AI in Telecom: Top Strategies and Use Cases

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In recent years, telecommunications firms have been under unrelenting pressure, tasked with scaling to support increasing demand for wireless and internet connectivity, while ensuring optimized service levels and contending with cost pressures, particularly for traditional voice and SMS services. The move to deliver 5G, and innovative, differentiated services that capitalize on these networks, represented vital competitive mandates.

In the wake of the COVID-19 pandemic, all these pressures only intensified. Within most telecommunications firms, this meant that multi-year digital transformation initiatives had to be delivered in months. As telecommunications leaders seek to adapt to these realities, (AI) is emerging as a strategic asset. Through AI, teams can set the stage, not just to weather current market demands, but be positioned to thrive in the long term.

In this post we look at the importance of AI in telecom, and specifically how data science teams in the telecom sector can leverage AI to transform their organizations.

IT

Within telecom organizations, IT teams are tasked with modernizing the infrastructure and ensuring high levels of service delivery. Data science teams can play a big role in equipping IT to perform these functions.

With the help of AI, IT teams can be well-informed about patterns and forecasts. For example, they can forecast the expected spike in website traffic due to an upcoming promotional campaign. This is useful to plan resources and allocate budget according to precise consumption patterns.

When securing IT systems, AI can be used to spot anomalies in network traffic. AIOps solutions of today have these kinds of capabilities built into their platforms natively. This can equip IT teams with the ability to preempt attacks and take remedial action ahead of time.

Looking ahead, AI can enable IT teams to take automatic action on telecom infrastructure. This includes running automated maintenance on hardware like towers, data centers, and edge infrastructure. The goal is to have self-healing hardware that can automatically perform root cause analysis and resolve issues faster. IT teams place a lot of value on the ability to predict outages or network latencies. AI will play a key role in helping IT build this capability into telecom infrastructure.

Product Development

With the advent of 5G, telecom providers are on the verge of reinventing their entire line of product and service offerings. The explosion of connected devices and machines has just begun. Self-driving cars, smart cities, smart homes, and connected medical devices present a huge opportunity for the telecom sector. AI can play a key role in guiding product development through these uncharted waters.

Network slicing is one example of how AI can help operators offer dynamic services that are in line with customer needs. Network slicing allows telecom operators to provision very small amounts of bandwidth for tasks like texting and gathering data from IoT devices, and allocate high bandwidth to other data-intensive tasks like mobile gaming. This allocation of bandwidth can be dynamic and uniquely matched to each user’s consumption pattern. It’s a win-win as customers pay only for the bandwidth they use, and operators charge a premium for the higher service levels.

Strategic Planning

New products need to reach new markets and this requires strategic planning. Here too, AI and data science can be of much help.

Data science teams can help executives identify target markets with great accuracy based on real-world data. In the past, this was restricted to hunches; but with the advancement of AI, and the availability of diverse data sets, strategic planning for telcos need not be uninformed.

As 5G expands to new locales across every city and state, AI can help direct this expansion. As new products and services are rolled out, AI can use indicators to predict the credit risk posed by new customers. This will be essential as mobile wallets and digital payment systems based on blockchain become the norm for financial transactions.

Customer Experience

AI is also being embedded into customer support operations. The most prominent example is of customer support bots that speed and automate conversations. These include simple chatbots that can field a customer complaint or sign them up for a new service, without the help of an agent. There are also examples of realistic digital avatars that can emote and bring in the human element to AI. The goal of this is to provide faster resolutions to customer issues, and along the way look for opportunities to upsell and cross-sell services.

AI in Telecom Operations

AI has huge potential to help improve the operational efficiency of telco organizations.

Robotic process automation (RPA) is a widely used and very successful model that is already saving organizations a lot of time and effort. RPA uses rule-based systems to speed up and automate manual backend work. These are often closed-loop processes with a definite end, and they can be as simple or as complex as the process requires.

In sales and marketing, AI can help glean more value from customer data. Telcos have a treasure trove of customer data. The data, however, lies siloed across different systems within the organization. By unifying this data for a 360-degree view of the customer, telcos can offer better and more personalized experiences to their customers.

Conclusion

In conclusion, the telecom sector is currently in a sea of change. Navigating these waters requires new ways of operating that are based on AI. Data science holds value for every function and team within a telco. Identifying the possibilities is the first and necessary step before teams can start making real progress towards implementing AI-based initiatives.


Twain began his career at Google, where, among other things, he was involved in technical support for the AdWords team. Today, as a technology journalist he helps IT magazines, and startups change the way teams build and ship applications. Twain is a regular contributor at Fixate IO.


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