A new report from SAP Concur and Kelton Global finds universities waste 2,000 hours each month manually processing expense reports and invoices.
As “universities and colleges face pressures to increase enrollment, these hours could add up to anywhere from 15-18 full-time employees who could be focusing on more mission-critical work,” according to David Ballard, senior vice president for public sector at SAP Concur.
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One major concern, the report notes, is the number of financial inaccuracies that can occur when so many expense reports are processed by hand.
About one-third of decision-makers said the number of inaccurate reports and invoices that have been submitted has increased over the past year.
These include everything from minor accounting errors to potentially fraudulent charges. Close to one-quarter of respondents said they’ve accidentally reported incorrect information because of errors in expense reports or invoices, according to the report.
A contributing factor to this risk is the sheer number of hours it takes to process each form. With so many to go through — with each expense report taking an average of five hours and each invoice taking an average of two hours — administrators do not have time to look carefully through each filing to catch crucial errors.
“Fixing these big-ticket issues — loss of man hours, reporting errors and budgeting inaccuracies — sounds like a major uphill battle,” Ballard writes. “Especially when organizational leaders and their employees across different departments are so busy with day-to-day operations that they don’t have the time to rethink processes and create culture shifts around how they operate.”
To avoid errors and wasted hours, higher education institutions are turning to artificial intelligence to help organize and file expense reports.
While smart tools do not take the burden of filing paperwork completely away from university staff, a partnership between man and machine can make the process more efficient, leaving more time for more important tasks. As this collaboration continues, AI tools will learn how reviewers assess expense reports and adjust to be increasingly helpful.
“You could think of it like an apprenticeship,” Thomas W. Malone, founding director of the MIT Center for Collective Intelligence, says in a post for T Brand Studio, the brand marketing unit of The New York Times. “Sometimes the apprentice may ask some explicit questions or the expert may make an explicit comment, but the apprentice learns a lot just by watching. I call it a cyber-human learning loop.”
Injecting smart technology can help with other administrative tasks as well, opening up opportunities for employees to come up with creative, strategic solutions to improve workflow in such a labor-intensive environment.
“Factor in the labor of admissions and retention staff and supporting administrative personnel and one begins to develop a high appreciation for the amount of effort that institutions put into the learning and success of all their students, both online and traditional,” notes a recent Learning House report. “As artificial intelligence (AI) becomes more effective and machine learning becomes increasingly capable of internalizing complex concerns, we approach an age where faculty and staff can be relieved of many labor-intensive, but ultimately rote, tasks.”