Automation is changing the month-end close. But faster books aren’t necessarily better

Finance departments have traditionally seen “closing the books” as a frenzied period of entries and spreadsheets. But with new tools in hand, they’re now able to take on much of the repetitive work while taking the strain out of preparing financial accounts at month’s end.

According to a 2025 survey of top CFOs, 58% of middle-market CFOs reported forecasting delays due to fragmented systems. This phenomenon can be true across the board in many verticals. Dan Rogers, Manufacturing Industry Lead at Slalom, recently highlighted this in an article for Manufacturing Outlook, saying that, “IT had improved access to data, but plant employees still struggled to use that data in the context of their work…A dashboard could show uptime, production volume, or quality trends [but] it could not necessarily help someone troubleshoot a failed production system, investigate a quality issue, identify a likely root cause, or decide what to do next.” 

The potential plus sides of automation in finance too are significant, as faster closes can give executives faster access to information about cash flows and spending, as well as overall business performance. As the month ends, finance teams have to collect information from different systems, reconcile transactions, and prepare reports before management can confidently assess this performance.

But as automation moves deeper into financial reporting, a bigger question emerges: can companies close their books faster without compromising accuracy, accountability, or trust?

From month-end scramble to continuous finance

The traditional financial close is a process that can sometimes take weeks. In Ledge’s 2025 Month-End Close Benchmark Report, it was found that only 18% of finance teams closed their books within three business days, and even five-day closes can conceal bottlenecks, unresolved reconciliation items, or weak review controls. The reality is that it isn’t the duration of the close but the ability to reveal the underlying health of the process. 

It’s argued that AI can help make this transition to continuous finance possible. AI-enabled systems can match transactions, gather supporting documentation, and distribute unresolved problems to the appropriate people, instead of waiting for accountants to assemble information manually. This allows finance teams the ability to address discrepancies throughout the month, potentially leaving fewer outstanding issues when the reporting period ends.

Earlier this year, Eamonn O’Neill , co-founder and CTO of Lemongrass, described in CIO how AI agents can assist with tasks such as reconciling financial data, detecting inconsistencies, and monitoring workflows for bottlenecks. The approach isn’t limited to one accounting platform, although implementing agents in complex enterprise resource planning environments requires careful integration and configuration.

The shift will be from using automation to complete individual tasks more quickly towards redesigning the entire close process around continuous data validation and exception management. AI’s value in the financial close lies not just in identifying discrepancies, but in helping finance teams determine which one deserves our attention the most. By prioritising exceptions according to their potential materiality and risk, AI can help finance professionals focus their time where it matters most.

AI agents are taking us to the “zero-day close”

The monthly grind of preparing a financial close could benefit greatly from an autonomous mind. Or several even. AI agents offer a way to coordinate transactions, account balances, and journal entries and, depending on their configuration and the permissions they receive, they can retrieve information from approved sources and apply predefined rules. 

Rather than simply producing a summary, an agent-based workflow can help move an item through multiple stages of a process. For example, an agent might identify a mismatch between an invoice and a purchase order for a business.

In its August 2026 account of building an AI-native finance function, OpenAI described ambitions that include a “zero-day close” and continuously updated forecasting. The idea is to establish a reconciled view of financial performance that can support decisions as business conditions change, rather than relying exclusively on static month-end reports. The company presents these as ongoing ambitions, not as a universal capability that finance teams can implement immediately.

A zero-day close should therefore be understood as a direction of travel: the more work completed and validated during the accounting period, the less remains to be done after it ends. It doesn’t mean that every organisation can eliminate the work altogether.

The difference between closing quickly and closing well

As AI makes it easier to process transactions and clear routine exceptions, finance leaders will need to distinguish between process efficiency and process quality. A shorter close is useful, but it doesn’t automatically demonstrate that the resulting accounts are reliable.

Shagun Malhotra, founder and CEO of SkyStem, argues that measuring success requires a broader perspective than simply counting the days required to complete the process.

“A useful Close Quality Score should look beyond simply measuring how quickly the books are closed. I would suggest evaluating several dimensions, including close duration, post-close adjustments, unreconciled accounts or aging reconciling items as well as the number and severity of exceptions or the quality of review and control execution.”

For Malhotra, the weighting should reflect where the organization is most exposed. The score isn’t there to answer a broader question like ‘Did we close on time?’ Instead, businesses should be asking the question: ‘How confidently did we close?’”

This approach offers a pragmatic way to evaluate whether AI is genuinely improving the financial close. An organisation might reduce its close time substantially, for example, but still discover that it is making numerous adjustments after reporting, carrying unresolved reconciliation items or failing to investigate material exceptions adequately.

Malhotra also emphasises that, though AI agents can be a powerful tool, automation can’t become a substitute for professional judgment.

“Finance leaders need to distinguish between process efficiency and process quality. Automation should reduce repetitive work and improve consistency, but finance professionals still need to understand the exceptions, assess their significance, and determine when something requires deeper investigation.”

That distinction is particularly important because an exception isn’t necessarily an error, and a transaction that appears routine isn’t necessarily risk-free. AI can help identify patterns and prioritise work, but financial professionals must decide what those signals mean in context.

“The process may be consistent, but the risk isn't always consistent,” Malhotra said, adding that “the strongest finance teams aren’t necessarily the ones that clear the most items the fastest; they are the ones that can identify which signals deserve deeper investigation and act on them before they become larger problems.”

Ultimately, the emergence of AI in financial close is about more than compressing a process or the need for speed. Compressing what once took weeks into a matter of days isn’t the end goal anymore.. The new horizon of automation is an opportunity to give finance teams a more continuous, reliable view of business performance, while freeing professionals to focus on the exceptions that demand human judgment. 

Editor’s note
Every piece goes through careful verification, but mistakes can happen. Readers who spot an error or have additional information can write to rosalia@thebrightminded.com.