Over the past two years, agencies have focused on shifting the workforce to “high-value” work — a key goal of the President’s Management Agenda — by taking advantage of robotic process automation and other technologies to reduce error, improve compliance and eliminate repetitive administrative tasks.
The Playbook continues: “If the government deployed RPA at scale and achieved only 20 hours of workload elimination per employee, the net capacity gained would be worth $3 billion — and that is only scratching the surface.”
RPA, a building block for AI
Many agencies across the federal government have initiated RPA programs to automate tasks of varying complexity across multiple functional areas including finance, acquisition, IT, human resources, security, and mission assurance. Popular uses of RPA include data entry, data reconciliation, spreadsheet manipulation, systems integration, automated data reporting, analytics, customer outreach and communications.
In 2019, the Food and Drug Administration’s Center for Drug Evaluation and Research reported it had seven RPA projects in development, including one that automated drug intake forms and freed up the pharmaceutical and medical staff for the agency’s core science mission. Last year, the Defense Logistics Agency completed a first-of-its-kind proof of concept in government that allowed unattended bots to work around the clock. DLA recently reported it has saved more than 200,000 labor-hours with the 82 RPA bots it launched in the past year, CIO George Duchak said during an AFCEA DC virtual event in May. In fact, using basic bots is the first step in the agency’s AI journey, he said.
RPA/AI use case: Transaction matching, fraud prevention
Large financial management offices struggle to resolve and match hundreds of thousands of transactions, many of which require significant manual effort. An RPA solution can automatically access data from various financial management systems and process transactions without human intervention, but it will fall short when data variances exceed tolerances for matching data and documents and will result in unmatched transactions. The addition of an AI/ML capability would accelerate the handling and processing of data and associated actions, including matching financial transactions or identifying fraud.