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СКАЧАТЬ informed on the RPA adoption process and how they will be affected by it, the change manager will be much more successful in leading employees through the change. Managing this change is vital since any stakeholder that hears about bots being used to automate parts of their tasks will likely become fearful that they are being replaced. This fear would be rooted in their lack of knowledge of what the role of RPA is. This is where a change manager can step in and reassure the employee that the bot is not an AI solution meant to replace them, but more of a tool that can eliminate repetitive, rules‐based tasks, thus allowing the employee to focus more on value‐added process. If this point is not communicated well, employees will become fearful of the technology and unwilling to learn about or undergo training to use it, thus affecting the change manager's ability to manage the change. Clear and open communication to stakeholders is critical during the RPA journey to ensure stakeholders that the solution is not meant to replace them (UiPath, n.d.).

      RPA Implementation Announcement

      Whenever the prospect of automating tasks is brought into a business, concerns will arise among employees that they could potentially be laid off after being replaced by automation. Any company hoping to adopt RPA will likely have to deal with these concerns. How this is handled is extremely important since employee onboarding is a necessity for RPA to be successful. Opus Capital is a venture capital firm that manages over $1 billion in assets and was able to successfully implement RPA to handle the processing of new employee relationships and changes in employee payment details (Hallikainen et al. 2018). As news of RPA implementation moved through the payroll department, concerns grew among the employees. One employee stated, “Yes, I had these thoughts that … a robot is coming here to sit down there and do the typing, and then I would lose my job” (Hallikainen et al. 2018). The supervisors of the pilot dealt with these concerns by emphasizing to the employees that these bots were not meant to be replacements. Their functionality is limited to repetitive tasks, and there is still a great need in the organization for human workers who are better equipped to deal with cognitive tasks due to their adaptability which the bots lack. The supervisors framed RPA adoption as a way to free up employees from repetitive tasks. A supervisor at Opus Capital was quoted saying “[The robot] will free time for other type of work [by humans] that a robot could not do … it will [therefore] bring a positive change to everyone's workload” (Hallikainen et al. 2018). In addition to making this clear early in the process, it is also important to try to get employees involved in learning about the technology so they can be advocates themselves.

The abovementioned is a best‐case scenario as there is always the possibility that some workers, whose jobs are entirely replaceable by the RPA bots, will no longer be needed. In a case study done by the KTH Institute of Technology, it was found that 13% of the clients of RPA providers laid off employees as a direct result of RPA implementation and another 13% offered retirement to some workers during the adoption process (Sandholm 2019). In addition to layoffs and retirement, 19% of these companies slowed their recruitment initiatives after adoption (Sandholm 2019). In an ideal world, RPA would not lead to any kind of reduction in full‐time employees, which also is typically the case. However, it is undeniable that for certain positions, RPA will lead to layoffs and repurposing of staff. Employee concerns thus seem somewhat justified, but it must be kept in mind that if an RPA bot can replace them, their job could be anyways outsourced to a BPO center.

      As a company goes through a digital transformation, assessment of staff is critical. Staff with the technical skill to work with RPA or staff with analytical, critical thinking, or creativity skills should be identified early in the process to avoid talent waste. It is advised that senior management and human resources align with RPA project leaders to identify the potential risks of the project and come up with the right messages for the workforce. Finding a balance of what information to share and when to share it could mean the difference in retaining key staff.

      Liberated Knowledge Workers

      Once RPA has been implemented and is running smoothly, employees will have been freed of some of their rote and repetitive work tasks. Employees will be able to concentrate their efforts on higher‐value tasks that require cognitive and interpretive skills like analysis, innovation, and problem‐solving (Slaby 2012). Usually, the rearrangement of employees' 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 СКАЧАТЬ