the food courts and restaurants that the user can benefit from. Such recommendations would improve the quality of experience of the user and thus also improve their loyalty to the service provider. Similarly, if a user regularly downloads a large amount of data every day, then there could be a recommendation for a plan that would allow a large data download at discounted rates. This would again result in savings on the side of the customer, which would help in keeping their trust and loyalty. Another example of AI/ML is understanding the network better. The network components generate a large number of logs and KPIs, and those contain a lot of information. This data can be analyzed to observe trends. For example, a certain region may see a peak in network traffic at some hours of the day; this could be analyzed by the AI engine and there could be a suggestion to divert some of this traffic to another region to share the load and ensure better performance. Another example of AI-enabled operations is an engine analyzing the data and latency of the whole network and automatically suggesting what the points of congestion are and what equipment is specifically causing it. It would be a more accurate suggestion as the engine would crunch data from hundreds of days to arrive at a conclusion, besides the fact that an automated procedure would not have an inherent technology bias or reluctance that a human might have when suggesting bad news. Thus we see that a mobile network, if truly monetized, would add to numerous pathways of generating revenue for a service provider, rather than the traditional data plan lead revenue. We have already started seeing the use of secure APIs to expose relevant information to third-party applications that consume mobile network-generated events and information to translate to business intelligence. In the upcoming years, as businesses modernize, this will be a very common practice. Summary Summary In this chapter, we have seen that automation plays a key role in the smooth operation of a 