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"How to Incorporate AI in Telecom to Build an Intelligent Dispatch" by Sepideh Seifzadeh (IBM)

Facing its own path toward digital transformation, network communication companies such as Sprint have rose to the challenge to make sure the vision of 5G is a indeed a reliable one. Sprint started preparing their data for Artificial Intelligence (AI) with the goal of using machine learning algorithms to gain near real-time insights and increase responsiveness to customers.

The success of Sprint's digital transformation depends on the ability to quickly discover, organize and present the right data at the right time to those teams that make decisions that impact the customer journey. IBM Cloud Private for Data, a leading enterprise insight platform provided the right solution for Sprint, enabling AI projects in a shorter timeframe through unifying and simplifying four critical stages in the journey to AI: the collection, organization, analysis, and modeling of data.

Sprint currently uses IBM Netcool to collect and understand network alarms and events. Based upon specific alarms, trouble tickets and dispatches of people may occur. The dispatches often require equipment to be carried with the dispatched resources, where often the incorrect or less than optimal equipment and resources are sent, causing multiple truck rolls to resolve issues. In this work, the objective was to use Artificial Intelligent and train a Machine learning algorithm to predict the resolution of faults and alarms and, when necessary, to dispatch resources and make recommendations on the correct parts needed based on historical data.