Pt 2 in our Blog series focused on AP automation and relieving your AP team of manual labor
According to AIIM, 27% or organizations relying on manual processes require an average of 3-5 days for invoice data entry, validation and approval. This compares to companies relying on automated solutions where 26% indicated they require less than a day and 22% indicated they require less than an hour.
What’s the value of faster processing time? It isn’t just about paying invoices more quickly. One of the biggest sources of value is more timely, accurate access to financial data like g/l accruals, allowing companies to achieve greater visibility and control over cash.
That visibility and control can be leveraged to achieve greater cost savings, but looking at the benefit of paying vendors early and taking advantage of early pay discounts.
Dig one layer deeper and you discover that the ultimate advantage is that your finance team, including your AP staff, can spend more time analyzing data, looking at trends and applying their skills towards setting up your organization for future success, rather than trying to keep up with the demands of the past.
To take the friction out of back office processes like accounts payable, companies can rely more on their process owners and less on IT to deploy a solution. While an overwhelming majority (91%) of AIIM respondents indicated that they saw value in integrating an AP automation solution with financial systems such as ERP systems, the reality is that integration isn’t as complex as it used to be when it comes to finance automation deployments.
Today, integration as a service solutions streamline the integration process, and do so in a way that is flexible enough to prevent those integrations from breaking every time you go through a system upgrade.
Neither is the set-up and configuration of intelligent data extraction solutions that can ‘read’ invoices, extract and validate relevant information like invoice #s and amounts, vendor information and even g/l codes. In the past, these systems often needed a lot of pre-configuration to be able to handle a wide range of document types and formats.
Machine learning technologies help these systems to learn and adapt to new document types or process exceptions. Rather than relying on IT or a developer, these systems can take input and direction from a process owner, then modify or adapt their algorithms.
In reality, the classic objections to adopting process automation, particularly for routine, high-volume tasks like accounts payable invoice handling, fall apart in light of what modern solutions can deliver—and in the wake of the kinds of experiences and results companies of all sizes are achieving today.