So that is an important part of the mission and we’re thinking about network design and architectures. It is really not even for the next three years. We are thinking about the next 20 and 50 years. Network investments take a long time, and we want to make those investments with economics in mind, but also very much ensuring the most reliable network offering.

Laurel: You mentioned artificial intelligence and machine learning in a previous answer. What are some ways that AT&T is using AI and ML, or thinking about deploying artificial intelligence?

Raj: Great question and also a very timely one. As a company, we have had researchers working on AI for many years. With the advent of a lot more compute power and a lot more finer grain data, the opportunity has really opened up with the last, I would say, five years. It does play a very significant role at AT&T. Again, we have approached AI in an evolutionary way on how we infuse it.

First, we think about AI as the engine, and the fuel is the data. It begins with how we want to collect data and learn from it. That’s where a lot of the machine learning capabilities come in. We have been investing in a lot of big data management capabilities over the past few years, ensuring that those are well exposed to our AI engines. Our chief data officer in particular has worked very hard to establish a democratized ecosystem for both the data and AI capability. There’s a step function here in complexity as the amount of data increases, particularly with 5G, and we get kind of finer grain visibility, and we have a lot more intelligent controls to then apply decisions. So, we’re taking those steps in that evolutionary way.

Internally we have many use cases, including how we can use AI for planning, functions, AI for design decisions, but also in real time to help our customers, as well as the network, under various scenarios to provide better efficiency, better customer experiences, detect security threats, the threat analytics, as well as how to use feedback loops to constantly optimize the network. So, a lot of use cases across the life cycle.

Laurel: I’m speaking of that focus on security, which is top of mind for most executives these days. But not only security, AI and automation also are playing that really important role for 5G functionality.  What other ways is that coming into play right now with the capabilities of 5G?

Raj: Again, this is very timely and a very active work area. Let me give you some context on how we are structured. In thinking about 5G, we think about it as day zero, day one, day two. Day zero is the planning activities and forecasting. I can see some natural ways where AI and machine learning can help you through your forecasting. There’s your day one, which is actually building and designing your network. You want to do the greatest efficiency. Again, the feedback loops and reinforce learning kind of helps you do that as well as use of deep learning technology to analyze maps and geospatial data, to determine where you want to have buried fiber optics and where you want to place a small cell versus a macro cell. So, there’s a lot of the building engineering where we rely heavily on AI, deep learning, and neural networks.

Then there’s a lifecycle, which we call day two. In that, there are opportunities, things like energy savings where we are trying to optimize the energy footprint of our equipment. Again, both a corporate priority, but also a societal priority on the carbon footprint. We see great opportunities for economics but also helping the planet.