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Embracing the AI-powered telco shift

By Jacqueline Carloni and Erika Nolting Young
woman at computer

An industry at the forefront of technology advancements

Telecommunications (telco) was born from technology, and advancements in technology over time have enabled telco to evolve. From the invention of the telegraph and telephone in the 1800s to the development of cable and satellite TV systems in the mid-1900s, to the first mobile networks in the late 1900s, and the development of 5G in the last decade, telco companies have been at the forefront of some of the largest technological advancements of the last two centuries. So, it should come as no surprise that telco companies are already actively investing in AI technologies, including generative AI (GenAI), in an effort to continually advance their services and operations.

Adoption of AI in telco

As in any industry, one of the strongest use cases for AI in telco is to enhance efficiency and productivity internally. Telcos are known for being capital-intensive businesses, especially in recent years, as the demand for fiber-to-the-home and mobile 5G networks continues to keep capital expenditure high while driving top-line growth. With increasing investments in their networks, telcos are searching for ways to optimize network performance and maximize return on investment (ROI). Investments in GenAI are particularly impactful if they lead to stronger network performance and broader coverage. Other use cases for AI that telcos are testing are ways to tap into the velocity of incoming data to drive product and service innovations and to automate internal processes once considered manual and time-consuming.

Improving network performance

Historically, telcos have relied on customer-initiated feedback to learn when the speed, quality, and reliability of their service are not performing as expected. In 2018, Verizon sought to build an infrastructure of predictive analytics and made investments in AI and machine learning technologies to monitor network performance. By monitoring 3GB of data every second running across its millions of network interfaces, Verizon is able to make sense of data across customers’ routers, sensors that gather weather data, and software that listens to operational data, such as billing records. According to an article published in Forbes, the result of this platform was that the company was able “to predict 200 customer impacting events before they happened and take steps to prevent them from occurring.” Evolving this technology has become increasingly important as Verizon seeks to protect and optimize investment in its 5G network.

Additionally, telcos are investing heavily in 5G network infrastructure and spectrum rollouts in a race to provide faster and more reliable services to their customers. With the help of AI modeling, Verizon is finding the best locations to place thousands of 5G wireless transmitters for optimal performance of midband spectrum. Maximizing coverage with the least number of transmitters helps telcos be conservative in how they invest their capital and leverage midband spectrum, which has efficiencies when it comes to speed and coverage range. AI is modeling data from spatial and geographic elements, scanning bridges, trees, and building heights to find the best spot for its transmitters. It will also analyze the density of cities to ensure they offer quality services to more customers. This will maximize the build’s ROI by minimizing costs and optimizing revenue and subscriber opportunities.

Bringing representatives closer to customers

Telcos are constantly trying to find new and innovative ways to get in front of customer challenges. While customer care is a common use case for AI across many industries, telcos and internet service providers have historically had some of the lowest NPS and customer satisfaction scores despite being able to acquire a significant amount of data related to their customer base. There has been a barrier in harnessing this data to its full potential, but T-Mobile is attempting to utilize AI to bring its representatives a 360-degree view of customers. CIO reports, “When T-Mobile customers contact the company through phone or online chat channels, they trigger AI software that pre-populates information about the customer. This includes what the customer is reaching out for, as well as billing history and other pertinent details.” Instead of investing in AI to power chatbots or other intercepts to handle patient requests, T-Mobile is leveraging AI to help make their customer service representatives more effective and give them the tools to resolve complex customer problems.

Reimagining the telco customer experience

When the iPhone emerged in 2007, it vastly changed consumer expectations about their interactions with organizations. Similar to the emergence of the iPhone, AI technology (and especially GenAI) will have a resounding effect on consumer expectations. As consumers experiment with public GenAI tools and become more familiar with what the technology can enable, they will expect that businesses will intelligently and respectfully use the data and information at their disposal to provide frictionless, delightful experiences. Failure to do so when technology exists to enable a better experience may feel like negligence.

What consumers will expect of telco providers

Let’s use the LBGUPS (learn, buy, get, use, pay, support) customer-experience model to break down potential customer expectations at each stage of the experience in an AI-enabled business world.

  • Understand the customer’s needs and proactively direct their experience to the proper channels and resources.
  • Provide service or sales representatives information about the customer and what products/services they are interested in.
  • Know their purchase history from the company, acknowledge their loyalty, and offer eligible promotions.
  • Gather information about the consumer relevant to the product/service they are considering to suggest other products/services.
  • Use predictive insights to narrow the expected arrival window of installation field techs and shorten the waiting period for the customer.
  • Assess all available insights about the telco infrastructure and service quality to determine if any additional troubleshooting is needed to enable a smooth experience for the new customer.
  • Know when the service is not performing and proactively alert customers.
  • Analyze product/service usage and provide suggestions for cost savings, service optimization, or upgrades needed.
  • Identify payment or nonpayment behavior patterns and offer to set up due-date reminder alerts or suggest payment-assistance options (payment plans, promotions, etc.).
  • Identify when a customer is paying for a service they are not using or are underutilizing, and proactively offer discounts or incentives to retain the customer.
  • Analyze all information available about a consumer to understand what support they are looking for and which channels are best to direct them to for assistance.
  • Identify the customer’s tone and respond accordingly to assuage any frustration or concerns.

Failure to embrace technology creates a gap in the market

What happens when emerging technology enables a better experience for customers, but a company fails to embrace the new technology quickly? This creates an opening in the market for new entrants or intermediaries who can better meet the needs of consumers. In the last decade alone, telecom carriers have met challenges from technology companies that are quick to adopt new innovations. For example, when Google came to town with Google Fiber in 2010 promising gigabit speeds to customers, traditional service providers accelerated their own infrastructure investments by several years to respond to the competitive threat.

More recently, SpaceX’s Starlink is breaking into telecommunications, seeking to use new satellite technology to bring broadband service to all, especially in rural communities currently lacking high-speed internet infrastructure. Starlink is also seeking to provide a more seamless, no-hassle customer experience. They offer four simple service plans, an app-based guided installation experience and service analytics to help customers understand when services are not performing optimally. SpaceX is signaling that it wants to expand into the wireless services market, with a partnership it announced with T-Mobile last year. What will happen when tech companies known for moving fast and being disruptors harness AI? Unlikely partnerships will form, customer expectations will change rapidly, and those that don’t adapt quickly will be left behind.

A day in the life

AI’s ability to continuously improve with more data places telcos in a unique position to both enable and deliver more integrated experiences. Customers will expect greater network coverage and reliability at higher speeds powered by GenAI. To illustrate what this could look like in practice, here are some examples of how GenAI could be applied in telco.

The true-to-life story of Ferris and Sloane: The value and impact of GenAI on network management

Ferris and Sloane are two people who have never met, but they have a significant impact on one another. Ferris, a network services engineer for a major telco provider, makes decisions that affect the quality and reliability of Sloane’s wireless network. Sloane, a regional healthcare manager, spends a lot of time traveling to meet clients and provide demonstrations of her company’s virtual reality products, which aid surgeons in predicting better patient outcomes. Sloane depends on a great 5G network to deliver product demonstrations and keep her connected to customers and her family while she’s on the road.

As a network services engineer, Ferris is responsible for monitoring network performance, identifying bottlenecks or issues, and recommending proactive measures to optimize performance. To accomplish this, Ferris leverages a variety of tools to monitor health, traffic, and performance and gathers data from network devices. From there, he goes through a process of manually comparing historical data analysis to identify trends and seasonal variations in network usage and forecast demand. He’s then responsible for recommending solutions for improving network quality and valuing these solutions with finance, product, local markets, and network infrastructure and build stakeholders. The cycle time from data gathering to solution recommendation is time-consuming, sometimes taking months to resolve. In the meantime, Sloane suffers from poor quality of service, experiencing sporadic connectivity during her demos, dropped or garbled calls, and delayed responses to her customers.

In turn, Sloane calls customer care to understand how she can resolve these issues, but they don’t have access to the data needed to resolve these issues and instead offer discounted service, which only satisfies Sloane temporarily. After months of trying to resolve her problems with numerous calls and transfers to get the right agent to work with her, Sloane faces finding a new provider.

In a world of AI-enabled telco, Ferris could leverage a tool that ingests an unprecedented amount of data across customer care, network, finance, and marketing, trained to look for anomalies both past and present, building forecasting predictions and offering cost-effective solutions in days. In this scenario, AI would save time, make investments in the network more effective and efficient, and improve Sloane’s network reliability faster, removing friction in the customer experience. Additionally, Ferris’s company would receive fewer calls, lower average handle time, and offer discounts to customers less often. Most importantly, Sloane is more productive at work and stays connected to her family while traveling.

The true-to-life story of Ali and Jamie: Advancing customer care and empowering representatives with GenAI

Ali is a new customer who just switched from his previous carrier. He purchased the latest iPhone and got a new customer promotion to boot! However, when Ali sees his first bill, he’s confused about the billing representation; it is higher than expected, the promo charges are confusing, and his new wireless plan doesn’t show the advertised price.

Ali calls into customer care, hoping to resolve this issue quickly. Jamie is a customer service rep who has been on back-to-back calls all day. By the time customers get to her, they’ve usually been through a lot of prompts in the interactive voice response and are starting to get impatient. Jamie answers Ali’s call, asks him to explain his issue, then puts him on hold to investigate the billing problem. While Ali is on hold, Jamie quickly searches across multiple systems to determine why the bill isn’t meeting Ali’s expectations. Jamie attempts to balance the wait time against conducting thorough research to tackle all of Ali’s questions.

Eventually, Jamie determines that the charges are not in error and now must synthesize the information across different systems to formulate an explanation in terms that Ali will understand in a matter of minutes. Despite Jamie’s attempts, Ali still has difficulty understanding his bill and leaves the call feeling confused and frustrated with his carrier. Jamie feels this was just another complicated billing call and tries to shake it off before her next call.

In a world of GenAI, there are multiple innovations that would advance customer care in this scenario. First, with the application of a natural language processing GenAI software in the customer care system, Jamie would have Ali’s voice directly query the application, and the model could analyze data across multiple service systems, determine the issue, and provide a customer-friendly response to Jamie in a matter of seconds. Moreover, a well-trained system with advanced predictive capabilities could even offer responses to common follow-up questions that customers ask in this scenario, which would empower Jamie to give a thorough explanation, alleviating customer concerns and building brand trust and loyalty. This predictive AI would also increase customer care consistency as representatives will answer customer queries the same, regardless of the representative’s tenure.

Embracing AI for success

The telecommunications industry is constantly evolving with new technologies, and in the era of AI, telcos are making significant progress in advancing their services and experiences. AI has several practical use cases for telcos, and companies that are leading the way are leveraging AI to optimize network performance, predict and prevent issues, and strategically build infrastructure. Moreover, AI has the potential to create highly personalized customer experiences that meet and exceed evolving customer expectations.

The future of telco is being defined by these innovation applications of AI. Telco leaders should identify the gaps in their organizations where innovative AI-based solutions will help them stand out in the industry. By embracing this technology, telcos can advance their strategy, stay ahead of the competition, and provide exceptional services to their customers.

Let’s solve together.