In this section, we're going to look at the landscape for artificial intelligence within the communication service provider industry. What's really driving this is a huge deluge of data, data that's being generated by multiple sources, such as the consumers, the internet users, devices, and things-- coming from things like autonomous vehicles, connected airplanes driving huge amounts of data, industrial IoT type applications and services, such as smart factory, which again is driving vast amounts of operational data that needs to be transmitted and processed. And also on the cloud video side, generating huge amounts of video data that's being transmitted across the networks. And really there's great value in all this data, everything from deriving security insights, for example, identifying security threats before they happen, being able to protect and address these. Looking at the operational aspects of the data and its applicability to the business, such as reducing operational cost, increasing efficiency, increasing productivity, increasing quality. And then there's the business aspect of this data. What information you can derive around increasing profitability of service that's being delivered, but also looking at what potential new services can be delivered based upon the data that's being generated within the industry. So in terms of what is the impact on communications service providers around this, first of all, it's the transmission of the data, the connectivity parts that the communication service providers have a role in playing around this data. But secondly, in terms of the operational aspects of a communication service provider, consider a service provider that currently supports 10 million endpoints and 10,000 nodes. This could increase by five times over the next four to five years. And looking at this from an operational aspect, the number of events that are being generated, the number of items that need to be managed as part of these events being generated, this soon becomes a problem that cannot be solved manually. It requires some intervention from artificial intelligence and some managing of that data. So if we look how artificial intelligence has been transformative across multiple industries, the communication service provider has a great number of use cases here and applicability within this, everything from the cyber security side, everything to self-optimizing networks, to the location-based marketing type services. But beyond the communication service provider vertical, there's a lot of synergies with other types of use cases across multiple industries. If we look across here, you have consumer, the retail, the finance, for example. Use cases such as fraud management, cross-selling, upselling, marketing type use cases, as well as media-related use cases, media analytics related use cases. Again, deriving insights from the data within these sectors. And then if we look at where the whole industry is around artificial intelligence and the adoption, over 50% of enterprises are currently looking and investigating artificial intelligence as a tool to use within the business. But only really just over 10% of enterprises and companies are currently using artificial intelligence within their current operations. So this shows us a huge opportunity to start to look at strategy, start to look at data, start to look at use cases, how to use artificial intelligence within your business to drive those business insights, operational insights, and security insights. And if you look at where artificial intelligence has come from and is currently going to-- so today very much it's around the descriptive analytics, diagnostic analytics, initially really driving hindsight and information of actually what's gone on. Looking about why incidents have happened, deriving insights from those data. But really moving on, next stages is looking at the predictive analytics, prescriptive analytics, really moving towards that cognitive analytics. And within a comm service provider context, very much looking at the holy grail, which is a cognitive networking, how to make the autonomous network from an operational and planning perspective. And really looking at this from this automated watch, decide, act, learn cycle, driving and helping with the data ingestion coming to common datasets, being able to coordinate the configuration, control, and management of the network. Looking at network and resource utilization, making that optimized and efficient. And really everything down to what happens when an incident happens, looking at managing and investigating and understanding the blast zone around any incidents that may happen within the network.
