Accelerating digital transformation: Robotic process automation

What if there was a way to remove human error from business operations? To free up employees’ time, energy and headspace to focus on wider, critical objectives? To achieve all of this in a cost-effective, highly efficient and streamlined way?

Automation has paved the way for monumental digital transformation within the world of business. Its aim is to take everyday tasks out of human hands, placing them instead in the care of robots and computers – and there are any number of potential applications.

With the wealth of benefits this brings to enterprises, it’s easy to see why business process automation is such a huge player in the global business process management (BPM) market: it’s estimated to grow to a colossal $41.8 billion by 2033.

What is robotic process automation?

According to IBM, robotic process automation (RPA) – also referred to as software robotics – is the use of ‘automation technologies to mimic back-office tasks of human workers.’ RPA can replace the need for human intervention and labour in repetitive tasks that follow a series of steps, such as file management, data entry and extraction, and form completion. For human employees, such tasks can be tedious, standardised and routine – not to mention required hundreds of times each day; for automation technology, such repetitive actions can continue endlessly.

RPA software is able to interact with any system or application in the same way humans do, engaging in any processes that involve communicating with other digital systems, triggering responses, manipulating data and performing transactions. It provides error-free execution of repetitive business processes by way of software robots. The difference between RPA and machine learning (ML) and artificial intelligence (AI) is that RPA is restricted to routine tasks, whereas ML and AI can be utilised for problem solving, decision making, and other more complex activities.

RPA’s widespread use and adoption has led to the development of a number of popular RPA tools. Gartner’s 2023 Magic Quadrant for Robotic Process Automation reviews a cross-section of them, including:

• UiPath
• NICE
• Kofax
• Automation Anywhere
• Pegasystems
• Power Automate
• Blue Prism
• EdgeVerve Systems

How does robotic process automation work?

Machine learning and artificial intelligence technologies are not needed in robotic process automation. Instead, RPA is built on rules-based programming.

It uses a combination of application programming interfaces (APIs) and graphical user interface (UI) interactions to bridge enterprise and productivity applications. Manual tasks are recorded or programmed into software scripts and algorithms, developed either by users with programming skills or via an RPA platform’s low-code or no-code UI. From there, scripts that emulate human functions can be deployed and executed across unrelated software systems.

What are the benefits of robotic process automation solutions?

Robotic process automation enables businesses to get more out of each and every working day – as well as unlocking other, far-reaching advantages. In fact, 98% of IT specialists agree business process automation is vital to delivering end-to-end business benefits.

Some of the many benefits of RPA software bots are:

• the ability to work around the clock without stopping or tiring
• the speed at which tasks are performed
• guaranteed reliability and precision
• high-volume yield and increased productivity
• streamlining of business processes and workflow
• cost-effective alternative to human labour
• improved customer experience and customer satisfaction
• huge scalability potential
• enabling employees to focus their time and efforts on other tasks
• higher return on investment (ROI).

Bots excel in terms of volume, efficiency, consistency and repetition. With the cost savings, quality assurance and productivity they’re able to deliver, it’s easy to see why leaders across all industries are actively seeking out RPA implementations to transform their day-to-day business operations.

What are some common use cases for robotic process automation?

RPA solutions are hard at work in all manner of industries – including manufacturing, financial services, human resources, life sciences and telecommunications – overhauling everything from supply chain management to customer service. Common RPA use cases include processing payroll, website scraping, call centre operations, credit card applications, ecommerce orders and employee onboarding.

Let’s take the example of RPA technologies in the healthcare industry – a sector well understood to be struggling under extreme, sustained pressure across multiple fronts. RPA has successfully accelerated digital transformation efforts and supported providers to deliver higher quality patient experiences and outcomes. RPA-driven initiatives in healthcare include:

• appointment scheduling
• medical billing
• patient onboarding
• administrative data entry
• compliance management and auditing
• record and case management
• discharge instructions.

With an incredible volume of repetitive – but vital – tasks handled seamlessly by automation software and RPA bots, healthcare providers save money, time, and are able to focus employee efforts where they are most valuable.

And it doesn’t end there: the future of RPA technology looks set to transform accelerated automation, smarter automation, automation discovery, and the handling of ever-more complex and challenging tasks. This means that further game-changing benefits to healthcare – and any number of other industries – will soon be on the horizon.

What is hyperautomation?

Hyperautomation is the extension of process and operations automation beyond the remit of individual business processes.

It brings together AI, ML and RPA tools to enable virtually any repetitive activity to be automated. This combination offers a degree of flexibility and power previously impossible to achieve, automating undocumented processes that use unstructured data inputs. A common example of hyperautomation is the use of natural language processing (NLP) to understand and respond to emails.

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