Robotic Process Automation (RPA) 2023

by Akshat Narwal · Published 19/04/2023 · Updated 14/05/2023

Robotic Process Automation (RPA) 2023

What is robotic process automation?

Robotic Process Automation (RPA) is a technology that mimics how humans interact with software to perform high-volume, repeatable tasks. RPA technology creates software programs or bots to log into applications, input data, calculate and complete tasks, and copy data between applications or workflows.

When combined with AI and machine learning, RPA can capture more context of the content you’re working with by reading text or handwriting with optical character recognition (OCR); when extracting entities such as names, billing terms, or addresses using natural language processing (NLP); and by capturing more context from images, such as automatically estimating accident damages on an insurance claim image.

RPA is gaining popularity because it can reduce costs, speed up processing, and drive better customer experiences. Another attraction of RPA software is that business units can implement it without learning new tools or calling on IT teams for support and without changing an organization’s underlying IT infrastructure.

However, as RPA has gained popularity, companies see the need to integrate RPA process automation into their IT systems. While RPA automation can dramatically speed up a business process previously handled by humans, bots can break when application interfaces or process workflows change.

Newer RPA tools use artificial intelligence, machine vision, and natural language processing to mitigate breakage issues. Modern RPA platforms also provide some integration with centralized IT governance and management capabilities, making it easy to expand the use of RPA across the enterprise.

Robotic Process Automation (RPA) 2023

How does RPA work?

RPA reflects the way people are used to interacting with and thinking about software applications. RPA’s ability to mimic the way humans perform a computer-based process has contributed to its popularity compared to automation tools like application programming interfaces (APIs) or low-code development that are more scalable. , but less intuitive or require expert knowledge to use.

These six benefits of RPA can help companies achieve digital transformation.

The simplest RPA bots can be created by recording the clicks and keystrokes when users interact with an application. When issues arise, a user can watch the bot connect with the app and identify the steps that need to be adjusted.

In practice, these basic recordings often serve as a template for building more robust bots that can adapt to changes in screen size, layout, or workflows. The most sophisticated RPA tools use computer vision to interpret the icons and layout on the screen and make any necessary adjustments.

Some RPA tools can also use these initial recordings to create hybrid RPA bots that begin by simply registering an existing workflow and then dynamically generating workflow automation on the back end. These types of hybrid bots take advantage of the simplicity of RPA development and the scalability of native workflow automation.

In other RPA implementations, process and task mining tools automatically capture business process workflows, serving as starter templates for RPA automation. For example, process mining can automatically analyze logs from ERP and CRM applications to generate a map of common business processes automatically. Task mining tools use a locally running application with machine vision to capture a user’s interactions across multiple applications. All the major RPA vendors are starting to develop these types of process mining integrations.

RPA tools can also be connected to AI modules that have capabilities such as OCR, machine vision, natural language understanding, or decision engines, resulting in what is called Intelligent Process Automation. These capabilities are sometimes packaged into cognitive automation modules to support best practices for a particular industry or business process.

Who uses RPA?

RPA is used in most industries, particularly those that include repetitive tasks such as insurance, banking, finance, healthcare, and telecommunications.

RPA is used in finance to automate governance, reconcile accounts, or process invoices.

RPA is used to automate various supply chain processes, including data entry, predictive maintenance, and after-sales help desk.

RPA is used across industries to automate high-volume memory tasks.

Telcos use RPA to set up new services and the associated billing systems for new accounts. Telcos also use RPA to extract data from various systems when evaluating equipment outages or predicting problems.

All the major system integrators, including Capgemini, Deloitte, EY, Genpact, Tata Consultancy Services, and Wipro, are using RPA to help build vertical applications that make it easier for companies to adopt best practices in their niche.

What are the benefits of RPA?

Robotic process automation technology can help organizations on their digital transformation journeys by doing the following:

enabling better customer service;
ensuring that business operations and processes comply with regulations and compliance standards;
drastically speeding up processing time;
improving efficiency by digitizing and auditing process data;
reducing costs by reducing manual and repetitive tasks; and
allowing employees to be more productive.

RPA implementations can be challenging. Here are five pain points that executive leadership must be prepared to address.

What are the challenges of RPA?

There are a number of challenges related to RPA, which have limited its use:

Scalability. Companies have had difficulty scaling RPA automation initiatives because while RPA software bots are relatively easy to implement, they can be difficult to govern and manage and, therefore, difficult to scale.
Limited abilities. Although its name includes “process automation,” many critics have pointed out that RPA software tools automate tasks. More work is often required to tie multiple tasks into one process. Craig Le Clair, an analyst at Forrester Research, cautioned companies to follow the “rule of five” when building RPA applications because they tend to fail when a bot is required to make more than five decisions, manipulate more than five applications, or do more than 500 clicks.
Security. RPA bots sometimes need to access sensitive information to complete their tasks. If compromised, they pose an additional security risk to businesses.
Limited resistance. RPA failures can occur when applications change in ways developers don’t anticipate.
New quality control issues. Bots require a variety of new QA practices to ensure they continue to work as intended.
Privacy. Bots may engage in work with personally identifiable information that is governed by privacy requirements. Teams must ensure this data is processed in compliance with local data protection laws, such as the GDPR. For example, if an RPA bot moved data outside a certain country without encryption, that would violate Article 44 of the GDPR. RPA vendors are starting to look at ISO 27701 certification as a foundation for managing sensitive information.
Efficiency. RPA bots manually traverse an application the same way a human does. This may not be as efficient as automating applications via APIs or workflow automation built into the application itself.

RPA applications

Some of the top RPA applications include the following:

Customer service. RPA helps businesses provide better customer service by automating contact center tasks, including verifying electronic signatures, uploading scanned documents, and verifying information for automated approvals or rejections.
Organizations use RPA for general ledger, operational accounting, transactional reporting, and budgeting.
Financial Services. Businesses in the financial services industry use RPA for foreign exchange payments, automating account opening and closing, managing audit requests, and processing insurance claims.
Health care. Medical organizations use RPA to handle patient records, claims, customer service, account management, billing, reporting, and analytics.
Human Resources. RPA can automate HR tasks, including onboarding and offboarding, updating employee information, and timesheet submission processes.
Supply chain management. RPA can be used in supply chain management for procurement, automating order processing and payments, monitoring inventory levels, and tracking shipments.

Top RPA Providers

Listed in alphabetical order, the following are some of the top RPA providers:

ABBYY has long been a leader in developing OCR tools to streamline back-office applications. The company has recently expanded to help extend its automation capabilities to more use cases.
Automation Anywhere provides an enterprise digital workforce platform for procurement, payment, quote-to-cash, human resources, claims processing, and other back-office processes.
Blue Prism is focused on helping organizations in regulated industries automate processes by offering desktop-aligned robots that are centrally defined and managed.
Kryon provides full-cycle automation capabilities, including process mining, governance, and AI modules that can extend RPA capabilities.
NICE has traditionally focused on improving customer interactions with call centers across multiple touchpoints. The company expanded its various automation capabilities to support RPA, focusing on improving customer experience across multiple channels.
Pegasystems has traditionally been a leader in business process management (BPM) tools but expanded into RPA with the acquisition of OpenSpan in 2016.
UiPath offers an open platform to help organizations efficiently automate business processes.

What to look for in RPA software

When business leaders look at RPA technologies, they need to consider several things:

Scalability. Enterprises are encouraged to select RPA platforms that can be centrally managed and scaled from a central control panel rather than deploying and scaling on each desktop.
Speed. Companies should be able to design and test new robotic processes in a few hours or less and optimize bots to work quickly.
Reliability. As companies release robots to automate hundreds or even thousands of manual tasks, they need to look for tools with built-in monitoring and analytics that allow them to monitor the health of their systems.
Simplicity. Organizations should look for simple products for company employees to create and use to handle various types of work, including collecting data and turning content into information that enables leaders to make the best business decisions.
Intelligence. The best RPA tools can support simple task-based activities, read and write to any data source, and take advantage of more advanced learning to further improve automation.
Business class. Businesses should look to tools built from the ground up to achieve enterprise-grade scalability, reliability, and manageability.
Governance. Companies should look at various security and governance capabilities to help manage bot security credentials, assess privacy concerns, and flag any issues.
Financial planning. Tools to track bot usage can help teams assess the ROI of existing bots and prioritize opportunities for new automation based on estimated value.

Management-level decision-making around RPA

Although automation software will replace many jobs, others will be created for those who maintain and improve RPA software.

As software robots replace people in the business, management-level executives must be held accountable for ensuring that business results are achieved and new government policies are followed.

Robotic process automation technology also requires the CTO or CIO to take a more leadership role and take responsibility for business outcomes and the risks of implementing RPA tools.

In addition, the COO, IT manager, human resources manager, and the relevant management-level executive who owns the process being automated must work to ensure the availability of a secure, enterprise-grade platform for control and operation bots on all systems.

The future of the RPA market is driven by hyper-automation

A report by Global Market Insights Inc. expects the RPA market to reach $5 billion by 2024. Increased adoption of RPA technologies by organizations to improve their capabilities and performance and increase cost savings are key drivers for the expected growth of RPA.

Although RPA has been popular for its simplicity, companies need help scaling implementations. Gartner predicts that in the long term, the growth of RPA will be accelerated by hyper-automation.

Hyperautomation efforts combine RPA with other automation tools, including low-code and no-code development tools, BPM tools, and decision engines. The IPA and Cognitive Automation modules will make integrating AI capabilities into this automation easy.

Process and task mining will help identify new automation. Other AI governance tools will help companies manage the overall process to streamline processes in ways that ensure trusted AI.

As hyper-automation takes hold, companies must develop a strategic approach to identify and generate automation opportunities and manage the overall process across the enterprise. Some organizations have established an automation center of excellence to coordinate and scale automation projects.

Forrester Research has predicted that the collective impact of these various types of automation technologies could help companies save $132 billion in labor value in the US alone.

Human-robot: how to trust AI?

Researchers, led by Matthew Gombolay, director of the Cognitive Optimization and Relational (CORE) Robotics Lab at Georgia Tech, recently investigated human-robot dynamics.

Through a series of experiments, they proved that humans sometimes need to trust a robot’s recommendations. This mistrust is particularly evident regarding the actions they must perform. Scientists then developed the cobot to explore areas where robots and humans can collaborate to accomplish tasks. Using table tennis, this program determined when the human could work closely with the robot called “the Barrett WAM arm.” This robot is equipped with a camera and a racket trained in tennis, thanks to learning by imitation.

A possible solution would be to design more transparent robots in their decision-making processes.

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Robots can become great work partners.

Gombolay and his team trained a robot to become a safe table tennis partner through machine learning. For this, they used data from previous work on table tennis and demonstration learning techniques. These are, for example, courses where humans show the robot how to hit a ball.

To test the robot’s skills, they devised a positive reinforcement system for successful and negative reinforcement systems for failed volleys. The experiments showed that the robot was able to work closely with humans.

These results make it possible to envisage the development of intelligent systems capable of working in collaboration with humans both physically and socially.

Humans still have a hard time trusting robots.

The researchers also needed more trust from the human participants during these experiments. Despite the explanations given by the robot, the latter were less inclined to collaborate with it. One of the possible reasons for this mistrust is that the robot does not have the same motivations as its human partner.

As soon as the participants felt that the robot had the same objectives, they were more likely to trust its recommendations. According to Gombolay, it is important to develop robots that can communicate effectively with humans in a way that instills trust.

Decision making: should robots be trusted?

Powerful machines

The development of increasingly intelligent robots, capable of learning and evolving, has gradually changed our decision-making processes: as is the case in the context of recruitment (link article recruitment), humans are increasingly assisted – and even replaced – by robots whose algorithms are capable of analyzing and interpreting an ever-increasing variety and number of data.

What is the advantage of this automation? Delegating decision-making to robots would save time and productivity, but in some cases, make better decisions (their algorithms allow robots to process more information and do it more adequately than humans).

Several factors to consider

So how do you decide for which tasks to delegate decision-making to robots? This is the question posed by Vasant Dhar, professor of information systems and editor of the journal Big Data.

Based on his experience in the field, Vasant Dhar proposes a reflection framework to guide the allocation of decision-making between robots and humans. This decision framework is based on two dimensions:

The predictability factor:

The automation of decision-making becomes interesting when the predictability is strong, that is to say, when a predictive system (human or automated) will do better than chance.

The risk factor:

The interest in automated decision-making should not be the only decision factor: it is also essential to consider the risk involved, i.e., the consequences of an error and its costs.

Vasant Dhar has developed a graph from these two dimensions that visualize the fields where automation can be trusted. Bottom line: the lower the cost per error and the greater the predictability, the more robots can be left to handle decision-making.

But beware, as this map shows, these “automation borders” are far from fixed:

The risk factor (the cost of an error) can, for example, decrease with the improvement of the legal framework.
The predictability factor can – and should – increase with improving the technologies used (strengthening their predictive capacities).

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Robotic Process Automation (RPA) 2023