4 Use Cases for AI in the Telecom Industry

AI can help network operators improve the user experience, optimize their operations and drive new revenue

Brenden Rawle
4 Use Cases for AI in the Telecom Industry

In the last year, the hype we’ve seen surrounding large language models and AI in general has been relentless. It started with the record-breaking release of ChatGPT, which introduced millions of people to the power of generative AI for the first time. Since then, we’ve seen the launch of a new enterprise version of ChatGPT as well as new AI toolsets from familiar names like Microsoft and Google.

Enterprises are scrambling to take advantage of these solutions, and for good reason: It’s clear that the companies that adopt AI first and start using it to its full potential will have a competitive advantage in the years to come. That said, recognizing the value that AI offers and capturing that value are two different things altogether.

What makes AI for the telecom industry so challenging?

In the telecom industry, many organizations realize that the opportunities offered by AI are exceeded only by the challenges they’ll face along the way. For example, success with AI requires telcos to collect the comprehensive data sets they need (including data shared by external partners), move that data to the right locations without delay, process the data quickly to ensure timely and accurate results, and then act upon the insights to drive business value.

They need to do all this while also keeping an eye on costs and their sustainability metrics. And even after their AI models start delivering results, they need to do it all over again on a continuous basis to ensure their models remain accurate over time.

It’s a tall order, and it’s no wonder so many network service providers (NSPs) feel they have neither the infrastructure nor the internal expertise to do it. From an infrastructure perspective, AI is best done in a distributed manner. The entire AI workflow depends on an iterative process of model training and model inference, with those workloads having different infrastructure requirements. Specifically:

  • Model inference workloads are more latency sensitive, so they’re best hosted at the digital edge.
  • Model training is more resource intensive, so it’s better suited for a core data center or the public cloud.

The need to run different AI workloads in different locations could be challenging for NSPs. Building on a distributed digital infrastructure platform like Platform Equinix®—which offers a global colocation footprint, digital infrastructure services that customers can deploy at software speed and dense ecosystems of partners and service providers—could help NSPs overcome complexity and pursue AI use cases to their full potential.

What makes AI a good fit for the telecom industry?

To put it simply, so many telcos are interested in AI because the opportunity to transform themselves is so vast. According to a recent Frost & Sullivan report, AI is expected to become the core technology in telecommunications services:

“AI technologies provide opportunities to transform telecom services and create significant business value.”[1]

The report also found that telcos see improved customer experience and optimized network operations as the top two benefits of integrating AI into their operations—named by 71% and 63% of surveyed telcos respectively.

As they look to overcome AI challenges and achieve these benefits, telcos may be better prepared to adapt than they realize. Complex and far-reaching operational models have been commonplace in the industry for years now, and NSPs can easily apply the lessons learned from building those models to assist them in their future AI efforts.

For example, NSPs that have built 5G networks in recent years will likely see significant overlap between 5G and AI workloads: They both require managing infrastructure on a massive scale, with many different endpoints across many edge locations.

Telcos that have learned how to manage complex combinations of services and make the most of automation now see AI as a natural extension of the things they’re already doing. As a result, they’re using AI to pursue use cases such as:

  1. Predictive maintenance
  2. Traffic flow optimization
  3. Network architecture optimization
  4. Identifying new revenue opportunities

Predictive maintenance

NSPs must ensure an exceptional user experience for their enterprise customers. These customers depend heavily on their network services, and they need them to work whenever and wherever. To achieve this, NSPs can apply AI-driven insights to identify anomalies and schedule maintenance to prevent outages before they occur. Many NSPs have already begun using predictive AI models to better maintain the networking equipment itself and the underlying infrastructure that supports that equipment.

The logical endpoint of predictive maintenance for telco is self-healing networks. These advanced networks can keep themselves online by identifying and remediating issues with no human intervention. The idea of self-healing networks is not new, but we’ve now reached the point where NSPs have access to the capabilities needed to pursue self-healing networks at scale. The integration of predictive AI models with automation and software-defined networking capabilities is one important piece of the puzzle that has only fallen into place recently.

Traffic flow optimization

NSPs have been applying automation capabilities to balance and reroute traffic for some time now. By adding AI capabilities, they can optimize their traffic routing even further. AI tools can analyze the flow of traffic over time, giving NSPs the insights they need to optimize their routing and capacity management strategy. Once again, this is all about improving the experience for customers and end users: AI-powered networks can better detect and respond to unexpected spikes in traffic, adding temporary capacity to prevent delays that degrade the user experience.

NSPs can also use AI capabilities to manage network components intelligently during periods of less-than-expected traffic. This could be particularly useful in the mobile space, where the number of users served by a particular radio access network (RAN) can vary greatly over time. NSPs can use AI to program these RANs to enter low-power mode or even turn off altogether when they aren’t needed, thus enabling a more efficient 5G network.

Network architecture optimization

Today’s NSPs recognize that the network architectures that served them well in the past won’t necessarily be a good fit for the current business landscape. They need new ways to design, build and manage their networks—both fixed and mobile—to support the latest digital applications and the people who use them.

One example of this is digital twins. Using AI and digital twins together, NSPs can get a very detailed and accurate look at how their networks would perform in various real-life scenarios. This can help them make informed decisions about where they should position network components and how they should manage them to get the best results. As demand for 5G use cases like gaming and smart cities continues to increase, NSPs will be able to expand their networks with confidence to better support those applications.

Identifying new revenue opportunities

The idea of using AI models to better understand what customers want—and how much they’d be willing to pay for it—isn’t unique to the telco industry. However, the industry does have some especially promising opportunities in this regard. For instance, NSPs can analyze usage patterns to get granular insights about how customers use their networks and why.

They can then use these insights to better meet the expectations of those customers with more tailored, specific services. This could include network slicing, where the operator offers different classes of service for different users. As a result, NSPs can help many different customers meet their exact needs—around latency, reliability, capacity, security and more—while using the same physical network infrastructure for all customers. If customers are willing to pay more for a higher class of service, network slicing makes it possible to give them what they want.

Platform Equinix is the ideal place for telcos to deploy AI

Regardless of why NSPs use AI technology, it’s where they do it that could make all the difference. Specifically, they need to run their distributed AI workloads on a hybrid multicloud architecture that includes diverse public cloud environments, colocation services and on-premises data centers in different locations. In addition, NSPs need to work with a digital infrastructure partner that can help extend their capabilities to the cloud, allowing them to tap into the cloud services they need, when they need them, while also maintaining flexibility and keeping costs low.

Platform Equinix can help NSPs get everything they need from their hybrid multicloud environment. This includes low-latency on-ramps to multiple clouds in key metros around the world and a global colocation footprint that makes it easy to create a presence at the digital edge—wherever that might be. In addition, our digital infrastructure services can help NSPs:

  • Connect with ecosystem partners at software speed
  • Deploy bare metal compute and storage capacity on demand
  • Access virtual network services to reduce the cost and complexity of deploying at the edge

In addition, Platform Equinix is home to a dense telecom ecosystem, including more than 2,000 network service providers. Being able to interconnect quickly with clouds, business partners and their fellow network operators is one reason that top NSPs like DISH are partnering with Equinix. It allows them to scale their infrastructure and prepare to capitalize on emerging technologies.

Equinix can support private AI models

Digital businesses around the world have recognized that the public cloud isn’t right for all their needs, and telcos are no exception. Many of them are concerned about the privacy, data protection and compliance challenges they could create for themselves if they rely too heavily on public cloud services for their AI workloads.

Instead, many of them will adopt a private AI strategy, which could involve building out private services using a DIY approach and/or working with AI Model as a Service providers to access the models they need. Regardless of what their approach to private AI looks like, Platform Equinix can provide the infrastructure services and ecosystem access needed to do it right.

Learn more about how Equinix can help NSPs

AI is just one example of how today’s NSPs are working to modernize their infrastructure and provide the performance and flexibility their end users expect. Read the Equinix white paper Create Digital Advantage at Software Speed to learn how we help technology and service providers innovate quickly and set themselves up to thrive in the rapidly changing digital landscape.


[1] Global Growth Opportunities for Telecommunications Service Providers in Artificial Intelligence (AI), Frost & Sullivan, April 2023.

Subscribe to the Equinix Blog