The “virtuous circle” comprising RPA, machine learning and analytics was the central theme of this month’s BotVisions webinar series. Joining me were Kelly Coupe, Principal Product Manager, and Abhijit Kakhandiki, VP of Products, to share their insights into how business users are integrating the execution capabilities of RPA with the cognitive capabilities of machine learning to take the business benefits of automation to the next level.
While extremely good at executing specifically defined tasks, RPA tools are limited in the sense that they cannot adjust to new conditions or learn from experience. Machine learning, meanwhile, applies Artificial Intelligence (AI) capabilities to lend business context to the tasks executed by RPA systems, enabling the latter to make better decisions and be more productive. For example, RPA systems can effectively perform many tasks associated with loan origination or account management. However, they typically can’t determine if a person making an inquiry is who they say they are. By analyzing unstructured data (say, reviewing a scanned passport image and matching it against a customer’s account record), machine learning creates a connection between doing and thinking in an automated environment.
Other potential applications of RPA and machine learning synergy include insurance claims and customer service. For auto insurers, sending claims agents out to review fender benders is expensive and inefficient. Today, many are exploring the use of computer vision applications that incorporate AI capabilities to assess the context of how an accident happened to enable remote review and approval of minor claims. For customer service organizations deploying chat agents, AI tools use sentiment analysis to detect anger, dissatisfaction or sarcasm conveyed in the written word, allowing businesses to flag at-risk customers and escalate issues for proactive outreach.
Kelly and Abhijit also discussed how RPA and machine learning establishes a symbiotic link of continuous improvement between execution and analysis. The transactional data generated by RPA tools provides a steady stream of analytical fuel to drive AI capabilities forward and enable a deeper level of understanding. That deeper understanding, in turn, can be applied to expand RPA adoption even further. Emerging analytical tools, meanwhile, provide increasingly enhanced visibility and transparency into business events and data records.
In this context, we should rethink the common tendency to view RPA, AI and Analytics in a sequential manner or as comprising discrete activities. Rather, the process is fluid, where RPA deployments generate data to refine AI capabilities on an ongoing basis. Those capabilities are then applied to conduct ongoing and increasingly targeted, relevant and effective data analysis. The result is a wide range of new possibilities and enhanced business value in terms of cycle time reduction, scalability, innovation and ongoing productivity gains.