Technology and Telecoms invoice management – catching up with progress

19 October 2020

Even in the most outwardly advanced enterprises, the processing of invoices for technology and telecommunication services tends to be approached in outdated and time-consuming ways.

Marco Almeida, Head of Operations at Adapt IT, says it is not unusual for local enterprises to spend enormous amount of time every month manually capturing and allocating data from service provider invoices. “Companies may have thousands of employees and contracts with multiple service providers, so the number of invoices to be checked and captured monthly can require weeks of work by admin staff every month and presents the possibility of errors creeping in.”

It’s ironic that while payment processes have long since been digitised and automated, the invoice management process has lagged.  “Companies need their high-volume invoice management to catch up with payment processes,” Almeida says.

Digitising and automating technology and telecoms invoice management takes out the slog work – and the errors.

 

“Automated technology and telecoms invoice management is the solution to validating and vetting thousands of invoices from multiple suppliers.  Deploying automation and advanced workflows simplifies the entire environment and takes costly human resources out of the process, along with the risk of human error. It becomes possible to check massive bills, screen for duplicate line items and discrepancies as well as automatically flag exceptions. It furthermore supports contract compliance, IT governance and controls.”

In addition to speeding up processing and reducing reliance on manual work, automation of invoice management correspondingly presents opportunities for the enterprise to integrate the automated, digitised data from invoice processing into the broader enterprise systems. “The data from this environment feeds back into their CRM and ERP, bringing with it the potential to add benefit to the business. This data can be used in terms of big data analysis and bring new understanding that impacts operational models and forecasting,” He notes.