Data bottlenecks constrain finance teams in accelerating month-end closing process
Financial software company LiveFlow released research on Tuesday showing that nearly 80% of corporate finance professionals attribute month-end closing delays to waiting for data from other systems or departments. Cross-platform information reconciliation is another major obstacle, mentioned by over half of respondents. Despite widespread investment in financial automation tools, key accounting processes remain constrained by challenges such as fragmented data environments. LiveFlow financial data analyst Aaryn Ross pointed out that AI applications are mostly concentrated on high-value analytical work, while repetitive operations remain predominantly manual.
Key Takeaways
- Nearly eight in ten (about 80%) corporate finance professionals blame delays in month-end close on waiting for data from other systems or departments, according to research released Tuesday by financial software company LiveFlow. The study waspublished by LiveFlow。
- Reconciling information across multiple platforms was another hurdle, cited by more than half of respondents. The findings indicate that despite widespread investment in financial automation tools, key accounting workflows remain hampered by challenges such as fragmented data environments. Aaryn Ross, financial data analyst at LiveFlow, told CFO Dive.
- "Companies are investing in AI, but the speed of closing the books still hasn't met expectations," Ross said.
Deep Dive
LiveFlow's enterprise resource planning platform is designed to automate accounting and finance workflows, including month-end close. Ross said the New York-based technology vendor conducted the survey partly to better understand where bottlenecks persist in finance operations.
The company found that although finance teams are increasingly equipped with advanced analytics and productivity tools, these capabilities have not substantially changed the structure of the month-end close cycle.
Only 16% of respondentssaid they could complete month-end close within three days. The largest group (37%) reported a close cycle of three to five days, followed by 21% of respondents needing five to ten days, and another 16% with cycles exceeding ten days.
Ross noted that AI is often applied to higher-value work, such as analysis and reporting tasks, while the underlying operational workload has shifted little, leaving the most time-consuming parts of the close process largely unchanged.
About 80% of respondents said they use AI at work to draft content, and 65% use it for financial analysis. In more operational use cases, adoption drops significantly, with only 23% of respondents relying on AI features embedded in financial systems.
"A lot of repetitive work—data entry, transaction classification, number reconciliation—is still done manually," Ross said.
A November 2025 report from McKinsey confirms that even with accelerated AI adoption, achieving substantial operational impact remains difficult. Nearly two-thirds of respondents said their organizations have not yet begun enterprise-wide AI rollout, and many projectsremain stuck in pilot phases, or have failed to integrate into core processes.
"To unlock AI's potential in finance, teams cannot simply layer new tools on top of old ways of working," the report's authors wrote. "They must restructure core processes, talent, and technology to make AI adoption truly take hold and create value."