Across the board, federal agencies are making significant investments in IT modernization and digital transformation. Much of this effort is driven by executive action and regulatory pressure designed to create a more digitally enabled, secure government infrastructure that can withstand increasing cyberattacks while improving the end-user’s experience.
However, another significant part of this story is federal agencies’ need to streamline operations amid tightening budgets and workforce constraints. Today’s federal employees are being asked to do more with less. As agencies struggle to adapt to growing demand, robotic process automation (RPA) is one way they can simplify existing workflows to help make their employees more efficient and effective.
RPA delivers scalable efficiency rewards with minimal risk
Much of RPA’s promise lies in its ability to automate repetitive tasks in a relatively low-risk environment. This frees up federal employees to devote more time to higher-value, more complex work without creating the same security concerns that agencies must account for when implementing artificial intelligence (AI).
RPA is a rule-based, process-driven form of automation that performs a limited, pre-defined function. If an RPA bot is asked to perform a task outside of its core function or if there’s a problem with the underlying data, it will simply generate an error message and refuse to perform it. RPA is also relatively low-cost and quick to implement. It does not require custom software or deep systems integration, and it can be deployed as part of a software-as-a-service (SaaS) model—making it easy to scale across multiple workloads.
RPA is also a critical first step toward embracing more complex technologies. This progression would lead to intelligent process automation (IPA), which combines RPA with other technologies, such as process mining, analytics, and AI. IPA flows naturally into adopting AI to support the future of work, where users can take advantage of automation that frees them to perform higher-level tasks.
AI brings numerous benefits in terms of speed, scale, and accuracy, particularly when dealing with massive amounts of data. At the same time, AI (in particular generative AI) also requires updated governance and additional diligence: organizations must thoroughly evaluate the AI model’s underlying code to ensure it was developed responsibly and without bias. They also have to implement sufficient guardrails to ensure the AI can’t be abused or manipulated into exposing sensitive data.
This does not mean that federal agencies should avoid AI altogether. In fact, the White House is pushing for increased investments in AI as a core enabler of its national security mission. Agencies just have to take more stringent security precautions when using AI in their environment compared to RPA.
Free up human expertise with automation
Challenges with RPA typically come from change management. The good news is that the federal government knows how to solve this problem.
Before implementing RPA, federal agencies must secure stakeholder buy-in from process owners and agency leadership to identify the right opportunities for automation and ensure existing processes can be effectively completed by an RPA bot. Ideally, agencies should create an embedded RPA pilot team that can interview stakeholders, identify which processes should or should not be automated, facilitate implementation, and measure the technology’s impact over time.
The interview process and stakeholder buy-in are especially important for RPA success because they help federal employees view automation as an enabler of workforce productivity rather than a competitor that will take their jobs. RPA is not designed to replace human intelligence. Rather, it can offload repetitive tasks so that employees have more time to focus on high-value work that drives mission success and contributes to their professional development.
For example, every year the DoD and its constituent elements go through a financial audit to ensure that its financial statements are presented fairly in accordance with generally accepted accounting principles. As part of this process, the DoD receives notices of findings and recommendations (NFRs) that inform the agency of any issues identified during the audit. From there, organizations will develop corrective action plans (CAPs) to address the NFRs. Any CAP closure attempt must then be validated to determine whether or not the NFR can be considered resolved. Depending on the complexity of the NFR, this process can add significantly to DoD employees’ workloads. RPA steps in to help offset that burden, taking over basic data entry or admin functions so that employees can focus on developing and executing CAPs in time for the following year’s audit. Under this model, RPA does not detract from anyone’s core job function. Rather, it frees up employees to focus their efforts on complex, interesting problems that require human ingenuity and creativity to solve.
Ultimately, RPA supports federal priorities by ensuring that automation complements rather than competes with human expertise. As agencies continue modernizing operations, leveraging RPA can serve as a foundational step in fostering a digitally empowered federal workforce, primed to meet growing demands and uphold mission priorities with resilience and agility.
