Robotic Process Automation (RPA) is one of the most interesting and exciting developments over the past few years and has been a trigger to a huge number of innovative ideas across many industries and organizations.
For any organization, the key driving factors for success are its top-line growth, improved margins, controlled risks, and increased customer satisfaction. RPA, which primarily focuses on reducing or eliminating repetitive and redundant activities, is a definite boon for organizations that are pressurized by thin margins.
While Robotic Process Automation (RPA) is a huge success and advancing rapidly, a shift from the existing style of doing business is bound to change. This will very soon lead to a stage where the business will look out for more intelligent and transformational solutions than pure scripted Robotic Process Automation.
While the market is hot and organizations are addressing the low-hanging fruits, it will become very critical to introduce learning in every action of the RPA. The industry is soon picking on the term of Cognitive Process Automation (CPA) that is an integration of RPA, Natural Language Processing (NLP), Machine Learning (ML) and Data Analytics. Designing RPA with active learning points for future cognitive integration is the key to businesses going forward!
One point to be noted is that a bot can be considered the agent to deliver the processed output, but what is more important is the underlying cognitive platform that ensures that the required data is collated and processed, and hence, feeds the learning back for enhanced processing.
Today, 80% of data is unstructured and that implies that this data cannot be taken directly to apply any algorithm or processing. What will be needed is a mechanism to work similar to a human brain by knowing how to extract the right data and map them in the right way to make some sense out of that.
For this to happen, the SME or domain expert is extremely critical to train the system on what to look for and constantly learn and evolve.
Organizations will realize benefits by making their existing or new processes cognitive, implying that the processes should have access to functions and platforms that perform the required cognitive actions and consume them appropriately. Bots can be one mode to enable processes to be cognitive and efficient.
While we have been seeing RPA being more strongly accepted and adopted in BPO segments, we definitely foresee its play in the product engineering lifecycle too.
For those contemplating on which areas are best suited for RPA and CPA in a product engineering lifecycle, the first step is to identify key bottlenecks in terms of time or skills, and identify right processes that can drive cost savings as well as leverage optimization opportunities.