In Business Model, Internet Access, Mobile

Artificial intelligence might prove to be a very-important driver of incremental revenue growth for mobile operators in the 5G era, for reasons partly enabled by the advent of 5G. So here’s the logic: AI enables many new use cases and apps, and changes business models and revenue streams.

AI is computational intense. For many use cases, there will literally not be time (latency will matter) to base the apps on use of traditional cloud computing centers. Instead, processing will have to be done “at the edge.”

Edge computing, in turn, will create new value for locations scattered around the network that can do the processing, and mobile operators have some advantages (real estate, power sources, high bandwidth connectivity, low-latency networks, incentives to grow a role in edge computing and applications requiring edge computing) that could be leveraged to create a role in the new business.

That is, in part, why 5G networks will feature high bandwidth and low latency, but also might require use of small cell architectures that put many new potential nodes out in the network.

So, though it is not always obvious, AI could enable new sources of value, business models and revenue for mobile operators.

It is fairly easy to see how artificial intelligence (AI) is a benefit for app and device suppliers. To use the obvious examples, voice interfaces and customization of content are applied examples of AI. And though AI enables features, not necessarily full business models, the issue is whether, as mobile operators attempt to move “up the stack,” AI can help, and if so, how?

According to Gartner analysts, there will be many practical applications for AI, in the near future, though most do not immediately and obviously have a “mobile” underpinning.

By 2018, for example, up to 20 percent of business content will be authored by machines.The obvious examples are structured content such as shareholder reports, legal documents, market reports, press releases, articles and white papers all are candidates for automated writing tools.

Likewise, financial services will undoubtedly move early to use AI to support investing, trading and forecast operations. Banking and insurance likewise will likely be early adopters.

Still, there are a few areas noted by Gartner that seem to have significant and more direct implications for mobile scenarios, and possibly, therefore, for opportunities to move “up the stack.”

Sensors and other devices themselves will begin generating huge numbers of “customer service” requests. According to Gartner, by 2018, six billion connected things will be requesting support. It is not clear how well horizontal services to support such requests can be created, but many of those requesting devices will use mobile and wireless connections.

Even as artificial intelligence is used to handle a growing number of human-initiated customer service requests, so we will have to develop ways of efficiently handling “machine” requests as well.

Also, by  2018, two million employees will be required to wear health and fitness tracking devices as a condition of employment, including first responders.

Employee safety is the issue. In addition to emergency responders, professional athletes, political leaders, airline pilots, industrial workers and remote field workers could also be required to use fitness trackers, and those devices will rely on mobile connections as a primary requirement.

By 2020, smart agents will support 40 percent of mobile interactions, Gartner also says. To be sure, it often will be the app providers and device suppliers that directly provide those capabilities. The point is that virtual assistants routinely will monitor user content and behavior in conjunction with AI-based inference engines that will draw inferences about people, content and contexts.

The goal will be prediction. If the agents can learn what users want and need, they also can act autonomously to fulfill those needs.

So it is easier to see how mobile networks and service providers could use AI to support their own operations than to see how they could create horizontal platforms or vertical applications, beyond the autonomous vehicle, connected vehicle spaces or perhaps consumer health technology.


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