Accelerating financial closes with help from AI agents: A pragmatic guide

IBM's pragmatic guide explains how AI agents can accelerate financial closes by automating data integration, reconciliation, and anomaly detection, but emphasizes that humans must remain in the loop due to risks of inaccuracy and regulatory requirements. The approach is particularly relevant for SAP environments, though applicable broadly.

Historically, financial closes required were tedious, manual-intensive processes, which makes them excellent candidates for agentification. AI agents can handle much of the “dirty work” associated with integrating financial data from various sources, reconciling transactions and so on. That said, there are limits on how far AI agents https://www.ibm.com/think/topics/ai-agents can go in streamlining and accelerating the closing process. It’s unrealistic for businesses to remove humans from the picture entirely. With this caveat in mind, here’s a look at practical approaches to driving more efficient financial closings with help from AI agents. To ground the conversation, I’ll focus on what the process might look like within environments based on SAP, although many of these lessons apply to any organization and tech stack. Although ERP systems like SAP house most or all of an organization’s financial data within a central system, closing out the books still tends to be a highly complex process, hampered by challenges like the following: These are all areas where AI agents can help, even if SAP’s Advanced Financial Closin https://www.sap.com/products/financial-management/advanced-financial-closing.html g is used. For example, instead of requiring humans to assess each irregular transaction manually, businesses can employ agents to review the situation and suggest a resolution. Agents also excel at tasks like integrating multiple data sources, then identifying and addressing redundancies or inconsistencies across them. Similarly, agents can continuously monitor financial workflows throughout the close cycle, flagging anomalies and potential bottlenecks before they delay reporting deadlines. They can automatically collect supporting documentation, validate data against predefined business rules and route exceptions to the appropriate stakeholders for review. By reducing the amount of repetitive manual work required during closing, AI agents help finance teams focus on higher-value analysis and decision-making. This can lead to faster close times, improved accuracy and greater confidence in the integrity of financial reporting. That said, agents can’t handle every aspect of the closing process entirely on their own. Two key limitations apply. The first is that, as with any LLM-powered technology https://en.wikipedia.org/wiki/Large language model , agents are at risk of making inaccurate decisions or inferences. Businesses can’t blindly trust agents to interpret financial data accurately all of the time. A second factor is that, due to strict regulatory requirements, it’s essential in most cases for humans to sign off on financial accounts. Telling regulators or auditors that you know your books are accurate because an AI agent told you so is not a recipe for compliance success. Because of these limitations, a healthy perspective on AI agents in financial closing contexts is to think of them as a way to improve visibility, agility and efficiency, not as a replacement for people. Agents can make recommendations, but humans need to be the ones who review, validate and sign off on any actions before they are final. How can organizations actually take advantage of AI agents to help with closing? The answer is complicated because every business’s books and closing process are different. This means that, despite the growing inventory of AI agents now available on platforms like SAP, it’s unrealistic to expect to “drag and drop” agents into existing closing workflows and have them do what they need. Instead, many businesses will find that they need to build custom agentic solutions. Often, they’ll benefit from implementing multiple agents targeted at different tasks, e.g., accounts receivable, accounts payable and foreign currency exchanges, along with an orchestrator agent https://learn.microsoft.com/en-us/azure/architecture/ai-ml/guide/ai-agent-design-patterns that oversees them all. Each agent will need to be tailored for the organization’s data sources, governance and compliance obligations, etc. In addition, organizations must carefully define how agents interact with financial systems and employees. While some activities can be automated end-to-end, others require human review and approval to satisfy internal controls and regulatory requirements. Establishing clear workflows, escalation paths and audit trails is essential to ensure that agent-driven processes remain transparent and trustworthy. Organizations also need to invest in testing and validation to confirm that agents produce accurate results and can handle exceptions without introducing new risks into the close process. The fact that SAP itself is a complex platform, with native agentic capabilities fully supported only in the latest versions, further complicates the agentification of the closing process. Enterprises need to assess the agentic support level available within the SAP version they use, then determine the extent to which they can leverage SAP’s own agents versus working with third-party agents. Another key consideration is data quality. AI agents can only perform effectively when they have access to complete, accurate and timely financial information. Organizations may need to improve data governance https://cloud.google.com/learn/what-is-data-governance practices and address integration challenges before agents can deliver meaningful value. The extent to which they can do this easily depends, in large part, on how healthy their underlying SAP data governance practices are. All of the above means that taking advantage of agents to accelerate closes and other financial workflows within SAP is no mean feat. It requires deep technical expertise in both agentic technology and the complex SAP software portfolio. But the investment is worth it for organizations seeking to reduce the uncertainty and slowness traditionally associated with closing the books. Over time, well-designed agentic workflows can help finance teams spend less time on manual reconciliation and exception handling while enabling faster, more predictable financial close cycles. This article is published as part of the Foundry Expert Contributor Network. Want to join?