| | June 20209CIORevieweconomic impact? How to assess data maturity? How to ensure a responsible use of AI? At our blog "data-speaks", we explain several of those questions in detail. Let us dive a little deeper in the specific opportunities for AI in the telecoms industry. Firstly, there are many applications to optimise the core business. Think about network planning optimisation, point of sale optimisation, churn reduction, intelligent pricing, device recommendation, B2B sales optimisation, and so on. There are literally dozens of possible use cases, which together can generate hundreds of millions of value if applied at scale across the organisation. Secondly, AI and data can be used to improve the interaction with the customers. Think about chatbots that not only answer generic questions, but have access to personal customer data to attend to each customer in a personalised way, and allow for multiple types of interaction such as text, speech, and point & click. Current Natural Language Processing and Machine Learning techniques are good enough to automate a significant amount of customer interactions. If you take into account that those interactions happen in many different places including websites, mobile apps, and social networks, call centres, and shops, then you can imagine the huge opportunity implied by implementing this technology at scale. This reverts in better, more consistent customer service, where AI takes care of the repetitive and boring interactions, while human agents can focus on the more complex and interesting interactions. But it also allows for significant cost savings through less outsourcing of customer relation services. The third opportunity is based on the differential value that mobile network data embodies. Every interaction with a telecoms network, be it from a person or an IoT sensor, generates a digital trail. By anonymising and aggregating all those data points in a privacy-respecting manner, this data can be turned into high-level insights about mobility patterns in cities or countries (how crowds move around) and about footfall (where crowds are at certain times). And those insights provide very relevant information for sectors such as transportation, tourism, retail, finance, and public administration. As an example, Telefónica's Data & AI Unit--LUCA--has performed over 400 of such projects with clients in the past three years; really a new business. However, apart from business value, the same insights can generate value for societies: Data & AI for Social Good. In several projects with international organizations such as UNICEF, FAO, IABD, UN Global Pulse, and World Bank, we have used those insights for predicting spread of pandemics, assessing the impact of natural disasters, improving air quality in large cities, and supporting governments in understanding forced migrations. Current Natural Language Processing and Machine Learning techniques are good enough to automate a significant amount of customer interactions
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