In this article, we present an end-to-end automated helpdesk email ticket assignment system driven by high accuracy, coverage, business continuity, scalability, and optimal usage of computational resources. The primary objective of the system is to determine the problem mentioned in an incoming email ticket and then automatically dispatch it to an appropriate resolver group with high accuracy. While meeting this objective, it should also meet the objective of being able to operate at desired accuracy levels in the face of changing business needs by automatically adapting to the changes. The proposed system uses a system of classifiers with separate strategies for handling frequent and sparse resolver groups augmented with a semiautomatic rule engine and retraining strategies to ensure that it is accurate, robust, and adaptive to changing business needs. Our system has been deployed in the production of six major service providers in diverse service domains and currently assigns 100,000 emails per month, on an average, with an accuracy close to ninety percent and covering at least ninety percent of email tickets. This translates to achieving human-level accuracy and results in a net savings of more than 50,000 man-hours of effort per annum. To date, our deployed system has already served more than two million tickets in production.
Process automation has evolved from end-to-end automation of repetitive process branches to hybrid automation where bots perform some activities and humans serve other activities. 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 bots is a logical progression to enable collaboration between humans and bots. This work presents a no-code approach to provide a conversational interface that enables human workers to collaborate with bots executing automation scripts. The bots identify the intent of users' requests and automatically orchestrate one or more relevant automation tasks to serve the request. We further detail our process of mining the conversations between humans and bots to monitor performance and identify the scope for improvement in service quality.
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