Apr 18 2024

Leveraging RPA, AI and Automation in Government Processes

While RPA is meeting some automation needs, agencies are also looking to advanced technologies, including artificial intelligence, to solve more complicated process challenges.

The government continues to grapple with a cybersecurity staff shortage, despite recent attempts by the Biden administration to fill thousands of vacant positions.

Fortunately, agencies are harnessing a growing technology area to meet some of their staffing needs: robotic process automation.

“Across the government, agencies are short-staffed, and they are seeking ways to accomplish more work but with the same staff count,” says Steve Shah, senior vice president of product at Automation Anywhere. “You need to make the team more productive, so there’s a huge payoff in using automation. With so much data coming in, you’ll want to use accelerators such as automation to speed up that process.”

The cyber talent shortage is a prime example of where RPA can serve as a force multiplier, taking over repetitive, rules-based processes that require little coding and that can be deployed with minimal staff training. Employees then have more availability to take on higher-level tasks supporting the agency’s mission.

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Beyond standard RPA, recent artificial intelligence innovations have dovetailed with RPA to provide another option: AI-enhanced RPA. And stand-alone AI is also finding applications within agencies.

Careful planning and appropriate implementation of these technologies goes a long way toward addressing not just the cyber talent shortage but other staffing- and process-related challenges hindering effective government business operations.

How Are Agencies Using Robotic Process Automation?

The General Services Administration pioneered the use of RPA in federal agencies back in 2018, launching ten automations, or bots, with a goal of reducing employees’ workloads and enhancing the experience of submitting budget justifications to GSA. Since then, RPA bots have been adopted across agencies for a wide variety of tasks, acquisition, administrative and financial business functions in particular. Some examples include:

  • Acquisition: The Defense Logistics Agency rolled out RPA to close out long-term contracts and agreements in sustainment, restoration and modernization.
  • Administrative: NASA is using bots to monitor its budget and accounting mailbox for emails about working capital fund advances.
  • Financial: The Navy implemented RPA bots to take screenshots of authorization letters for purchase orders.


The number of federal employee members active in the Robotic Process Automation Community of Practice

Source: General Services Administration, Federal Robotic Process Automation Community of Practice, July 25, 2022

“With RPA, you want to make sure it is a very defined process with very clear goals,” says Terry Halvorsen, vice president of federal client development at IBM. “The automation is just replacing the process. You are applying it to tasks that are repetitive, mundane or rules-based.”

“The federal space is dominated by paper, and cross-department automation is not common,” Shah says. “They often ask, ‘Where can we find savings?’ Applying automation to back-office tasks has been happening. The next step is to bring this to the front office, where automation can integrate with existing workflows and bring large cost reductions.”

The Challenges of RPA

While RPA is a good fit for repetitive, rules-bound processes, it cannot be deployed for tasks that require even limited decision-making. This restricts the range of its applications. AI-enhanced RPA opens the door to some of those use cases.

“One of the key differentiators between RPA and AI-enhanced RPA is the level of autonomy and task specificity,” says Amy Spruill, senior vice president and managing director of U.S. regulated industries at SAP. “RPA is more about configuration and simple rules and is applied to specific, preconfigured tasks. AI-enhanced RPA sits in the middle, blending the specific task orientation of RPA with the broader capabilities of AI.”

Terry Halvorsen
If it’s just RPA that’s automating a process that is very defined, it is automated. But if AI is included, then humans should be in the loop.”

Terry Halvorsen Vice President of Federal Client Development, IBM

AI goes one step further. For a given workflow, AI provides cognitive automation — imitating how a person thinks and learns. It can make decisions on its own. For example, when coupled with computer vision technology, AI can read unstructured data, such as a handwritten invoice, and decide how to respond and process it.

“AI has more autonomy and can handle a wider range of tasks,” Spruill says. “It can ‘understand’ a document in a way that algebraic rules simply cannot. AI use cases are plentiful, from finding errors or patterns in finance and supply chain to assistance for pilots, surgeons or maintenance techs.”

The Challenges of AI

The role of human beings in the decision-making loop is one of the key differentiators between RPA, AI-enhanced RPA and AI technologies. Across the spectrum of these technologies, RPA requires less direct human involvement, while AI requires human oversight.

“If it’s just RPA that’s automating a process that is very defined, it is automated. But if AI is included, then humans should be in the loop,” Halvorsen says. “If we look at the cybersecurity example, we might be using AI, but it is still an analyst that is making the final decision. Where in the loop do they fit? That’s the question for many agencies using RPA with AI.”

Another challenge of using AI with automation is managing the data that it is trained on.

“With AI-enhanced RPA and AI, the quality of the data going in is important. You need to have humans check that,” Halvorsen says. “If there’s a deviation or you have to apply a recommendation to the process, you really need to make sure the data sources are transparent, accurate and auditable, and you need to understand how that data could be impacted by attackers.”

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RPA vs. AI vs. AI-enhanced RPA

For agencies struggling to fill positions or looking to drive greater efficiencies in their operations, RPA, AI-enhanced RPA and AI offer different paths to meeting these needs. Part of the challenge is determining the right technology for the task.

If you can define workloads into set rules or tasks, then it’s probably suitable for automation via RPA,” Spruill says. “AI helps when you need intelligence or advanced logic, but all of this requires having a clear strategy from top-level management. Start building teams that can gather user needs and use cases, then develop functional requirements.”

In thinking about where to apply these technologies, it’s also helpful to look beyond the individual task and tech. Understanding where a single task fits into a larger process or use case can help determine the best technology option for the job. This requires management to think more holistically, with an orchestration mindset.

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“We are now seeing a shift toward process orchestration,” Shah says. “With intelligent process automation, we can now think at an orchestration level. I have 20 tasks to automate, not a single task. That’s big-picture automation. You end up with a richer set of processes rather than simple tasks. Long-running processes take the idea a step further by no longer depending on something happening immediately. They can wait until the necessary action occurs, such as the delivery of a package, before moving forward with the process.”

Measuring Success with Automation and AI

At GSA, recent findings from an inspector general review found that the agency lacked evidence supporting the purported work-hour savings it had claimed for its ongoing RPA program. Being able to measure and define success accurately should be a key component of any RPA, AI-enhanced RPA or AI implementation.

“I’d use three rules to measure success with automation and AI,” Spruill says. “How much time is saved from the task? How much time is saved from an employee’s week? And how much time is saved by the team? If you want more precise metrics, bring in a lean engineer to perform value stream mapping, but these three make great starting benchmarks.”

NicoElNino/Getty Images

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