Finance leaders are increasingly leveraging agentic AI to automate accounts payable (AP) processes, transforming manual tasks into autonomous workflows and driving significant return on investment (ROI). While general AI projects saw an average ROI of 67% last year, autonomous agents, capable of handling complex processes with minimal human intervention, delivered an impressive 80% ROI. This notable performance gap underscores a critical need for chief information officers (CIOs) to re-evaluate automation budget allocations.
Agentic AI systems are now bridging the gap between theoretical value and tangible business returns. Unlike generative AI tools that excel at summarizing data or drafting content, agentic AI executes predefined workflows within strict operational rules and approval thresholds, offering a distinct advantage in process automation.
This strategic pivot is largely driven by boardroom pressure. A recent report highlights that nearly half of chief financial officers (CFOs) are facing demands from senior leadership to integrate AI across their organizations. However, a significant portion of finance leaders acknowledge that many custom-developed AI agents have been deployed primarily as experimental probes into AI capabilities rather than as direct solutions to pressing business problems. These experimental approaches often fall short of delivering a clear return on investment. Traditional AI models typically generate insights or predictions that necessitate human interpretation, whereas agentic systems integrate decision-making directly into the workflow, effectively closing the loop between insight and action.
As Jason Kurtz, CEO of Basware, points out, the tolerance for unstructured experimentation is diminishing. “We’ve reached a tipping point where boards and CEOs are done with AI experiments and expecting real results,” he stated. “AI for AI’s sake is a waste.”
**Accounts Payable: The Proving Ground for Agentic AI in Finance**
Finance departments are strategically deploying these intelligent agents in high-volume, rules-based environments. Accounts payable (AP) has emerged as a primary use case, with a substantial majority of finance leaders identifying it as the logical starting point for agentic AI implementation. The AP process is well-suited for autonomous deployment due to its structured data nature: invoices are received, undergo cleaning and compliance checks, and ultimately lead to payment processing.
Teams are utilizing these agents to automate critical tasks such as invoice capture and data entry, which occupy a significant portion of finance leaders’ daily routines. Other live applications include the detection of duplicate invoices, identification of fraudulent transactions, and the reduction of overpayments. These are not speculative applications but rather real-world tasks where algorithms can operate with a high degree of autonomy, provided the parameters are correctly set.
The success of agentic AI in this domain is heavily contingent on data quality. Leading solutions, for instance, are trained on vast datasets of processed invoices, enabling them to deliver context-aware predictions and differentiate between legitimate anomalies and errors without requiring constant human oversight. This capability is crucial for maintaining accuracy and efficiency in high-volume environments.
Kevin Kamau, Director of Product Management for Data and AI at Basware, aptly describes AP as a “proving ground” because it uniquely combines scale, control, and accountability, elements that are harder to find in many other financial processes.
**The Build Versus Buy Decision Matrix**
Technology leaders are now grappling with the decision of how to best procure these agentic AI capabilities. The term “agent” itself is broad, encompassing everything from simple workflow scripts to sophisticated autonomous systems, which can complicate procurement strategies.
Procurement approaches tend to diverge based on functional areas. In accounts payable, a notable preference exists for agentic AI solutions embedded within existing software, compared to those built in-house. Conversely, for financial planning and analysis (FP&A), a greater proportion of finance leaders opt for self-built solutions.
This divergence suggests a pragmatic framework for executive decision-making. If the AI capability enhances a process that is common across many organizations, such as AP, then integrating a vendor-provided solution often proves more efficient. However, if the AI application is designed to create a unique competitive advantage for the business, developing it in-house may be the more strategic path. The general guideline is to buy for accelerating standardized processes and build for differentiation.
**Governance as an Enabler of Speed**
A significant hurdle to the adoption of autonomous systems is the apprehension surrounding potential errors. A substantial percentage of finance leaders express reluctance to deploy agents without robust governance frameworks in place. This caution is understandable, as autonomous systems necessitate stringent guardrails to operate safely and effectively within regulated business environments.
However, the most successful organizations do not allow governance concerns to impede deployment. Instead, they leverage governance as a mechanism for controlled scaling. These leaders are considerably more likely to utilize agents for complex tasks, such as compliance checks, compared to their less confident peers.
Anssi Ruokonen, Head of Data and AI at Basware, advocates for treating AI agents akin to junior colleagues. While trust is essential, these systems should not be empowered to make critical decisions without careful vetting and gradual introduction of autonomy. A human-in-the-loop approach, particularly during the initial stages, is crucial for maintaining accountability and ensuring responsible operation.
Concerns about job displacement due to automation are also prevalent. A significant portion of finance leaders believe that job displacement is already occurring. Proponents of agentic AI, however, argue that these technologies are more likely to transform the nature of work rather than eliminate it entirely.
By automating routine, manual tasks such as data extraction, organizations can free up valuable human resources to concentrate on higher-value strategic activities. The ultimate objective is to shift from mere task efficiency to achieving operating leverage, enabling finance teams to accelerate closing cycles and make more informed liquidity decisions without necessarily increasing headcount.
Organizations that extensively implement agentic AI report superior financial returns. Leaders who deploy these tools on a daily basis for core functions like accounts payable consistently achieve better outcomes than those who limit their usage to experimental phases. Confidence in AI capabilities grows through controlled exposure, where successful small-scale deployments pave the way for broader operational trust and enhanced ROI.
To emulate the success of early adopters, executives must transition beyond unguided experimentation towards purposeful implementation. Data indicates that a large majority of finance teams experiencing weak returns did so under pressure without clear strategic direction, in stark contrast to the much smaller percentage of teams achieving strong ROI who operated with defined objectives.
Achieving success with agentic AI requires its seamless integration into existing workflows and the application of governance principles, much like those applied to human employees. “Agentic AI can deliver transformational results, but only when it is deployed with purpose and discipline,” concludes Kurtz.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/17410.html
