Companies are increasingly integrating agentic artificial intelligence (AI) into their financial operations to enhance efficiency and reduce human intervention. This technology, capable of acting independently within set objectives, is starting to reshape back-office functions like accounts payable, procurement, and cash flow management. As global enterprises look to stay competitive, CFOs and treasurers are evaluating whether embedding AI in specific platforms or centralizing it across systems delivers greater value. These decisions are becoming crucial as they define the pace of AI adoption and its effectiveness in financial operations.
Earlier AI implementations in finance primarily supported automation of repetitive tasks. Now, agentic AI introduces autonomous systems that interact with each other, such as invoice processors negotiating terms with liquidity managers. This marks a shift from passive automation to proactive system behavior. For example, where cash flow forecasting was once reactive, AI agents can now forecast and initiate actions in real-time, reducing time lags and administrative burden.
How Are Vendors Integrating Agentic AI Into Financial Tools?
Could These Tools Reshape the CFO’s Decision-Making Process?
Recent announcements illustrate the momentum. Tesorio revealed its new Supplier Portals Agent for autonomous management of invoicing and payment tracking within its platform for accounts receivable and cash flow management. Workato expanded its AI capabilities by acquiring DeepConverse, a move aimed at enhancing AI-driven automation in enterprise support functions. These developments reflect the growing demand for tools that support end-to-end process automation.
Leading financial institutions are also considering how AI will influence platform design.
“Do I want a central AI tool analyzing all systems? Or do I want AI embedded within key financial platforms like AP and ERP?”
asked Alexandre Hoffmann, general manager of North America at Edenred Pay. This question highlights the tension between centralized AI governance and embedded, task-specific agents.
The U.S. Securities and Exchange Commission recently hosted a roundtable to address AI’s growing role in financial services. Participants discussed potential risks such as data privacy, regulatory uncertainty, and algorithmic inequality. While companies are eager to adopt AI, regulatory caution remains a key consideration for finance leaders.
“It’s important to ensure that we’re capturing the different priorities of AP, IT, treasury and procurement stakeholders,”
said Holly Tennent, director at Bank of America.
The rise of agentic AI echoes earlier discussions about AI’s role in healthcare, such as Apple (NASDAQ:AAPL)’s effort to develop a health-focused AI agent. However, the enterprise finance sector is emerging as a more immediate and practical application area. Unlike consumer-facing AI, enterprise AI adoption is more likely to follow structured implementation paths, guided by measurable outcomes and cross-functional priorities.
CFOs and treasurers face strategic decisions in how to implement agentic AI: whether to centralize intelligence or distribute it across critical workflow platforms like ERP and AP systems. Financial departments with complex supply chains and liquidity needs may benefit the most, as AI can optimize decisions across vast transactional data. However, there is still a need for oversight, ethical considerations, and trust in automated decision-making systems. Successful adoption will depend on balancing innovation with regulatory compliance, workforce transition planning, and performance evaluation frameworks.
Organizations looking to implement agentic AI should begin by identifying areas with high volumes of repetitive activities, such as invoicing, payment approvals, and supplier negotiations. By piloting AI agents in these functions, companies can evaluate their impact on efficiency, cost savings, and error reduction. Technology partners like Tesorio and Workato offer modular deployment options, allowing businesses to scale gradually. It will be important for leadership teams to assess how different departments interact with AI tools and whether their current system architecture supports interoperability across platforms. Understanding these factors can help enterprises make informed investments as they integrate AI into financial processes.
