Since 2017, the ISG ENI has been focused on improving network operations by specifying the optimization of networks, monitoring, the classification, management and networked systems as well as applications. The enhancements come in the form of recommendations and/or commands; both are formulated based on using the accumulated experience from operating the network in conjunction with manual decisions as necessary. ENI is defining an experiential architecture (i.e., an architecture that allows the learning and reasoning from observing and acting on a working system). This architecture also enables the use of artificial intelligence (AI) in network operation and optimization, focusing on real-time service management that is augmented and enhanced by offline learning and reasoning. The key innovation in this experiential architecture is the gathering of data from various network implementations, normalizing these data, processing it with AL functions, and delivering the results using policies. The actions and/or recommendations are passed to the assisted network of new, migrating, and/or legacy equipment. The ENI architecture is based on context-aware, metadata-driven policies, which can make actionable decisions based on situational awareness. In this context, the unique added value of the ISG ENI approach is to also quantify the Operator Experience by introducing appropriate evaluation and measures with procedures to optimize and adjust the Operator Experience over time by taking advantage of cognitive methods (e.g., the above-mentioned AI mechanisms).
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