Business processes have advanced rapidly in the past few years, with exciting developments in artificial intelligence and machine learning promising huge returns across every sector. The implications are exciting, especially from an IT perspective; with new automation opportunities come opportunities for significant improvements in productivity and innovation, as well as reductions in operational costs.
Among all this change rests a lesser known but equally important strategy: robotic process automation. RPA made its mark on the IT sector long before AI took over and it is not about to be displaced anytime soon. Instead, experts anticipate that AI will amplify existing RPA strategies.
This could mean substantial changes in both IT departments and among business leaders. Moving forward, both types of professionals will need to be well-versed in RPA, especially as it relates to AI. To clarify these solutions, let’s take a deep dive into robotic process automation for IT: how it works, how it has evolved, and how it can be implemented in the age of AI.
What Is Robotic Process Automation?
Robotic process automation draws on a blend of computer vision and machine learning to expedite and automate a variety of rule-based, trigger-driven tasks so they can be completed without human intervention.
Nintex's Aaron Bultman stated The Enterprisers Project that this strategy involves a "form of business process automation that allows anyone to define a set of instructions for a robot or ‘bot’ to perform." He added that RPA bots can mimic "most human-computer interactions to carry out many error-free tasks, at high volume and speed." Ivanti's Marcel Shaw further simplifies the concept with the evocative term "virtual robot copycat."
When Was RPA Invented?
Robotic process automation entered the IT landscape in 2012, when the term was coined by HFS Research CEO and chief analyst Phil Fersht. It was conceived as an IT development process, with pioneering vendor Blue Prism referring to the "custom applications produced by its software and methodology" as robots.
History of Robotic Process Automation
While robotic process automation took its most noteworthy leap forward with the introduction of Blue Prism's revolutionary system, the underpinnings of this movement arrived far earlier, with automation entering the picture in a big way during the Industrial Revolution.
Through the late 1800s and early 1900s, machines began to handle complex manufacturing processes that had once been completed exclusively by hand. Meanwhile, concepts such as process mapping entered the lexicon, revealing how workflows could be clearly depicted and expedited.
Another major stride forward occurred during the 1980s when digital workflow management systems emerged. At this point, however, essential knowledge and skills often remained siloed, with few transparent processes available for conveying essential information. Enterprise resource planning (ERP) began to fill in the gaps in the 1990s, revealing that a variety of day-to-day business activities could seamlessly be managed by integrated software systems.
The next big leap involved the widescale adoption of Agile processes, which emphasized continuous improvement across numerous dynamic iterations. Agile called for more efficient operations, so user interface (UI) testing was automated to bridge the gap. Screen scraping, in particular, was heavily used to automate data extraction. In the early 2010s, businesses sought new opportunities to reduce expenses — and many saw RPA as a realistic solution for facilitating digital transformation.
How Does Robotic Process Automation Work?
At its most basic level, modern RPA involves accessing data from existing IT systems, often through backend integrations with databases and enterprise services (although a myriad of front-end connections can also facilitate this). Captured data can then be integrated with current applications, with the end goal of automating tasks. Software applications can be taught to complete specific, repetitive tasks within a complex workflow that may encompass many steps.
Specific rules defined for RPA robots must include which actions they are supposed to take, and under which set of circumstances. Once the desired response has been triggered, the RPA completes specific actions according to instructions from the developer. Examples might include:
- Copying and pasting data
- Completing forms
- Opening emails
- Logging into various applications
The bots are instructed by a series of variables (such as numbers, names, or other fields that can be manipulated) and conditions (such as if/else or if/then). The latter makes branched scenarios possible, but within the confines of loops, which cause the bots to repeat desired actions until certain conditions have been achieved.
Where Is Robotic Process Automation Used?
RPA is incredibly versatile and, in the proper context, can enhance productivity across a wide range of industries and enterprises. There's no denying the value of robotic process automation for IT, but it is also an increasingly popular solution in the following fields:
- Finance. Many high-volume tasks in the finance sector involve manual, repetitive effort. Examples especially suited to RPA include payables and receivables, widely regarded as the industry's most mundane and time-consuming tasks. RPA has also been cited as a compelling solution to streamline client onboarding, tax reporting, and vendor invoice collection.
- Manufacturing. From inventory management to order processing and even regulatory compliance, RPA promises to boost productivity and accuracy throughout the manufacturing sector. This is increasingly regarded as the best way to resolve ongoing concerns regarding labor shortages and extended time-to-market.
- eCommerce. When handled by humans, return processing can be time-intensive, leading to increased costs and significant delays. Through RPA software, eCommerce businesses can expedite the process of updating inventory and internal billing systems.
- Healthcare. From appointment scheduling to claims management and even asset tracking, there are numerous opportunities to build RPA into otherwise expensive and time-consuming healthcare processes. RPA promises to improve revenue cycle management systems and could even play a valuable role in boosting regulatory compliance.
RPA Benefits for Business
Disruptive technologies such as RPA are poised to transform technology and the business market as we know it. The 2022 McKinsey Global Industrial Robotics Survey indicates that industrial spending in this niche is accelerating quickly. Key benefits associated with RPA implementation include:
- Reduced operational costs - Often the primary driver of RPA adoption (at least to start), cost-cutting represents a considerable advantage. Simply put, automated systems cost less and produce more than their strictly human counterparts.
- Increased accuracy - Many manual rote tasks are highly susceptible to human error. RPA systems, however, are highly accurate, as they can be depended on to consistently follow defined rules. This, in turn, plays into even lower operational costs, as the need for reworking tasks is greatly diminished.
- Higher employee morale - By taking over the dullest and most repetitive tasks, RPA frees up employees to fully utilize distinctly human skills, such as creativity and empathy. Team members can instead set their sights on solving problems that call for higher-level thinking, making their work more rewarding and producing significant improvements in general morale and satisfaction.
Processes That Utilize Robotic Process Automation
Not all processes are ideal candidates for RPA. This strategy is typically reserved for tasks that involve extensive human data processing, with RPA regarded as a supplement for repetitive functions. RPA is best utilized when the following standards apply:
- The process is rule-based, with clearly defined inputs and outputs.
- The process involves a pre-defined trigger or repeats at regular intervals.
- The process calls for a high volume of repetitive tasks.
RPA and Artificial Intelligence
The relationship between RPA and artificial intelligence (AI) is complex, with many experts questioning whether RPA qualifies as AI or is simply related. While RPA systems are valuable, they don't learn on the go like their AI counterparts. That said, these strategies can complement one another.
A helpful analogy: RPA functions a lot like the blue-collar workers of yesteryear, primarily handling manual tasks, while AI takes a similar role to white-collar professionals. Both are essential and when they collaborate effectively, both can drive impressive gains in productivity and profitability.
Machine Learning and Repetitive Tasks
As a subcategory and specific application of AI, machine learning draws on existing algorithms to reveal patterns garnered through a wealth of data, thereby gaining insights without requiring direct instruction or programming. Through experience, machine learning systems gain the ability to make informed decisions and predictions, albeit within a highly defined scope of practice. This differs from RPA, in which reprogramming is required whenever the needs of the project or process change.
While RPA is not as dynamic as machine learning, it can play a vital role within modern machine learning applications: through its impressive output, it can be used as an engine to accelerate machine learning capabilities.
As IBM's VP of Product Management Bill Lobig explains, "RPA has driven a significant rise in document extraction technologies, systems integration and process mining," adding that, while these systems must be used in conjunction to fuel intelligent automation, "RPA and machine learning are a big part of it."
An exciting example exemplified by the consulting firm Booz Allen Hamilton: Automated solutions that capture refund requests by phone. This information is transcribed and categorized based on the customer's intent — details that can be translated into RPA triggers.
How to Implement Robotic Process Automation
RPA implementation should be carefully planned and executed to ensure that all parties involved know what to expect and which outcomes RPA will be used to achieve. First, the objectives must be identified, with strict verification procedures used to determine whether RPA is a good match for the process in question.
The human side of implementation must also be considered. This means introducing team members and leaders to automated systems and revealing how these solutions will improve their day-to-day functions in the workplace.
Along the way, tech support must be mobilized, and performance metrics set based on previously identified goals. A detailed implementation plan can determine when specific tasks will be automated, who will oversee these projects, and the extent to which human or financial resources may be required along the way.
During the software deployment phase, a few key necessities must enter the picture:
- A proof of concept to validate the technical components of the automation effort. This must be configured and tested to ensure its compatibility with existing IT infrastructure.
- Bots are deployed according to a detailed plan that outlines how they will be managed and monitored.
- Metric tracking verifies ongoing accuracy and processing times, with adjustments made as necessary when bots fail to perform as expected.
Information Technology and Business Involvement
As RPA enters new industries and departments, its most noteworthy applications will center around information technology but under the guidance of tech-savvy business leaders. IT professionals will no doubt have a huge say in how these systems are shaped and implemented, but there is also a strong need for insight from business professionals. Well-versed in both IT and business administration, these experts should understand not only how RPA functions but also how it can be implemented in a strategic and highly goal-oriented manner to ensure it reaches its full potential.
Business leaders can facilitate the seamless implementation of RPA and the adoption of systems that enhance communication between departments, thereby limiting the information silos that have so frequently accompanied RPA's human-based predecessors. It will also take a nuanced perspective to determine when RPA is called for — and when alternative solutions can be employed to greater effect.
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