When we talk about hyper-automation, we first need to talk about Robotic Process Automation (RPA). RPA involves robots replacing humans in order to perform repetitive tasks; here, the term “robots” refers to software robots, not those performing physical actions. Accordingly, RPA involves repetitive and simple office work being executed on a computer (workstation) rather than repetitive work in a physical environment. RPA involves applying automation to replace the work of the back office which supports production tasks as priority work. The purpose of the automation is to provide a level of speed and accuracy that would not be possible with humans in terms of productivity by performing simple tasks quickly and accurately under human command. However, with the advances achieved in the digital era, the level of automation expected by humans did not stop at the level of just performing simple tasks. The realization of such expectations can be seen as a ripple effect of artificial intelligence or AI.
Some have theorized that the emergence of hyper-automation has been influenced by COVID-19. However, it would be more accurate to say that the pandemic accelerated the development of hyper-automation, rather than wielding an actual influence. The concept of hyper-automation was well established even before the pandemic. By combining it with digitization, it goes beyond the “simple automation” expected from RPA. Together with AI and machine learning (ML), hyper-animation allows people to focus on more creative tasks and to hand over the less creative ones to robots. It even allows robots to make decisions within a certain range.
Hyper-automation requires other components in addition to AI and ML. To automate a process, it is first necessary to analyze the patterns of repetitive work. To this end, sophisticated analytics technology is used to analyze accumulated data, discover patterns, and verify rules. Furthermore, Intelligent Business Process Management Software (iBPMS), which focuses on the overall process rather than particular tasks, has a wider concept of automation that extends beyond RPA. In addition, there are systems that further support hyper-automation such as Low Code/No Code (LCNC) and Integration Platform as a Service (iPaaS).
Gartner cited hyper-automation as one of the top ten strategic technology trends for 2020, 2021, and 2022. Even though it was not included in Gartner’s list for 2023, it remains a major point of focus after being selected as a key technology trend for three consecutive years.
As already mentioned, together with RPA, hyper-automation involves AI, ML, and diverse technologies for optimization. Depending on the combination of these technologies, they can be used in accordance with the goals and needs of the user and can also provide scalability and flexibility. Through the independent use of the selected combination, efficiency can also be enjoyed from the perspective of the user and the hyper-automation that provides it. In other words, by utilizing reduced resources selectively from an operating perspective to fulfill needs, systems can be set up to perform faster than when using many resources, ultimately enhancing productivity.
Above all, as hyper-automation is founded on digital-based automation, users can easily integrate digital technologies with assets. They will be able not only to share related data but also facilitate communications within or between departments. This type of communication directly leads to efficiency, and here, resource utilization can be optimized, and profits improved through the elimination of human error, the use of analytics technology, and intelligent forecasting.
As with RPA, the use of hyper-automation also involves digitalization. It is possible to apply automation to not only information input but also to monitoring, management, and feedback, providing an automation system that can be applied effectively to logistics. When we look back on when logistics operations involved manpower and manually written records, we can appreciate the tremendous extent of support currently provided by robots. One of the many factors to be considered when securing competitiveness in the logistics industry is inventory management. Automation realizes efficiency in terms of time consumption in the areas of purchasing, maintenance, and updating. It also greatly reduces the number of mistakes that usually occur in manual work and enables a speedy response by readily sharing information with partners. Automation can also provide specific help with the process of receiving and fulfilling orders, organizing and shipping completed orders, and setting up overall plans. In particular, tracking the condition and geographic location of products so that customers can be aware of their status is probably one of the biggest advantages gained through automation in the field of logistics. These elements aid both companies and customers in many aspects, including time prediction, customer taste, and resource efficiency, and these data are expected to create additional benefits in the future in the form of reporting and feedback. These are only some of the effects that can be obtained by automating documents that were typically used in digitization, and through this digital environment hyper-automation is being accelerated.
RPA has been introduced and proven effective in the financial, insurance, and large manufacturing industries. Hyper-automation is also being applied in a diverse range of fields due to its scalability and as an extension of the current trend. The benefit of using robots in the traditionally paperwork-heavy financial sector is highly evident. The loan approval process and some areas of accounting work greatly benefit from this technology. The manufacturing industry has endeavored to adopt a scientific approach to consistently improve productivity. By introducing hyper-automation, data generated from the production line is collected and analyzed, applied directly back to the line, and new or updated data are collected again to analyze trends. Through the repetition of these routines, continuous optimization is realized through learning and modification.
Recently, many businesses have been introducing chatbots, which are conversational AI, in their customer service. In the case of a pharmaceutical company, the capacity of their call center was inadequate to handle the many customer inquiries that were coming in due to the rise in demand for their products. As a result, the cost of handling such calls increased. They needed a way to answer the inquiries quickly and accurately, and they resolved this issue through their launch of a “virtual assistant.” Not only did it respond to customers, but it also provided explanations related to medical products and even acted as a sales representative. The virtual assistant was programmed not only to respond to inquiries in real time, but also to collect necessary data, from inventory management to sales performance. In addition, it provided real-time reports so that the company could be aware of every situation and create concrete strategic plans.
Tasks such as expense processing are universal to all industries. Except for certain factors that are specific to certain industries, the overall process can be described as very similar for all sectors. Some parts of this process can be automated using RPA, OCR, Chatbot, and ML technologies. After uploading a quote or an approval request, the data can be extracted using RPA/OCR, and a slip is created through a verification process. Compliance with regulations and authenticity of documents are verified through ML, and the decision as to whether it can be approved or if human intervention is required is made and followed up on by the robot.
Since the advent of AI, many aspects that seemed only possible in our imagination are now starting to emerge as reality. I remember when the whole world watched AlphaGo beat Lee Se-dol back in 2016, and many researchers immediately opened their laboratory doors and ran outside in excitement. Afterwards, the furor died down as the technology spread rapidly, but as new technologies are acknowledged and brought to the surface, I believe we are at a time when they have become a point of interest for all of humanity. Now, these technologies serve as our personal assistants, and help us cope with the fatigue we inevitably experience as humans. In the face of criticism that machines lack creativity, it appears that they are displaying their abilities to the fullest within their limitations, just as humans are sharing their intelligence with them. The Digital Transformation (DX) is ongoing and accordingly the hyper-automation that is based on it is evolving.
By Professor Chae Jun-jae, Air Transportation and Logistics, Korea Aerospace University
2023.04.18