Dive Brief:
- Nearly eight in 10 corporate finance professionals blame month-end close delays on having to wait for data from other systems or departments, according to research unveiled Tuesday by finance software firm LiveFlow.
- Reconciling information across multiple platforms was another barrier, cited by more than half of survey respondents. The findings suggest that despite widespread investment in finance automation tools, key accounting workflows remain hampered by challenges like fragmented data environments, LiveFlow Financial Data Analyst Aaryn Ross told CFO Dive.
- “Companies are investing in AI, but they're still not closing the books as fast as they would like,” Ross said.
Dive Insight:
LiveFlow’s enterprise resource planning platform is designed to automate accounting and finance workflows, including month-end closes. Ross said the New York-based technology vendor conducted its survey in part to better understand where bottlenecks persist in finance operations.
The company found that while finance teams are increasingly equipped with advanced analytics and productivity tools, those capabilities have not yet materially changed the structure of the month-end close cycle.
Only 16% of respondents said they complete their month-end close in under three days. The largest group, 37%, reported cycles of three to five days, followed by 21% at five to 10 days and 16% extending beyond 10 days.
Ross said AI is commonly applied to higher-value work such as analytical and reporting tasks, while the underlying operational workload has shifted far less, leaving the most time-consuming parts of the close largely unchanged.
About 80% of respondents said they use AI in their work for drafting content, and 65% cited financial analysis. Adoption drops off for more operational use cases, with just 23% relying on AI feaures embedded in finance systems.
“Much of the repetitive work — data entry, transaction categorization, reconciling numbers — is still being done manually,” Ross said.
A November 2025 McKinsey report underscores how elusive meaningful operational impact from AI remains, even as adoption accelerates. Nearly two-thirds of respondents said their organizations have not yet begun scaling AI across the enterprise, with many initiatives stalling at the pilot stage or failing to integrate into core processes.
“To capture AI’s potential in finance, teams will need to do more than add new tools on top of old ways of working,” the authors said. “They must rewire core processes, talent, and technology so that adoption takes hold and creates value.”
