INTRODUCTION TO RPA
The computing industry has evolved from mainframe computers that physically took up entire buildings, to the microprocessor in the 1970s, to present-day smartphones and tablets that have more processing power than mainframe computers ever had. This evolution introduces more opportunities to automate our daily tasks and workflow, examples of which we have seen throughout history: automated software testing and deployments, sending emails and message routing, voice command software (e.g., Amazon Alexa, Google Assistant, and Siri), the list is endless. RPA is a current trend and is being viewed in the information technology (IT) industry as the next step in the evolution of automation.
WHAT IS RPA?
RPA is the process of automating tasks or workflows that don’t require inference or insight, eliminating simpler types of tasks from humans. These tasks are responsibilities that can be performed through business rules or codified logic and are usually represented as scripts or “bots.” RPA is being widely integrated across many different business applications and problems, unbeknownst to many of us. Any task that can be automated with business logic or a programming language can be considered an application of RPA.
What is new for many organizations is how RPA is transforming enterprises with the integration of artificial intelligence (AI). AI is the ability for computer programs to complete tasks that normally require cognitive or human intelligence. Speech recognition, automatic photo recognition, and advanced decision making are all examples of AI. The integration of RPA and AI introduces powerful possibilities to solve complex business problems. For example, an AI application can be built to recognize people and objects in photos (e.g., facial recognition, and object detection) and integrated with a RPA process that will automatically send notifications to users when an object or person is identified. AI can provide complex and detailed analyses to make business decisions, and RPA can be the facilitator that sends decision outcomes to recipients and users.
While the integration of RPA and AI can solve problems that require complex reasoning and analysis, RPA tasks can be implemented independently without AI and still have a broad impact on an organization. A prominent example is eliminating the need to groom or prepare data prior to data analysis or data ingestion. It’s an unfortunate fact that data analysts and scientists invest a lot of time preparing data into specific formats before more complex and interesting analysis can be performed. Ingestion of data across different formats, sizes, and types also requires a lot of data preparation and grooming before data enters a database or data platform. RPA can be leveraged to automate these tasks altogether, freeing up analysts, workflow managers, data scientists, and software developers from this type of uninteresting and monotonous work.
RPA adoption and integration can be applied across many federal and commercial practices. While not inclusive of all industries or problem spaces, examples of where RPA could have significant impact include banking and insurance, regulatory industries and organizations (e.g., the Securities Exchange Commission, and the Consumer Financial Protection Bureau), security (e.g., Authority to Operate [ATO] documentation and process automation), acquisitions (e.g., IT Acquisition Review), and patent examination and review (e.g., the US Patent & Trademark Office).
Labor savings on data entry and analysis tasks – RPA removes the need for analysts to perform time-intensive data grooming and preparation tasks, which results in labor savings while also enabling analysts to focus on more critical or urgent workflows and analyses.
Reduction in workload – RPA can perform simple tasks, enabling staff in an organization to focus on complex problems and decision making, ultimately reducing their workload and stress, and providing a better work experience.
Accuracy and quality – minimize workflow errors and improve accuracy rates.
Improved compliance – data governance and other types of compliance (e.g., regulatory and financial) can be enforced through RPA.
Flexibility and reusability – RPA components are reusable and more modular than standard macros and scripts. RPA applications can also be reused across an organization and its business units, providing scalability and reuse across an enterprise.
When assessing if RPA is a good candidate for your organization, it’s important to first understand the problems your organization is trying to solve. There are many products in the market that are focused on RPA, but there is not a “one-size-fits-all” product or model in the industry for complete RPA implementation. Implementing RPA solutions requires detailed analysis into requirements, workflow management, and the data within an organization. Once RPA solutions are implemented and scaled across the enterprise or within a small business unit, your workforce will immediately see the long-term benefits and a more focused and leaner workflow.
EGT Labs is prototyping RPA and AI solutions, and our thought leaders have already developed solutions available for many different types of challenges, including automated cloud deployments and migrations, and complete DevOps and development workflow automation.
Contact us at firstname.lastname@example.org to find out how you can leverage RPA and AI at your agency!
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