What is Robotic Process Automation (RPA)?

RPA tools can record users performing repetitive tasks and generate a script that the software robot follows to perform said task. Oftentimes, the script that this bot creates is based on the clicks, entries, movements, and overall actions that the user performed on the screen. The RPA bot will then follow the script to repeat those steps — capturing data and manipulating the applications and systems just like the human would.

Automation has become a business-critical issue in this digital age as companies strive to improve productivity, enhance the customer experience, and quickly develop and launch new products and services. As the market for RPA technology grows, IT leaders are becoming increasingly interested in its ability to eliminate repetitive work, streamline operations, and provide cost savings. Some of the biggest benefits of RPA include:

• Efficiency: RPA is known to increase productivity for enterprise employees as they spend less time on repetitive tasks. Gartner has found that 30% of a full-time employee’s overall time can be saved with RPA.
• Accuracy: Data entry tasks are often done more accurately with automation than if they were done manually. RPA tools can also be in 100% compliance with industry and organizational policies.
• Cost savings: As employee productivity increases, the organization saves money. Employees are doing more, higher-value work in with the same amount of time.
• Access to legacy technology: Organizations still using legacy technology often struggle to integrate these tools with other cloud-based systems. RPA provides an easy entry point via the user interface to get data to and from legacy systems.

Like any new technology, RPA also has challenges, including:

• Displace human workers: One of the biggest criticisms of RPA and other automation technology is its potential to eliminate the need for data entry and clerical-style roles.
• Lacks intelligent capabilities: RPA as a standalone technology can only perform tasks that it can copy from a script — lacking the ability to learn and improve the script it’s performing. The next generation of AI and intelligent automation tools are now being introduced to address this pitfall of RPA.
• Project complexity: Transformative RPA projects tend to be complex and require a time commitment to see the return on investment — as a result, some RPA projects fail before the value is realized.

Fifty-three percent of organizations have already started their robotic process automation (RPA) journey, according to a survey conducted by Deloitte Digital. RPA has been dubbed one of the fastest-growing software categories by more than 200%.

RPA is used across industries to automate repetitive business tasks. Most often these use screen scraping as well as other automation techniques to move data from one system or application to another or perform an action.

Because RPA is mainly used for tedious, manual tasks — it is commonly used in roles, functions, and business units that spend significant time on these types of processes. RPA has played an essential role in the insurance, banking, and healthcare sectors, helping:

• Reduce costs and improve the customer and employee experience.
• Streamline customer onboarding.
• Automate data extraction, data entry, and processing across applications, documents, and images.
• Improve process accuracy and compliance.

RPA is most useful in organizations where employees perform a high volume of repeatable tasks and automation could free up their time to work on more value-driving tasks.

• Manual data entry and manipulation (e.g. CRM updates)
• Employee or partner onboarding
• Reporting and data aggregation
• Document generation
• Payroll processing
• User configurations
• Invoice and payment processing
• Order processing and shipping notifications
• Resume and candidate verification
• Expense management
• Loan, claims, and appeals processes
• Inventory and supply management

RPA is transforming various industries by automating repetitive and time-consuming tasks. Below are two real-life examples for how RPA is being used in the financial services and e-commerce industries.

A financial services company can use RPA to streamline its loan processing operations. Traditionally, loan processing involves multiple manual tasks such as data entry, document verification, and compliance checks, which are time-consuming and prone to errors. By implementing RPA, the company can automate these repetitive tasks, allowing bots to extract and input data, validate documents against predefined rules, and ensure compliance with regulatory requirements. This not only speeds up the loan approval process but also enhances accuracy and reduces operational costs, ultimately improving customer satisfaction and business efficiency.

An e-commerce business can use RPA to enhance its order fulfillment process. For instance, when a customer places an order, RPA bots can automatically handle order entry, inventory updates, and shipping label generation. These bots can cross-check inventory levels, update the database in real-time, and send notifications to the warehouse for packaging. Additionally, they can integrate with shipping carriers to create and print shipping labels, and send tracking information to customers. This automation reduces manual errors, speeds up order processing, and ensures a seamless and efficient fulfillment process, leading to increased customer satisfaction and operational efficiency.

Many people often think that RPA and APIs compete with one another — seeing as APIs allow the systems and datasets to be integrated while RPA scraps the data from one system to another. However, there are situations where an API-led approach could collaborate with RPA as a mechanism to broaden the scope of integration and enable access to more endpoints.

Where business process automation is not yet possible for an API-led approach, RPA may be used. Some examples of this are:

• A system lacks an API, such as a legacy or on-premises application or a system that is heavily customized for the organization.
• There is either delayed investment or lack of investment in the creation of an API for an endpoint — where RPA can be used as a stopgap for temporary access to the system.
• There are different teams managing automation and integration.

While AI and RPA are complementary technologies, they each have distinct characteristics that make them different.

AI refers to the development of systems capable of performing tasks that typically require human intelligence. This includes understanding natural language, recognizing patterns, learning from data, and making decisions. AI systems use complex algorithms and models to process and analyze large volumes of data, enabling them to improve over time and adapt to new situations.

RPA, on the other hand, is focused on automating repetitive and rule-based tasks that do not require human decision-making. RPA tools use software robots, or "bots," to mimic human actions such as data entry, transaction processing, and generating reports. Unlike AI, RPA does not learn from experience or handle complex tasks but excels in streamlining and automating structured, routine activities across various business applications.

The primary difference between AI and RPA lies in their capabilities and applications. AI aims to replicate human cognitive functions and can handle unstructured data, making it suitable for tasks requiring analysis, prediction, and decision-making. RPA is designed to automate specific, predefined tasks efficiently and accurately. While AI can enhance RPA by enabling bots to handle more complex scenarios, RPA focuses on increasing operational efficiency by reducing the need for manual intervention in repetitive processes.

As technology continues to evolve rapidly, the possibilities of RPA are expanding, particularly with the evolution of AI.

RPA has largely been used to automate repetitive tasks and connect legacy systems. With the introduction of AI, RPA bots are becoming more resilient and intelligent, allowing them to potentially handle complex tasks that involve unstructured data, such as understanding natural language, recognizing patterns, and making decisions based on predictive analytics. This synergy will allow bots to work more effectively with data, adapting to changes and learning from new information to continuously improve their performance. As a result, organizations can expect greater efficiency, accuracy, and flexibility in their automated processes, driving innovation and competitive advantage.

Solutions like MuleSoft RPA enable the composable business by replacing repetitive tasks with bots that can intelligently process documents, enter data, or act on the user's behalf, all without any code. This end-to-end business automation drives innovation and collaboration, streamlines processes, increases efficiency and speed, and enables users to integrate and automate with disconnected legacy systems. With MuleSoft RPA, customers can bring together best-in-class integration, API management, and RPA capabilities to transform into a composable business.

For more information on building a successful automation strategy, check out our CIO guide to enabling business automation.