Make sure that all the basic and special functions you need also work with your IT environment. Pay particular attention to the following factors:
- Bots should be easy to set up. In addition, different options for setting up RPA bots for different personas are essential. A recorder should capture the normal actions of business users. Citizen developers should be able to use low-code environments to define bots and business rules. And pro devs should be able to create real automation code that accesses the RPA tool’s APIs.
- Low-code features are essential. Typically, low-code combines a drag-and-drop timeline with an action toolbox and property forms – occasionally a code snippet also needs to be created. This is significantly faster than creating business rules using conventional methods.
- The solution of choice should be both Attended as well as Unattended Bots support. Some bots are only useful to run on demand (attended) – for example when it comes to executing a precisely defined task. Others are suitable for reacting to certain events (unattended) – such as due diligence checks for submitted loan applications. You need both forms.
- Machine learning capabilities are mandatory. Just a few years ago, many RPA tools struggled to extract information from unstructured documents. Nowadays, ML learning functions are used to analyze such data. Some providers and analysts also refer to this as “hyperautomation”.
- The human factor needs space. Categorical machine learning models typically estimate the probability of possible outcomes. For example, a loan default prediction model that gives a 90 percent probability of default might recommend denying the loan, while a model that calculates a 5 percent probability of default might recommend granting it. There should be room for human judgment between these probabilities. The RPA tool of your choice should therefore offer the option for manual reviews.
- Bots have to deal with theirs Integrate enterprise apps – otherwise you won’t be able to get any information from it and will therefore be of little use. Integration is usually easier than parsing PDF files. Nevertheless, you need drivers, plugins and login data for all databases, accounting and HR systems as well as other company apps.
- Orchestration options are indispensable. Before you can run bots, you must configure them and provide the necessary credentials, usually through a dedicated secured credential store. Users must also be authorized to create and run bots.
- Cloud-Bots can bring additional benefits. When RPA was introduced, bots ran exclusively on users’ desktops or company servers. However, with the growth of the cloud, cloud virtual machines have become established for this purpose. Some RPA vendors have also already implemented cloud-native bots that run as cloud apps using cloud APIs, rather than on Windows, macOS, or Linux virtual machines. Even if you have little current investment in cloud applications, this feature is worth considering going forward.
- Process mining capabilities can reduce effort. The most time-consuming part of an RPA implementation is usually identifying processes that can be automated – and prioritizing them accordingly. The better the RPA solution you choose can support you with process mining and task discovery, the faster and easier you can automate.
- Scalability is the be-all and end-all. If you want to introduce RPA company-wide and gradually expand it, scaling problems can easily arise – especially when it comes to unattended bots. A cloud implementation often helps, especially if the orchestration component is able to deploy additional bots when needed.
The best RPA software solutions
Below we have compiled the most important providers and solutions in the field of robotic process automation for you. The list does not claim to be complete and is based, among other things, on the reviews of users and the assessments of analysts.
It should be noted that AI agents are increasingly overtaking classic RPA solutions because they enable more advanced, intelligent automation initiatives: While robotic process automation primarily works on a rule-based basis, AI agents “learn” from data. Various providers have already responded to the trend and realigned their automation offerings accordingly.
