time takes the form of either redeployment within their unit or redeployment outside of their unit or sometimes a combination of the two (Lacity and Willcocks 2018). Having a plan in place of what workers will do with the time gained from implementing RPA is a key consideration for any company thinking about using automation. There may be some situations though where there isn't any work for the employee to fill their extra time with, or maybe their position can be entirely replaced by RPA. While employees could be laid off, it comes with repercussions about how layoffs in favor of automation impact your company's image as well as how internal stakeholders will react.
Interacting with RPA
Training the Bot
Once a task has been selected to be automated using RPA, the bot or bots must be trained to know how to do the task. A “bot” or “robot” in the context of RPA is the same as a unique software instance (Lacity and Willcocks 2016). RPA bots are not like AI and ML algorithms, i.e. they do not “learn” through iterations of large datasets in the same way that algorithms do. Instead, the bots must be given specific instructions to complete their task. Creating these instructions in a way that the bot can understand and follow is referred to as training, which is also what the same process is called in the field of AI. It is therefore important to remember that RPA training is not the same as AI training (Willcocks et al. 2015).
Various methods are used to configure a bot and making it carry out the intended process, such as screen recording, mapping out interactions on a GUI with process maps, or writing scripts (Willcocks et al. 2015). If programming instructions into the bot's code are required, technical knowledge is needed and will usually be done by an RPA provider with the necessary experience. On the other hand, recording screen activity and external inputs from a keyboard and mouse and then having the bot copy the actions in a way like recording a macro in Microsoft Excel are much simpler. It can be done by users with no technical coding knowledge, and many RPA providers have created easy tools for recording and replicating actions. The recording software of RPA provider UiPath even replicates your recorded actions into a flowchart that can be edited later using intuitive commands. In conclusion, depending on the skills and knowledge of the user and bot designer, training can be done with or without coding skills.
Next Evolution of RPA Training
Some RPA providers have started to bridge the gap between RPA and AI as they have designed ML algorithms to handle training. The software will observe a user performing a task over and over until it has enough data to “learn” how the task is done. However, the ML method is only employed to generate the set of instructions that the bot will follow. To achieve more widespread adoption, RPA and ML need to become “smarter.” The promise is that with the use of ML techniques, more complex and less defined tasks can be supported. Companies like Microsoft or Apple are already recording your frequently used activities and promoting their products, Power Automate and Shortcuts, respectively, as workflow automation tools. For now, the bot learns instructions and then will simply follow them. It will not continue to learn and improve as it works, like an AI or ML algorithm would. This method makes training much easier since the ML software that handles training can run in the background while the task is done normally by a human worker. The longer the software watches the actions and the more variants of the execution of the tasks that the bot sees, the better it will learn to do the task. All of this happens without any human interaction aside from turning on the recording software. Depending on the technological capabilities of the RPA adopter as well as the RPA provider and the nature of the task being automated, different approaches to training will be used. It is important for companies to carefully review and select a training method as the bot will only be as effective as its training allows.
Conclusion
RPA technology is a key example of the democratization that the Fourth Industrial Revolution is bringing to non‐IT professionals, which will be able to do more with their existing systems at a reasonable cost. The twenty‐first century should have been showing much more of what Peter Drucker called knowledge work where nonroutine, mental work is driving the economic prosperity and value creation of the global business world (Drucker 2012). Utilizing automation will be paramount to free up our knowledge work from menial, rote tasks and to help the logistics industry look to innovate in other areas. The use cases for RPA as representing lightweight IT are rising, as there are more IT proficient non‐IT professionals that would be enabled to quickly make cheap, customized applications for automating tasks in an “innovation arena” (Bygstad 2016). As logistics companies around the globe are struggling to identify initial pilots into the digitalization space, we recommend that you take a hard look at whether RPA might be that first project.
Key Takeaways
Workflow automation will threaten the offshoring of white‐collar jobs as it becomes easier to use and more affordable.
Internal communication regarding automation plans needs to be carefully considered and designed as the results of not informing staff about them can lead to unrest in the workplace. Training a bot can be as simple as pressing a button and having the software record the actions of a user at their computer.
Process mining and mapping are a critical step in identifying tasks that are ripe for automation and process improvement.
Consideration of using RPA should be driven by the number of tasks that you have that are repetitive, independent of human input, require no physical input, low subjectivity needs, and substantial in terms of the number of tasks being done by internal staff.
References
Business Intelligence: Third European Summer School, eBISS 2013, Dagstuhl Castle, Germany, July 7‐12, 2013, Tutorial Lectures
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