Robotics and Automation in Federal Government

Robotics and automation influence the federal system big time

Government agencies are undergoing transformations in order to become more effective and efficient. Many public sector organisations are hierarchical and they operate on command-and-control concepts and are also labour-intensive but, more often than not they do not adequately utilise digital technologies for managing routine activities, as they were designed for industrial-era operations.

High costs, unresponsive organisations and public unhappiness are the results of this. People believe that the government is bloated and inefficient, and that it does not serve the public good. They are concerned about the government's ability to address emerging difficulties in public health, school, transportation, trade, and national defence.

What is Robotic Process Automation?

Robotic process automation consists of software or techniques that allow routine administrative chores to be automated. It creates rules for processing paperwork and uses a sequence of "if/then" decision making to complete tasks according to those rules. The technology can pay invoices, process claims, and execute financial transactions after certain requirements are met.

Travel reimbursements, data collecting, and claim processing have all been done with digital tools, as have administrative adherence and invoice processing. Chatbots that can answer typical questions, such as determining opening and closing hours, detailing how to exchange damaged things, or assisting with submitting complaints, are also included, freeing human workers from having to ask the same questions repetitively.

RPA tools have been used to save money and time in the past. KPMG collaborated with a technology firm to create a digital initiative that enhanced financial invoicing processing. Instead of manually handling hundreds of thousands of invoices, it created a programme that automatically filled in crucial information and corrected errors as needed. As a result, the company was able to save money and increase production by up to 70%.

Another example is PwC's collaboration with Boston Scientific to automate portions of the company's tax and finance activities. It was able to automate multiple different activities after analysing a number of frequent transactions and data-entry processes. PwC claims that this procedure cut overtime, increased productivity, and conserved around 3,900 person-hours annually. According to the company, this resulted in increased staff and customer satisfaction.

What is Intelligent Automation?

Intelligent automation (IA), also referred to as cognitive automation, is a kind of RPA that integrates AI, machine learning (ML), and natural language processing (NLP). This can include data analytics as well as more advanced types of analysis. IA is a dynamic system that is based on algorithms that can learn and modify as they gather experience. These technologies, rather than being mechanical or static, can comprehend data and make judgments that go beyond mere rule application.

Analysing agency hearing materials for subjects, processing complaint logs, and controlling customer satisfaction are all examples of IA. Data analytics may track agency efficiency, and IA technologies are a subset of solutions that represent a means to understand data in an increasingly complex and efficient manner. Because a lot of public data is unorganized, IA is well-suited to deciphering text or picture information that isn't formatted consistently or isn't well-organized.

Examples of Federal Government Usage

RPA tools are being implemented by a number of federal entities. More than two dozen government departments are actively using RPA, as per the General Services Administration's 2020 RPA Framework. Purchases, automated payments, trip payments, and activity recording are just a few of their applications.

Food and Drug Administration

Workers at the FDA's Centre for Biologics Evaluation and Research found themselves increasingly working, setting up meetings and doing the same activities over and over again. They used RPAs to automate tasks such as prescription intake forms for pharmaceutical companies. Thousands of these forms were received each week by the centre, which required a considerable time to process. The firm now uses RPAs to create bots that gather data and input application materials. It also employs machine learning and natural language processing to do regulatory reviews and risk assessments. This saves time for workers and helps to focus on higher-level tasks other than paperwork.

Social Security Administration

The social security administration used to receive 20,000 returned supplemental security income payments annually, which needed an average of three minutes to process. Employees were able to decrease their processing time to 3 seconds per transaction after installing an RPA system.

Treasury Department

RPAs are being used by this department to improve financial monitoring systems. The agency uses "standardised solutions that improve financial integrity and operating excellence across government," as per its officials. This allows it to increase its management resources while also reducing financial reporting and analytical errors.

Defense Logistics Agency

The agency is currently working on 111 automation projects in order to reap the benefits of RPA. Most of these include the automation of post-award agreements, such as requests for renewals or legal agreements. DLA is automating these operations so that humans do not have to manually react to each request for hundreds of hours. Its bots are particularly adept at "comparing data from numerous systems to check for discrepancies and errors," as well as "updating sales orders and payments."

Conclusion

The federal government, on the other hand, offers RPA and IA prospects. A number of agencies have already begun to use new RPA software, and they have reported favourable results. Each one exemplifies a method for increasing worker productivity and streamlining administrative processes. These programmes have been shown to save workers time and reduce data errors. As long as they don't add biases, lack accountability, or fail to follow federal privacy and security policies, their adoption and implementation brings clear advantages to agency operations.