Organizations around the world have been using automation more and more over the last several years. Increasing pressure on bottom lines, rising competition, and evolving customer behaviors are driving increased adoption of Intelligent Automation (IA) among enterprises. IA is an ecosystem of next-generation technologies – such as Robotic Process Automation (RPA), Machine Learning (ML), computer vision, Natural Language Processing (NLP), process mining, and orchestration – that automates both transaction-/rules-based and judgment-intensive tasks.
Enterprises that lack strong automation vision and only leverage IA for a limited number of business processes are generally missing many of the wide-ranging benefits it offers. Those that have achieved significant success with IA have scaled the solution to multiple business functions, looking beyond rules-based automation to automate judgment-intensive tasks.
To become future-ready and the reap maximum benefits from IA, enterprises should transform their business functions from end to end with the help of IA technologies instead of modernizing in silos. A transformed business function that leverages both digital workers and human agents makes processes more efficient, reduces turnaround time, and enhances customer and employee experience. However, scaling automation across several business functions has its own set of challenges.
In this research, we explore the best practices mature organizations follow and have successfully employed to overcome challenges in scaling automation.