The opportunities for automation to digitally transform federal operations are enormous and have the potential to yield billions of dollars in savings. Many agencies have already started to employ RPA to automate tasks across a number of areas – including finance, acquisition, IT, human resources, mission support and security assurance. In one example, a federal agency that deployed RPA in its financial shared services center saw 40 separate processes become 100 times faster. It’s estimated the bureau will be able to reallocate $7 million worth of labor over the next five years.
Harnessing the full value of automation
When agencies get up and running with RPA, they often struggle to further advance and scale their automation efforts for a number of reasons:
- They quickly learn that processes and workflows are more complex than expected.
- They may have multiple automation systems in place that are difficult to manage and integrate.
- Automatically processing unstructured or semi-structured data is difficult because data is spread across many systems that don’t talk to each other.
- They may lack the necessary governance to ensure automation is done properly and reliably.
- Automation efforts that use standalone components and solutions make it hard or impossible to scale to any degree.
To truly advance and scale automation efforts to maximize return on investment, agencies need what the industry analyst firm Gartner calls hyperautomation, which refers to a varying roster of complementary tools that can integrate silos to more effectively automate business processes.
Making the jump to hyperautomation
To see how a government agency can capitalize on the power of hyperautomation, look no further than the eligibility verification process of the Affordable Care Act health insurance program. The program processes tens of millions of consumers each year, along with up to 45 million documents annually. To pull this off successfully — and most importantly, efficiently — the program uses an intelligent automation platform to automate the eligibility verification process and AI-enabled, data-capturing tools to extract data at massive scale.
In the end, hyperautomation completes as many as 300,000 processes per month, saving substantial labor hours and providing surge capacity that otherwise would have to be manual.