RPA is best fit for repetitive transactional processes with a high transaction volume. This enables a simple straight forward use of RPA with little risk and high economy of scale. Automating exceptions, for example the handling of special high-risk claims, do in general not pay off since they may only happen a few times per year and may in each case require adjustments to the RPA bot.
A process benefitting from RPA requires digital input and output of data, for example the creation of a spreadsheets with customer data from one system that needs to be entered into another application. A business process with lots of manual analogue working steps benefits less from RPA. It is also helpful if the data to be handled is straight forward text or numeric data. Complex images are more difficult to deal with – although the continuing improvement of Optical Character Recognition (OCR) technologies can help here.
If several application systems that support a process are not integrated through APIs, this could be another opportunity for creating value through the use of RPA. RPA can provide the required integration. In this situation it is helpful if the technical system access is easy, for example it simplifies the RPA application if there is no need to address security software of cloud-based applications. In general, the change rate of the systems RPA has to access should be low to avoid frequent RPA configuration adjustments.
Some of the most important technical criteria, to verify the fit of RPA as an improvement approach, are summarised in figure 7.
Figure 7: Selected criteria to verify the fit of RPA as improvement approach
The decisions in RPA-enabled processes should be based on clearly defined rules that can be automated. Judgement-based decisions are much more difficult to handle through RPA. However, Artificial Intelligence (AI) capabilities can increasingly help here. Hence, this option must be checked on a case by case basis.
In general RPA is a good fit for stable, well defined processes with as few exceptions as possible. Frequently adjusting processes or even emerging processes normally require significant RPA management effort that may be prohibitive for its use.
If a high impact low maturity process is a candidate for RPA according to these technical criteria, it has to be determined now whether the RPA effects support the value-drivers related to these processes. Only if RPA improves the process in regard to these value-drivers is it worth launching an RPA implementation. The result is a set of processes suited for improvement through RPA deployments that can be used to define appropriate projects and project portfolios (Kirchmer, Franz, Gusain, 2018).
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