Most organizations have either started investing significantly in RPA or are planning to and according to a Gartner 2019 poll, around 80% of finance leaders have implemented RPA or are planning to. Unlike digital transformation or cloud migration, RPA is much easier, in terms of time & money, to adopt. With most of leading RPA platforms investing in cloud services, Robot-As-A-Service (RaaS) or Robot-for-Rent services will be widely available and adoption rates will increase significantly.
If you are already using RPA, you are at either step 1 or step 2. This article will focus on the next steps: Intelligent RPA, Self-learning RPA and Hyperautomation.
Traditionally, an RPA robot takes actions based on what it is programmed to do, but with the introduction intelligence layer, a Robot can gain the ability to think and make decisions. There are 3 levels of intelligence:
- Configuration Driven: Consists of rules that tells the robot how to execute at different circumstances. This is usually static data driven or rules engine driven. This is the most primitive form of intelligence.
- Ready-to-Use ML Model: Leveraging an existing pre-built or pre-configured ML model, most of you would have used these models outside of RPA implementation. For example, Vision AI by Google and Compute Vision by Azure for image processing or Google Translate and Microsoft Translator for language translation. A lot of these generic and popular models are available on Azure, AWS, Google Cloud, or IBM Watson platform, but industry specific or use case specific models are also available. So, make sure you spend some time searching before building one.
- Custom ML Model: Ready-to-Use ML model may provide you with a generic model that may be applicable to your non-core business function (Legal, HR, sales), but most likely, you will have to build a custom model for your core business functions. The model can be trained to your specific business function and data domains. You can leverage cloud-based platform (Azure ML or AWS SageMaker) or an open source platform (TensorFlow or H2O). Most of the leading RPA platforms have also incorporated ML capabilities to their ecosystem.
An intelligent RPA evolves to a self-learning RPA when you add the ability of learning. Machine learning works based on the confidence factor, which defines the probability of an event. Higher confidence means that the result has higher rate to be true positive or false negative and lower confidence means that the result has higher rate of false positive or true negative.
Through ML model training and testing, you can try to increase confidence, but once the model is in production and it experiences new data, in some cases, you may have lower confidence and the robot may fail to execute the operation. In this case, users will step in and execute the process manually. If you can collect the data on exceptions/failures or successes and you can take course correction steps by retraining the model, your intelligence will improve over time.
With CD4ML (Continuous Delivery for Machine Learning) discipline, you can bring continuous delivery principles and practices to the Machine Learning model. If your organization has adopted DevOps practices, this is not a very complex process.
According to a Gartner report, (Gartner Predicts 2020: RPA Renaissance Driven by Morphing Offerings and Zeal for Operational Excellence) by 2024, organizations will lower their operational costs by 30% from combining hyperautomation technologies with redesigned operational processes.
With self-learning RPA, the RPA has now gained intelligence and can learn, but what is next? The gap is the RPA is executing a process that it was coded for, and it does not change based on process improvements. In real world, applications get upgraded, new screens or functionalities are added and business process improves, but the RPA does not automatically upgrade the underlying process.
With process mining technologies, we can discover these changes and improve business processes by monitoring system events or user interactions. RPA can use this information to improvise the automated process or self-heal. RPA vendors, like UiPath with Task mining, has started providing these capabilities.
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