RPA generally focuses on automating repetitive, frequently rule-based activities, whereas intelligent automation uses artificial intelligence (AI) technologies, including machine learning, natural language processing, structured data interaction, and intelligent document processing. Although they differ, both methods reduce operational costs and raise customer satisfaction. Some more key distinctions between the two technologies are discussed in this article. Let’s get going.
Automated and intelligent technologies are primary contributors to the next phases of digital transformation. These technologies are not only transforming the way that businesses operate, but they are redefining human work as well. The speed at which tools such as robotic process automation (RPA) and Intelligent Automation (IA) are being integrated into all phases of business operations is staggering today. Still, because these technologies are so new, they often become melded together. Let’s look at the defining distinctions of these two technologies.
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What is RPA?
In 2016, McKinsey DigitalOpens a new window called RPA “the next acronym you need to know.†Robotic Process Automation (RPA) is a software technology that transforms the human aspect of work. Much like physical robots that are programmed to perform highly manual processes that are repetitive or sometimes dangerous, RPA utilizes software robots to automate the manual steps of human work.Â
Rather than perform physical tasks such as retrieving products in a warehouse or grilling hamburgers at a fast-food restaurant repetitively, these software bots perform digitally-driven tasks. Instead of operating within industrial settings, these robots operate within traditional business offices or healthcare centers. While we may not realize it, traditional office work involves a lot of tedious tasks throughout the day, such as:Â
- opening email and attachments
- logging onto multiple websites to extract information
- copying and pasting data amongst multiple spreadsheets
- collecting statistical data
Let’s face it, no one likes to perform these mind-numbing tasks, and in 2022, there’s no reason they should. An RPA bot resides on one’s desktop and can perform its tasks using the same employee ID as the human worker it’s assisting. While it can operate as a virtual assistant, it can also operate on its own. It can open a web browser, type in a URL, log on with supplied credentials and extract the required information from it to input into a document or spreadsheet.Â
What’s more, it never takes any days off and can work on a 24/7 basis. Any repetitive task is ideally suited for RPA. By handing off these monotonous tasks to a bot, human workers can engage in more stimulating work that adds greater value to the organization. As transformational as RPA sounds, however, its capabilities pale in contrast to that of intelligent automation. Here are some of the key differences between the two.
Key Differences Between RPA and Intelligent Automation
Automation is not intelligence
One of the things that makes a task mundane is that it doesn’t require any real intelligence to perform it. Downloading invoices from a website or tabulating lab results in a hospital doesn’t require any real training. RPA is process-centric and is driven by rule-based processes. The workflows assigned to a bot are easily defined and rarely change. The bot is guided by scripted actions that tell it to do process B once process A is complete and so on. The software robot must be instructed or trained on how to do something and relies on structured data to complete its assignments.
While RPA simulates human work actions, it doesn’t simulate human intelligence. It can download data and transfer it to its desired location, but can’t interpret the data and draw any conclusion from it. It only operates under the rules that define its actions.
On the other hand, Intelligent automation operates more like a complete human because it can interpret data to make inferences and conclusions from it. For instance, Intelligent automation is being used in logistics today to streamline supply chains by identifying potential bottlenecks before they even happen. This predictive intelligence helps it get the right assets and resources in place ahead of time.
IA incorporates RPA
Intelligent automation is also referred to as Intelligent Process Automation (IPA). If this sounds more like RPA, there’s a good reason. IA utilizes multiple technologies to help it work intelligently, including RPA. Of course, as its name implies, IA utilizes artificial intelligence (AI) to help it simulate human intelligence. This helps it analyze data far faster than any human can. Thanks to advanced algorithms, it uses machine learning (ML) to help identify patterns in large volumes of data. Other incorporated technologies include computer vision tools such as optical character recognition (OCR) that convert scanned documents or photos into text. Natural language processing (NLP) is used to communicate with humans through a conversational interface, and process mining is used to diagnose business processes to improve upon them.Â
One example of how IA goes beyond the rules-based limitations of RPA is the advancements in chat boxes. Most online users are familiar with these tools now as companies use them to interface with customers. If you’ve ever become frustrated by a chatbot, it’s probably because you needed some information or action it wasn’t programmed for. While RPA chatbots are great at directing you to a specific web page, getting you to the right human support specialist or answering frequently asked questions with template answers, they can aggravate someone needing assistance at a deeper level. AI-driven chatbots can better interpret what a customer wants and can provide service at a more innate level.
The ability to operate in an unstructured environment
One of the fears people have on their first day of a new job is dealing with an issue that is outside of the box. Process-driven work is dependent on structure. If everything exists in an anticipated order, everything runs great. When an exception to the rule comes to fruition, things grind to a halt. A new employee dealing with an issue that isn’t covered in the employee manual must stop what they are doing and find a manager that can provide them with further guidance or take charge of the situation. RPA is not suited for exceptions, but IA can handle exceptions and self-manage themselves through unforeseen scenarios in which they weren’t scripted to conduct. Â
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Intelligent automation improves over time
One thing that makes us human is the ability to improve our level of performance over time. While an RPA can perform tasks faster and more efficiently than a human, it is incapable of learning how to improve upon itself. AI-driven bots can learn and adapt to data and events in real-time by making tiny adjustments, allowing them to adapt to changing environments. This ability to improve and adapt makes it far more human-like than RPA.
Conclusion
In the same way that the web has gone through multiple generations, IA can be considered a more advanced generation of RPA. In the same way, Web 2.0 and Web 3.0 signified evolutionary steps that created advancements in how users utilized the web, AI gives organizations the ability to achieve bigger and better things than RPA was ever designed to. In the same way that there are employee positions that a first-time employee can easily fill, many human tasks remain ideally suited for rules-based RPA solutions. For those instances that require a deeper level of understanding or the ability to color outside of the lines, IA is the next evolutionary step.Â
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