How Robotic Process Automation Helps Federal Agencies Manage Data

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When it comes to government deployments of automation and AI/ML, the spotlight is often on high-level applications, whether it’s increasing soldier performance on the battlefield, stimulate intelligence gathering or modernize the delivery of services to citizens.

Because of this, it’s easy to miss an ongoing transformation in the back offices of the Department of Defense and civilian agencies, where the adoption of robotic process automation is skyrocketing.

RPA technology requires a virtual software robot to mimic human actions in order to automate mundane, repetitive, and cumbersome tasks. Ideal candidates for automation include highly standardized processes with electronic data entry, such as streamlining data collection and processing, document management, identity verification, and automatic response to business inquiries. citizen information.

A 2021 survey sponsored by UiPath found that 6 in 10 federal respondents — and half of state respondents — view RPA as a building block for harnessing artificial intelligence and machine learning, accelerating data collection and improving data quality. Why? For starters, RPA has the lowest cost of entry into the intelligent automation space and delivers high rates of return when properly implemented.

RPA and the use of intelligent bots are proving essential for agencies looking to automate the time-consuming manual processes required to run a government agency. For agencies evaluating RPA, it’s critical to first identify which processes are best suited for automation — a decision largely driven by the impact on the time, money, and frustration saved with a new solution.

This article will detail how agencies at all levels can determine the best applications to automate through RPA, as well as the key benefits that RPA offers.

Strategies for Evaluating RPA Candidates

When evaluating where RPA can have the greatest operational and people impact, it is critical to work through the backlog of candidate processes. In other words, before RPA development even begins, perform a thorough analysis, improvement, and standardization of potential automations. Selecting the right candidates should not be done in a vacuum; this requires understanding the overall mission, business environment, and IT systems. With these considerations in mind, the five strategies below can guide decision makers toward the most effective candidates.

Is the process manual and repetitive?

The work best suited to RPA consists of repetitive manual processes that require an employee’s attention and involve following similar steps for each new entry. Processes that currently require human involvement or oversight, such as data entry or processing personnel records, will benefit the most from RPA because automating the process frees up workers’ time. employed for more meaningful tasks.

Is the process frequent or tedious?

Some business processes must occur monthly, some daily, and others on demand from thousands or millions of connected users. This consideration must be weighed against the burden incurred by the process.

Let’s look at a weekend report that takes an agency employee four hours a week to create and compare it to year-end reports that take twenty hours to complete. While year-end reporting is a more onerous task, weekend reporting occurs much more frequently, making it a top candidate for RPA. Automating the simplest but most frequent process saves two hundred hours compared to the twenty consumed by the “more important” task.

Does the process use rules or patterns?

AI-powered RPA solutions box being able to accomplish complex decision-making, but processes that follow an established set of rules or instructions enable the most effective use of intelligent automation. Even though the rules that guide a process are extremely complex – with branching paths of recommended next steps based on several different criteria for each input – a computer can internalize these rules much more effectively than a human.

Tasks that require some level of subjective judgment, on the other hand, are much more difficult to adequately approximate with a hard-coded set of rules. Modeled processes allow for much simpler RPA implementations, as AI can learn to perform complex analyzes of any input based on the appropriate model to make sense of the data.

Does the process support standard input and standard output?

Any process that considers a standard data type, such as a document, PDF, or spreadsheet, can be replicated by RPA trained to handle a given consistent data format. Processes that support multiple different formats, such as a mix of emails, paper receipts, and video recordings, require significantly more investment to automate. Standard output is also important – generating a file, logging information into a database, sending emails or updates over a network, etc. Scanned physical documents can be transformed through automatic natural language processing or optical character recognition through automated processes to transform these documents. into usable data.

How many business applications are involved?

The fewer business applications involved, the more efficient RPA can be, because the AI ​​only has to understand the inputs and outputs of a program and can interface directly with it. More advanced implementations can connect multiple applications and allow them to “talk to each other”, but this level of investment is most useful when the return on investment is expected to be substantial.

The benefits of RPA today and tomorrow

At the micro level, an illustrative use case of RPA can be found with the Air Force Mission Installation and Support Center, which faced significant challenges processing FOIA requests and responding through notification letters. Managers were overwhelmed by a backlog of hundreds of cases that were taking up their time on higher value tasks.

The FOIA process has caused significant drains of resources due to the procrastinating nature of the existing process, hence an optimal application of RPA to eliminate the error-prone process and strategically realign current resources. By replacing tedious manual tasks with automation, users could reduce their time investment in making critical decisions and reviewing work done by intelligent ‘bots’.

The result: a 30% reduction in the backlog and a significant increase in the generation of fees for requests processed on time. It also reduced processing time by 88% and increased accuracy to 99.9%. The implementation of RPA resulted in an overall reduction in employee workload and 2034 government FTE hours were reallocated for better use of time.

What’s next for RPA? As more agencies see tangible results, there will be a desire to continue the journey towards smarter automation. RPA is already evolving beyond basic rules-based chatbots, often used for customer and citizen support. Applications today take advantage of intelligent automation (AI), which Brookings defines as “…a type of RPA that includes AI, ML, or natural language processing (NLP). applied correctly, RPA and intelligent automation can reduce the number of overworked employees, make internal processes more accurate and faster, and make the organization more successful in the long run.

Mark Hogenmiller is Chief Transformation Officer at Aeyon, a provider of management consulting and data analytics services to the federal government.

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This article is an Op-Ed and the opinions expressed are those of the author. If you would like to respond or would like to submit your own editorial, please email Cary O’Reilly, C4ISRNET Senior Editor.

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