Automation has become one of the core pillars of digital transformation. Every discussion about automation will, at one point, include Robotic Process Automation (RPA) and Intelligent automation (IA), leaving many stakeholders confused if they don’t know the difference.
Questions about capabilities, differences, similarities, and use cases often arise and remain unanswered. We wanted to bring clarity to this topic and answer some of the biggest questions surrounding the two. We believe that gaining a clear understanding of automation technologies is the first step towards any successful automation project.
Both RPA and, more recently, IA are technologies that have seen an accelerated adoption rate, due to their proven capabilities. They are also both used to reduce operational costs, enhance customer satisfaction, and increase overall performance across functions. In their own way, RPA and IA are reshaping modern work and our entire perception of it. Many compare the effects of automation to those of the industrial revolution, and by looking at how much things have changed in business over the past ten years, it is a fair comparison.
The Story of RPA
RPA emerged in the early 2000s, and although its entrance was not meteoric at the time, it has since been reshaping operations and workforce. RPA is the software equivalent of physical, industrial robots that are programmed to perform repetitive tasks humans used to do decades ago.
Similarly, digital workers manage high-volume workloads based on input from human employees. Most modern business operations involve a certain degree of bulk, monotonous activities that are vital. Emails, spreadsheets, and databases are like a digital glue that helps distinct functions work as a whole. Much like the repetitive tasks on an assembly line, office work is dependent on repetitive actions like:
Opening emails and downloading attachments
Collecting and moving data
Copying/pasting data across multiple spreadsheets
Logging in to different internal apps or websites
Sending emails
In an RPA-enhanced environment, digital workers are integrated into the organization’s virtual space, secured, and controlled by their internal IT department. It takes very few resources for a digital worker to start delivering value immediately. Once a business user trains it, it will operate independently, 24/7, mirroring the business user’s actions exactly and interacting with the tools and apps required to complete the work. A basic example of work that is often delegated to digital workers is copying hundreds of rows from a spreadsheet and pasting the information onto a different document. It would take even experienced employee hours, if not days, to complete the task. A digital worker, trained by the same employee, can do the same work in a quarter of the time or less, completely error-free. The business value of having such capabilities is enormous. Many companies are presently leveraging RPA to free their employees from manual repetitive tasks. This not only helps them reach their higher potential, but it also brings more value for the organizations, as people are redirected to more relevant tasks that require innovation and creativity and support employee satisfaction.
Digital workers can operate attended or unattended. In the earlier days of RPA, integrating and managing digital workers in an organizational environment required input from external automation experts. In recent years, however, no-code platforms like Atomatik have given regular business users the ability to set up and schedule digital workers on their own, without needing programming skills. Using an intuitive, user-friendly interface, any process owner can manage their own team of digital workers.
While RPA can only function with structured data, its use has massive benefits for data integrity and accuracy, as it eliminates the risk of human error from repetitive processes.
The story of IA
Intelligent Automation is a more recent technology that incorporates an eco-system of technologies, which includes RPA. If we were to compare automation to a body, RPA would be the muscles doing the heavy lifting, while IA would be its eyes and brain. IA employs technologies like Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), and Natural Language Processing (NLP). Intelligent Automation has cognitive abilities that enable it to adjust to unexpected situations and solve problems as they arise.
IA builds on the strength of RPA and the other incorporated technologies, allowing business users to delegate a broader range of tasks and processes containing unstructured data. IA not only simulates human interactions but human intelligence overall. Like humans, IA evolves over time, getting better as it works more.
IA versus RPA
The difference between IA and RPA is that between brains and brawn, albeit this is an oversimplification. RPA is process-centered and can only work by rules given by a human. These rules rarely change, which makes RPA ideal for large-volume workloads based on structured data.
IA can take things further. It can interpret the data collected by RPA, much like a human would. An example of an IA use case could be a planning supply chain, where IA’s predictive capabilities can identify and prevent potential bottlenecks before they happen.
Which is better and for what?
The answer will of course vary depending on each organization. RPA is extremely versatile, industry-agnostic, and well-suited for automating a wide variety of processes. Any manual-intensive office workload can be delegated to digital workers with immediate value and results. If processes are well-defined and optimized, RPA can relieve business users of time-consuming activities, adding to the quality of their work and satisfaction.
One thing that makes us human is the ability to improve our level of performance over time. While RPA can perform tasks faster and more efficiently than a human, it is incapable of learning how to improve upon itself. AI-driven digital workers 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
Technology of any kind should be used to make people’s lives easier. Rather than excluding each other, RPA and IA are complementary. However, regardless of which of them an organization adopts, it should do so on a strong governance structure, following consultations with automation experts. Simply stacking one technology on top of another can backfire, causing confusion among employees, instead of creating a solid, future-proof organization.
Did this article help in answering your biggest questions about Intelligent Automation and RPA? If you would like to learn more about the potential uses of automation for your organization, book a call with our team of experts.
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