The big wave in entrepreneurship after the pandemic resulted in significant disruption of most industries, particularly reflected in significant and widespread adoption of technology, both ancient and contemporary. Today, technologies such as artificial intelligence (AI) and machine learning (ML) are applied across multiple departments and help teams work synergistically at a faster pace.
Finance teams are no exception to this trend. The month-end close process greatly benefits from automation, reducing manual errors, streamlining internal controls, running recurring events and tasks, and providing real-time insight into the process for faster decision making.
However, it can be overwhelming and time-consuming to adopt new platforms and technologies to speed up processes, especially if you don’t know where to start. That’s why I’ve put together three main strategies to get you on the road to fully digitalizing your business and noticeably improving the closing process.
Automate low-value tasks
The data collected in these steps will help you quickly identify the core issues of your business so you can assess what to do next.
There is an increasing need to take tedious and repetitive tasks off your team’s plate so they can focus on what matters. But what can and should be automated when it comes to the financial close process?
Here are some recurring tasks that, if automated, can help your team check their status or progress at a glance:
- Preparing and checking balance sheet reconciliations.
- Complete and manage closing checklists.
- Analysis of balance flux and/or P&L variance.
- Data analysis on the health and status of the month-end close.
The data collected in these steps will help you quickly identify the core issues of your business so you can assess what to do next.
Not only will the adoption of automation tools further optimize the close process, but as technologies continue to evolve, teams merging them will significantly improve speed and accuracy. These investments result in financial and operational growth, provide better analysis and support the decision-making process.
