Applying AI for improving prediction and business outcomes will transform decision making in most job functions of telecommunication operators in the next decade. AI tools used in the right context will improve customer care, network operations and planning, fraud detection, and personalized marketing. The abundance of high-quality data, advances in computational processing, and sophisticated machine learning models available in the open source community is reducing both the cost and accuracy of applying AI compared to conventional hard coded methods. Attempting to deploy AI, even 5 years ago, was not economically feasible because of limited data sets, higher cost computing, and inferior ML models to conventional statistical regression techniques. In short, it was difficult to justify the economic benefits. That has all changed and the technology is actively being deployed in many sectors outside of telecom for natural language translation, facial recognition, advance driver assistance systems, and industrial automation. Multitudes of use cases The use cases are abundant from improving customer care to avoiding fraudulent transactions and this will drive investments. In the automation and assurance domain we expect to see a 20-fold improvement in isolating faults and service impacting events. The value of AI is that it uses data to discover […]