Towards Hybrid Automation by Bootstrapping Conversational Interfaces for IT Operation Tasks

Abstract

Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where robots perform some activities and humans serve the others. In the context of knowledge-intensive processes such as IT operations, implementing hybrid automation is a natural choice where robots can perform certain mundane functions, with humans taking over the decision of when and which IT systems need to act. Recently, ChatOps, which refers to conversation-driven collaboration for IT operations, has rapidly accelerated efficiency by providing a cross-organization and cross-domain platform to resolve and manage issues as soon as possible. Hence, providing a natural language interface to robots is a logical progression to enable collaboration between humans and robots. Developers can use several ChatOps frameworks to build conversational interfaces for robots, but it requires significant development effort. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with robots executing automation scripts. We further detail our process of mining the conversations between humans and robots to monitor performance and identify the scope for improvement in service quality. Finally, we demonstrate our deployed solution that creates robots for a ChatOps environment enabling hybrid collaboration. The robots identify the intents of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request.