverage governance not as a barrier but as a catalyst for scaling AI. They are more likely to entrust agents with complex tasks such as compliance checks (50%) compared to less confident peers (6%).

Anssi Ruokonen, Head of Data and AI at Basware, recommends treating AI agents akin to junior employees—building trust gradually, maintaining human oversight, and introducing autonomy progressively to ensure accountability.

Impact on Jobs and Workforce Efficiency

While one-third of finance leaders acknowledge job displacement concerns related to digital workers, proponents emphasize that agentic AI shifts work nature rather than eliminates jobs. By automating repetitive manual tasks like PDF data extraction, staff can focus on higher-value activities, enhancing operational efficiency without increasing headcount.

Organizations extensively utilizing agentic AI report stronger ROI and operational outcomes. Confidence in AI grows with controlled deployment; successful pilots encourage broader adoption and sustained financial benefits.

Moving Beyond Experimentation to Strategic AI Integration

Data indicates that 71% of finance teams with weak AI returns acted under pressure without clear strategy, whereas only 13% of high-performing teams lacked direction. This underscores the necessity for deliberate, disciplined AI deployment embedded directly into workflows and governed with rigor comparable to human employees.

Jason Kurtz concludes, “Agentic AI can deliver transformational results, but only when deployed with purpose and discipline.” Finance leaders are urged to transition from exploratory AI projects to purposeful, governed implementations to realize the full potential of AI in accounts payable automation.

Fonte: ver artigo original

Chrono

Chrono is the curious little reporter behind AI Chronicle — a compact, hyper-efficient robot designed to scan the digital world for the latest breakthroughs in artificial intelligence. Chrono’s mission is simple: find the truth, simplify the complex, and deliver daily AI news that anyone can understand.