Historically, finance transformation focused on efficiency. Organizations invested in automation to reduce manual work, accelerate reporting cycles, and streamline processes that had traditionally consumed significant time and effort. Those investments delivered meaningful gains across everything from accounts payable to the close.
Even with that progress, many organizations still spend much of their day managing the work that surrounds the work. This includes tracking close activities, chasing approvals, investigating unusual transactions, managing exceptions, and removing bottlenecks before they slow progress. It can also mean monitoring supplier performance, reconciling inventory discrepancies, responding to production disruptions, or flagging fulfillment risks before they affect customers.
Previous waves of automation, such as EDI or workflows that highlighted unpaid invoices, helped organizations complete tasks faster. Agentic AI goes further, taking on the coordination work itself — identifying issues, surfacing exceptions, and moving routine work forward within established guardrails. As a recent IDC study commissioned by Sage put it, this is AI “compressing the work around decisions,” so finance leaders can spend more time on analysis, judgment, and action.
The expectations placed on finance leaders have changed considerably over the last decade. Finance is no longer expected to simply report what happened. Business leaders look to finance for guidance on investment decisions, operational performance, risk management, and growth opportunities.
At the same time, complexity has increased. Businesses are managing larger volumes of data, operating across more systems, and making decisions at a faster pace than ever before. They’re also navigating a more volatile business environment, where supply chain disruptions, changing trade conditions, inventory volatility, rising shipping costs, and economic uncertainty can quickly reshape priorities. Technology has improved access to information, but it hasn't always reduced the operational burden of managing financial and operational insights across the business.
Consider the month-end close. Most organizations have already automated portions of the close process, yet finance teams still invest significant effort to track progress, resolve exceptions, and ensure activities are completed on time. Agentic AI can help address that challenge. Intelligent agents can monitor close activities, surface delays, identify unusual activity, and highlight areas requiring attention before they become reporting issues.
The same discipline applies well beyond the close. In accounts payable, an agent can catch an invoice that doesn't match its purchase order, or flag unusual changes in vendor information for review before a payment is processed. In forecasting, it can identify when costs are rising faster than pricing, putting pressure on margins, rather than waiting for that trend to emerge in a board deck weeks later. It can also surface supplier delays, inventory imbalances, or rising shipping costs that may ultimately affect financial performance.
None of this replaces the finance professional — it gives them more time to apply judgment, which is where they create the most value.
As agentic AI takes on a larger role inside finance and operations workflows, the conversation begins to shift from capability to trust. The IDC study found that trust, not capability, is increasingly the factor determining how quickly finance organizations adopt AI at scale. When technology can perform most of these tasks, the question then becomes how much responsibility organizations are prepared to give it.
Leaders can delegate activities, but not accountability. If a forecast is inaccurate, a payment is approved incorrectly, a production issue goes unnoticed, or a financial control fails, responsibility still sits with the business. When an auditor asks who approved a transaction and why, "the system flagged it" isn't an answer. It must be traced back to a person, a decision, and a reason.
That requires confidence in the underlying data, visibility into how recommendations are generated, clear controls over what an intelligent agent can act on, and the ability to trace every action after the fact. Together, those elements turn an AI recommendation into something a CFO is willing to stand behind in front of a board.
Organizations may be willing to use AI to generate insight without any of that in place. They are far less likely to trust it with action. Confidence, control, and accountability are what let organizations move past experimentation into workflows that run the business.
Finance leaders should start by asking themselves a few questions. Where is the team still coordinating manually instead of deciding? Where is time going to find information rather than acting on it? Where does judgment need to stay firmly in human hands, no matter how capable technology becomes?
The answers point to where agentic AI earns its place first — and where it shouldn't.
Disclosure:
This feature was contributed by the author on behalf of Sage. It is not paid content; it's use within The ERP Update is solely for educational and informational purposes.
The headline illustration of this feature was generated by The ERP Update using HubSpot AI in support of the contents within this article, it was not furnished by Sage and is not specifically representative of either the feature as a whole, or any Sage product that may be available.